[v] PP Handbook Peter Blum November 1997

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1. INTRODUCTION

1.1. Objectives of Physical Properties Measurements

Physical properties of rocks and sediments are indicators of composition,

formation, and environmental conditions of the deposits. Some physical properties

can be measured rapidly and easily at high spatial resolution (core logging) and

serve as proxies for processes such as paleoclimatic changes. Physical properties

data are usually well defined and quantitative, which helps constrain the complex

mineralogical and fluid systems in rocks and sediments. They are used

increasingly by a wide scientific community for various scientific objectives. For

these reasons, physical properties data form the bulk of all core data collected on

board the JOIDES Resolution on each leg.

In soft and semiconsolidated sediment sections, physical properties data serve

mostly as proxies for sediment composition, which is controlled by provenance,

depositional and erosional processes, oceanographic and climatic changes, and

postdepositional processes such as consolidation, and early diagenesis. In

consolidated sediments and igneous rocks, diagenetic processes, including

cementation, major lithological changes, and major faults, tend to dominate many

physical properties. Hydrothermal circulation can be detected in sediment and rock

environments by using physical property measurements.

A major application of data collected at small sampling intervals (a few

centimeters), such as magnetic susceptibility, color reflectance, gamma-ray

density, and natural gamma radiation, is for core-to-core and hole-to-hole

correlation and for correlating core data to wireline log data. These correlation

procedures are essential for stratigraphic studies, and some of the most important

ocean drilling projects are unthinkable without the high-performance acquisition

of physical properties data.

1.2. Shipboard Laboratory Stations and Sampling

OVERVIEW

After cores arrive on deck they are cut into 1.5-m-long sections and stored in racks

for temperature equilibration. The first measurement station is the multisensor

track (MST), where the whole-core sections are loaded on a motorized core

conveyor “boat” for the automatic measurement of gamma-ray density,

compressional (P-)wave velocity, magnetic susceptibility, and natural gamma

radiation. The MST is used most effectively with cores completely filled with s

1—1PP Handbook , Peter Blum , November, 1997

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to semiconsolidated sediments that were retrieved with the advanced hydraulic

piston corer (APC). Intact sedimentary or igneous rock cores cut with the extended

core barrel (XCB) or rotary core barrel (RCB) also give good MST measurements.

Coring disturbance such as severe “biscuiting” (typical for XCB cores) and

fracturing (typical for RCB cores) associated with torquing significantly reduce

the accuracy and usefulness of MST measurements, sometimes to a degree

MST measurements should not be performed.

For soft sediment cores, the second station is the thermal conductivity station

where needle probes are inserted into the whole cores. Next, the cores are sp

either with a wire (soft sediment) or with a saw. The half-cores are designated

archive-half cores and working-half cores. Figure 1—1 shows the relative core

orientation conventions established to place core measurements, particularly

paleomagnetic data, in a geographic reference frame using absolute core

orientation measurements when the core is cut. The same conventions are us

other physical properties measurements that can be performed in multiple

directions and that may reveal anisotropy (e.g., acoustic measurements) or fo

structural measurements. The archive-half cores are preserved in a pristine

condition whereas the working-half cores are available for measurements tha

physically disturb parts of the cores and for theremoval of specimens for shipb

as well as shore-based studies.

The archive-half core is used for the visual core description, paleomagnetic

measurements using the cryogenic magnetometer, noncontact color reflectan

measurements (to be implemented), and photography. A track system is in

development that will measure the two physical properties of magnetic

susceptibility and color reflectance along with the acquisition of color images

the core surface. After core photographs have been taken, the archive-half co

are stored in plastic tubes and refrigerated.

UPWorking half

y(90°)

z

Split-core face

x(0°)

(Double line)

UPArchive half

z

Split-core face

-x(180°)

(Single line)

Figure 1—1 Core orientation conventions.

1—2 PP Handbook , Peter Blum , November, 1997

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The working-half core is used for the measurement of color reflectance (the

present mode of manual operation requires contact with sediment), P-wave

velocity by using probes that are inserted into the soft sediment, vane shear

strength by inserting a miniaturized vane into the sediment, and similar strength

measurements with the hand-held Torvane and penetrometer devices. Half-core

pieces of rocks are used for the measurement of thermal conductivity by using the

“half-space” needle probe. In the future, a gamma-ray densiometer will be ad

to the working-half station. Along with the use of a caliper (associated with theP-

wave system on this track) gamma-ray densities may be more accurate and p

than those obtained currently from the MST.

For the final physical properties measurement, specimens are extracted from

working-half core to determine moisture content and average mineral density

(MAD station). P-wave velocity can also be determined on specimens of

sedimentary or igneous rock extracted using a parallel-blade or cylindrical saw

The working-half core then proceeds to the “sampling table” where one to thre

individuals extract specimens for analysis on shore. The sampling voids are f

with Styrofoam, and the working-half core is stored in plastic tubes and

refrigerated along with the archive-half core.

MULTISENSOR TRACK (WHOLE-CORE MST) STATION

Measurement Systems The MST is an automated core conveying and positioning system for logging

physical properties at small sampling intervals. At present, the MST system

includes the following measurements:

• gamma-ray attenuation densiometry (GRA)

• P-wave velocity logging (PWL)

• magnetic susceptibility logging (MSL)

• natural gamma ray (NGR) measurements

The MST is one of the most routinely used devices onboard the JOIDES

Resolution. No other shipboard instrument produces a comparable amount of

data, and the MST data set is among the most widely used ODP data and

represents a worldwide standard of core analysis. The MST is designed to ha

the sampling of whole cores automatically, and all measurements except the

can also be used on split cores and for measurements on individual core

specimens. A new flexible, intuitive control interface was implemented in 1996

Sampling One of the most useful new features is the improved sampling parameter inter

The user can set sampling intervals and periods for all sensors and the progra

returns the calculated total measuring time for a core section based on an

optimized measuring sequence. A graphical display shows the sampling poin

with depth. Typically, the time permissible for a whole core (typically seven co

sections) is about 1 hr on legs with high core recovery (about 4 km of core or

more). Therefore, if full-time attention is given to the MST, about 10 min can b

allowed for measuring one core section. An overview of useful sampling

parameter settings is given in this section. More data and information are pres

in the individual sensor sections as appropriate.


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When selecting sampling intervals, consideration should be given to the depth

interval each sensor can resolve (see Table 1—1). For the GRA and PWL sen

the depth intervals are less than 1 cm, for the MSL loop it is about 4 cm, and

the NGR it is about 15 cm. Because the sensitivity of the MSL and NGR sens

decreases away from the center of the sensor, better resolution can still be ach

by taking measurements at intervals smaller than the intrinsic interval of influe

Generally, ideal sampling intervals for the GRA, MSL, and PWL are 1 cm and

should not exceed 5 cm. For the NGR, the best depth resolution possible is at

5 cm. Intervals should not exceed 30 cm, which is about the depth resolution

downhole logging tools.

Sampling periods are directly related to the data quality (precision) particularly

the nuclear sensors. Because of the high flux provided by the 137Ce gamma-ray

source, 2-s sampling with the GRA is sufficient. The MSL has an internal

integration time of 0.9 s (1.0 range) or 9 s (0.1 range); it should be set at 1 s.

MST program is best set to 2-s sampling time to allow for minor electronic an

communications delay. The NGR is most sensitive to the sampling period bec

of the low intensity and random nature of natural gamma ray emissions. The m

counts are accumulated, the more reliable the signal (the error is is proportion

N-0.5, where N is the number of counts; see “Natural Gamma Radiation” chap

for more discussion). If spectral analysis is attempted to estimate abundance

U, and Th (which is not implemented for routine application yet), at least 1 mi

should be counted. (One hour would probably be more appropriate to reduce

statistical error to a level that would yield a good estimate of K, U, and Th). If o

a total counts signal is desired, as little as 15 s is sufficient in terrigenous

sediments, whereas 30 s should be measured in carbonates. The PWL system

five measurements (data acquisitions or DAQs) at each point that are average

the sample and provide a sufficiently precise value.

For optimized sampling parameter settings it is important that intervals and

periods are multiples of each other. This ensures that the idle time of sensors

minimized and data quantity and quality are maximized for a given total core

section scan period. For example, if GRA is set to 2 cm and MSL to 3 cm, on

the two sensors is partly idle while the other is taking a measurement. It is mo

efficient to set both at 2 cm so they measure simultaneously. Similarly, if the c

stops every 1 cm for GRA and MSL measurements and 4 s are required for th

MSL, the GRA sampling period should also be 4 s rather that 2 s because the

additional time improves data quality but it does not require any additional tim

Table 1—1 MST sampling parameters.

Sensor Sensitivity Interval (cm) Period (s)

interval (cm) Best Typical Maximum Best Typical Minimum

GRA <1 0.5 1 5 4 2 1

MSL 4 1 1 5 10 4 1

PWL <1 0.5 1 5 10a 1a 1a

NGR 15 1 10 30 >100 20 5b

Notes: aFive DAQs are averaged per second. bFor amoving average applied to data taken at close spacing.


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A further optimization can be considered for NGR measurements. Rather than

taking a 20-s reading every 20 cm and leaving the other sensors mostly idle during

that time, a 5-s reading can be taken every 5 cm, simultaneously with the other

readings. This shortens total scanning time considerably. To get data quality

(statistical error range) equivalent to a 20-s counting time, the user simply runs a

moving average with a four-point window on the data.

THERMAL CONDUCTIVITY (TC) STATION

Measurement Systems Thermal conductivity is the only property measured at this station. Two systems

are available currently:

• Thermcon-85 system customized for ODP use and

• new TK04 system not customized for ODP.

A project plan exists to replace these with a fully integrated system that would

incorporate the best features of both existing systems. However, no resources

been allocated yet.

Soft-sediment cores are measured before they are split because the larger vo

of material surrounding the needle probe reduces geometrical problems (edg

effects). If the core material is too hard to be penetrated by the needles witho

excessive force, thermal conductivity is measured on working-half core piece

using the half-space needle probes.

Sampling Given the minimum time available until a soft sediment core must be split (abo

hr), at least 5-10 measurements can be performed (1- to 2-m sampling interva

This is usually sufficient because thermal conductivity variations are strongly

proportional to, but less sensitive and less precise than, bulk density

measurements. Density can be used as a proxy and calibrated against a limit

number of thermal conductivity measurements if higher spatial resolution is

required.

ARCHIVE-HALF CORE LOGGER (A-LOGGER, TO BE IMPLEMENTED)

Measurement Systems (to be implemented)

The archive-half core logger is under development and scheduled for deploym

later this year (1997). It will include the following measurement systems:

• color line-scan images,

• color reflectance spectrophotometry and colorimetry, and

• magnetic susceptibility.

The main goal for this development is to acquire color images of the cores (no

discussed in this note) and to automate the routine acquisition of visible light c

reflectance measurements. In addition, the spacial resolution and sensitivity o

magnetic susceptibility logging will be improved with a “point-sensor” that

requires contact with the core surface. Although line scans are truly nonconta

and nondestructive (i.e., ideally suited for archive-half logging),

photospectrometry and magnetic susceptibility require contact with the core

surface and these implications still must be evaluated. These measurements

haveto be obtained from working-half cores.


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Present Measurement System

The present “proto-A-logger” consists of a manually operated track for color

reflectance measurements. Measurements are usually performed on working

cores because imprints are left on the core surface from the manual operation

simple computer program writes the data directly to disk and assists the oper

further by incrementing sampling intervals automatically.

Sampling Color reflectance should be measured at the smallest intervals possible beca

is very sensitive to compositional changes. Variations in color reflectance serv

an excellent proxy for detailed correlation and compositional interpretation. A

measurement with the Minolta spectrophotometer covers an 0.8-cm-diameter

The manual mode sampling intervals used by shipboard scientific parties are

20 cm. With the future automated system, intervals should be set at 1 cm or le

WORKING-HALF CORE STATION (W-LOGGER)

Measurement Systems The working-half core station is semiautomated currently. It includes the follow

measurements (Figure 1—2):

• P-wave velocity with the PWS1, PWS2, and PWS3 systems,

• Shear strength using the automated vane shear (AVS),

• Shear strength using the manual Torvane (TOR), and

• Compressional strength using a pen-size penetrometer (PEN)

A component analyzer is available for resistivity measurements, but these

measurements are not supported by ODP at present. Users are required to p

their own probes, perform their own calibrations, and develop their own

procedures.

P-wave velocity Shear strength

AVSPWS1 PWS2 PWS3

y xzn/a any directionn/a

Split-core:Measurement direction

Specimens:z-y plane

n/a

Figure 1—2 Schematic view of the semiautomated instrumentation on thworking-half core track. n/a = not applicable.


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Sampling Sampling intervals for these measurements are mainly a function of available time

at a given core recovery rate and how much core destruction (particularly using the

AVS system) is permissible. The minimum sampling frequency on soft sediment

cores is one per core section; a more typical sampling rate is two per section (75-

cm sampling interval). If numerous measurements are desired on specimens that

must be extracted from the working-half core or that disturb the core, the ODP

staff representative must be consulted.

Whenever possible, the same sampling location should be coordinated for P-wave

velocity and strength measurements, as well as for subsequent extraction of

specimens for moisture and density measurements, carbonate, X-ray diffraction

(XRD), and/or magnetic rock properties measurements.

For velocity measurements on split cores in liners, no sample preparation is

necessary. An undisturbed interval is chosen for the measurement. For

measurements on specimens that require two parallel faces to obtain optimum

values, there are several ways to obtain such samples. In semiconsolidated

sediment, use a spatula or knife to cut a cube of approximately 20 cm3. For

indurated sediment, use a hammer and chisel or the Felker saw. The Torrance

double-bladed saw cuts good parallel faces. The easiest way to obtain a velocity

sample in hard rock is to “drill” cylindrical minicores. These samples are

particularly useful for sharing with the paleomagnetics laboratory (note the

orientation when taking the sample).

MOISTURE AND DENSITY (MAD) STATION

Measurement Systems At the MAD station, the following are measured:

• wet-bulk mass and dry mass of the same specimen (for moisture conand density) and

• volume of dry (and optionally wet-bulk) specimen using gas pycnome

From these measurements, basic phase relationships such as porosity, bulk d

grain density, dry density, and void ratio can be calculated. At present, a

convection oven is used to dry the specimens. Ideally, a freeze-dryer should b

used to avoid excessive extraction of interlayer water from clay minerals,

particularly smectite.

Sampling Sampling is typically 1-2 specimens per section, 10-mL volume per specimen

possible, the same sample interval should be used as for strength and/or P-wave

velocity measurements. Where numerous lithologic changes occur, denser

sampling may ensure measurements from all significant lithologies throughou

core. Where cyclic changes in gamma-ray density are observed, a denser sam

program over a characteristic interval may be desirable. In XCB and RCB cor

which commonly show the biscuiting type of disturbance, particular care shoul

taken to sample undisturbed parts of the core sections and to avoid the drillin

slurry.


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1.3. New Shipboard Data Management Environment

BACKGROUND

In the early 1990s, the JOIDES advisory structure, through input from shipboard

participants identified the need to design and implement a new database system on

the ship as well as on shore. The complexity and level of productivity of the

shipboard data acquisition environment made this a multiyear, multimillion dollar

project. The physical properties laboratory was the first shipboard laboratory to be

integrated into the new data management environment once the basic operational,

curatorial, and depth calculation functions were redefined and implemented.

The process of redefining the entire ODP data structure offered the opportunity to

implement more rigorous data acquisition, calibration, and control measurement

protocols for physical properties measurements and to give the user access to these

quality control data. A uniform data structure, compatible with the rules of

relational data management, was created wherever possible. Leg 173 (April to

June 1997) was the official “acceptance leg” for the new data management sy

as described in this first edition of the note.

From the user’s perspective, the data management system includes the follow

components:

• data acquisition interfaces and controls,

• data upload utilities,

• database and data models, and

• data access and standard queries.

The following section briefly introduces these components.

COMPONENTS OF SHIPBOARD DATA MANAGEMENT

Data Acquisition Interfaces and Controls

DAQ programs are written in various programs depending on the most suitab

software tools and available expertise and hardware at the time and place the

were written. During the past two years, two dominating standards have evolv

Neuron Data for operational and curatorial functions and descriptive data type

(excellent for PCs, but performs poorly on Macintosh computers); and Labvie

for instrumental data (Macintosh or PC). The Neuron Data applications are

integrated into a common user interface, called the Janus Application. Most

physical properties DAQ programs are written in Labview now, including the

MST control, MAD program, P-wave velocity and vane shear strength on half

cores (PWS, AVS), and control of the Minolta photospectrometer (COL). Ther

conductivity remains in a state of development, and both available systems

controls are written in QuickBasic.

Data Upload Utilities Once data are acquired and located on a local drive, they must be uploaded t

Oracle database. Although procedure this could be fully automated and beco

part of the DAQ program, it was decided that an interactive user quality contro

should separate the two functions. Invalid or erroneous data are frequently


data

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lp the

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using

age

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acquired, particularly on highly automated systems that are operated in a

conveyer-belt mode. The user has the option to delete such data from the local

directory before triggering upload to the database, which avoids excessive editing

within the database, a process that involves significantly more risk and effort.

Data upload utility programs are written in Neuron Data and are closely integrated

with DAQ programs written in Neuron Data. For DAQ programs written in

Labview or another language, a separate data upload utility must be operated. This

is the responsibility of the ODP technical support representative, but scientists may

learn the procedure and operate it themselves.

Database and Data Models

The new ODP Oracle database is designed specifically for ODP’s unique

shipboard environment and user needs. The system includes more than 250

tables in a complex relational scheme, capturing data from the initiation of a le

through core recovery and curation, physical and chemical analyses, core

description, and sampling. Physical properties alone use 65 tables at present

counting related tables for sample identification and depth data shared with o

laboratories, and will involve more than 100 tables once the remaining

measurement systems are integrated. The tables pertaining to a particular

measurement system are presented in the “Data Specification” sections to he

user understand how the data are structured and how they can be accessed.

Data Access and Standard Queries

At this early stage of using the new database, there are three different technic

approaches to data access, and the next few legs will show which is the most

efficient and user-friendly one. The three approaches are referred to as

• Janus Application,

• Report Access Program, and

• World Wide Web Data Access.

The first solution integrates an off-the-shelf reporting utility, Business Objects

into the Janus Application. Many reports are available through this main

interfacefrom which the user selects a particular report from a submenu.

The Report Access Program (RAP) was written as an alternative manager fo

Business Objects reports. The advantage is that the user does not have to log

the Janus Application, which may be somewhat time-consuming, and that acc

to and expansion of Business Objects reports and queries could be more effic

managed by ODP. This environment allows the user to create special reports

existing Business Object macros relatively easily.

The third approach is for ODP personnel to write standard queries in C-langu

and make them available through a World Wide Web (WWW) browser. This

approach has the advantages that routines are directly suitable for global data

access and that accessing data on the ship on the local web may be the faste

method. It will not provide the freestyle access to the database that Business

Objects in the RAP environment offers to the user. However, recent informatio

indicates that Business Objects will not continue to be supported on the Macin

which rules out its future use. The third approach will therefore most likely be

fully implemented.


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SAMPLE IDENTIFIERS AND DEPTH CALCULATION

Links to Curatorial Identifiers

In the relational ODP database, redundancy of information is minimized for

efficient data management. For example, site, hole, core, and section information

is entered in specific tables linked in a logical way, and all measurement locations

in a particular section are linked to the <Section> table. Similarly, if a core

specimen is extracted for shipboard or shore-based analysis, the basic curatorial

information is accessed through the <Sample> table, which is linked to the

<Section> table, etc. In the physical properties database models presented in the

following chapters, the field <section_id> alone or with the fields <interval_top>

and <interval_bottom> are the links to the more specific information in the

appropriate tables. The <Sample> and <Section> tables are listed in Table 1—

Depth Types Depth below seafloor of a core specimen or measurement location can be

calculated in different ways. The standard way is to measure the distance in t

recovered and physically expanded core and add it to the measured drill strin

depth datum for the top of the core. This depth scale is known as “meters bel

seafloor” (mbsf). Of course, this is only an approximation to the true depth be

seafloor. Problems inherent in this scale are that the recovered core length m

greater than the interval advanced by the drill string, and some of the material

this interval was lost between successive cores. With APC material, this resul

apparently overlapping sections between successive cores when in fact there

coring gap.

If a complete stratigraphic section is to be constructed, multiple holes are drille

the same site and a composite section is developed at the “meters composite

depth” (mcd) scale. This scale is at the physically expanded state of the recov

cores and does not match the drilled interval. However, it is a much more

continuous scale that can be fit approximately to the drilled interval using the c

top data (mbsf) or fit more precisely if good-quality downhole logging data are

available.

There are additional corrections that can be applied to derive a more accurate

approximation to depth below seafloor. These and other depth issues are expl

in detail in a workshop report (Blum et al., 1995), and a technical note dedicate

these issues will be produced. The redefined concepts are integrated in the n

database, which features a depth map that allows the rapid calculation of any

type provided that pertinent data have been acquired and entered (see Table 1


y. The

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Standard data queries prompt the user to specify the desired depth type. The

default map type (mbsf) is referred to (map_type_name) as “standard.”

1.4. Physical Properties Standards

Standard materials used to calibrate instruments are an essential part of the

analyses and should be integrated into the measurement systems accordingl

goal is to enter all standards used into database tables so that calibration dat

results can be tracked to the particular standard used at a given time. Our pla

populate a <Physical Properties Standard> table shown in Table 1—3. The ta

generic enough to accommodate any type of standard, and the value of any

property can be linked to any calibration utility and file in the physical properti

environment. This table may principally include standards from other laborato

as well.

A table of existing standards is in preparation.

Unfortunately, ODP has not made significant efforts to share standards and

calibration procedures with other core laboratories (with rare exceptions). Suc

efforts would benefit ODP as well as other laboratories, and therefore the scien

drilling community, because reliable and widely endorsed calibration standard

systems that measure complex natural systems are difficult to find.

Table 1—2 Database model for some essential s.

Map Type Depth Map Section Samplemap_type [PK1] section_id [PK1] [FK] section_id [PK1] sample_id [PK1]

description map_type [PK2] [FK] section_number location [PK2]map_type_name sect_interval_top [PK3] section_type sam_section_id . section_idmap_type_date sect_interval_bottom [PK4} curated_length sam_archive_working

map_interval_top liner_length top_intervalmap_interval_bottom core_catcher_stored_in bottom_interval

section_comments pieceleg sub_piecesite beaker_id . mad_beaker_id

hole volumecore entered_by

core_type sample_depthsample_commentsam_repository . repository

s_c_leg . legs_c_sam_code . sam_code

sam_sample_code_lab . s_c_l

Table 1—3 Physical properties standards database model.

Physical Properties Standard Physical Properties Std Datastandard_id [PK1] standard_id

standard_name property_namestandard_set_name property_descriptiondate_time_commissioned property_value

date_time_decommissioned property_unitslot_serial_numbercomments


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2. MOISTURE AND DENSITY (BY MASS AND VOLUME)

2.1. Principles

PHYSICAL BACKGROUND

Moisture content and mineral density are basic sediment and rock properties that

are determined most accurately through mass and volume determinations. Core

specimens of approximately 8 cm3 are extruded from the working-half core for

this purpose. Moisture content is determined by measuring the specimen’s m

before and after removal of interstitial pore fluid through drying. The drying

method is the most critical part of the entire procedure. At present, a convecti

oven is used for this purpose for 24 hours at temperatures varying from 100°

110°C. This method is suspected to remove a substantial portion of the interla

(hydrated) water from clays such as smectite in addition to interstitial water, wh

may result in porosity errors of up to 20%. Alternative methods such as microw

or freeze-drying have other potential problems and have not replaced the

convection oven.

Moisture content, porosity, and void ratio are defined by the mass or volume o

extracted water (assumed to be interstitial pore fluid), corrected for the mass

volume of salt evaporated during the drying process (see also ASTM, 1990).

mass and volume of the evaporated pore-water salts are calculated for a stan

seawater salinity (35), seawater density at laboratory conditions (1.024 g/cm3),

and an average seawater salt density (2.20 g/cm3). Any gases that may be presen

are allowed to escape during core retrieval, core splitting, and specimen extru

The volume of a specimen can be measured in three ways:

• method A: wet-bulk volume measured with special volume sampler,

• method B: wet-bulk volume measured by gas pycnometry, and

• method C: dry volume measured by gas pycnometry

Method A is the least standardized method. The device to be used depends o

preference and can be a simple steel ring (“fixed volume,” available on the shi

some sort of syringe (volume is measured after the sample has been taken).

advantage of method A, according to some users, is that a larger number of

specimens can be measured than with gas pycnometry in a given time. The

disadvantages are (1) the method works only in soft, non-sticky sediment (ma

homogenous carbonate oozes to a depth of about 200 mbsf), (2) the volume

measured includes potential cracks or other spaces filled with air, (3) there is

precision estimate for this method, and (4) there is no standard for this metho


ry

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ble.

This method should therefore be used only if there is ample justification, and

measurements must be “calibrated” with an appropriate number of pycnomet

results.

Methods B and C use the same gas pycnometer. The measurement principle

device is briefly described in the following. Gas pycnometry works with pressu

ratios of an ideal gas (helium), which are sensitive to contamination with parti

pressures of other fluids. The material to be measured should therefore be dry

ODP database contains thousands of examples from specimens measured w

both method B and method C. A systematic error is clearly discernible in

comparing calculated results, with bulk densities 1%–5% too high and grain

densities about 5%–10% too high for method B. It is therefore recommended

only method C be used.

The following relationships can be computed from two mass measurements a

one or two volume measurements. First, if methods B or C are used, the bea

mass and volume, which are determined periodically and stored in the progra

lookup table, are subtracted from the measured total mass and volume

measurements. If method A is used, only the beaker mass is subtracted (the

must specify the use of method A in the program). This results in the following

directly measured values:

• Mb: bulk mass,

• Md: dry mass (mass of solids, Ms, plus mass of evaporated salt),

• Vb(A or B): bulk volume, method A or method B, and

• Vd(C): dry volume = volume of solids, Vs(C), plus volume of evaporated salt, Vsalt.

Variations in pore-water salinity, s (s = S/1000), and density, ρpw, that typically

occur in marine sediments do not affect the calculations significantly, and stan

seawater values at laboratory conditions are used:

s = 0.035 (1)

ρpw = 1.024. (2)

Pore-water mass, Mpw, mass of solids, Ms, and pore-water volume, Vpw, can then

be calculated:

Mpw = (Mb – Md) / (1 – s) (3)

Ms = Mb – Mpw = (Md – s Mb) / (1 – s) (4)

Vpw = Mpw/ρpw = (Mb – Md) / [(1 – s) ρpw]. (5)

Additional parameters required are the mass and volume of salt (Msalt and Vsalt,

respectively) to account for the phase change of pore-water salt during drying

should be kept in mind that for practical purposes the mass of salt is the same

solution or as precipitate, whereas the volume of the salt in solution is negligi

Msalt = Mpw – (Mb – Md) = (Mb – Md) s / (1 – s) (6)

Vsalt = Msalt / ρsalt = [(Mb – Md) s / (1 – s)] / ρsalt, (7)

where the salt density value ρsalt = 2.20 g/cm3 is a value calculated for an average

composition of seawater salt (Lyman and Fleming, 1940; Weast et al., 1985).


the

loor,

every

or

g

is in

ip

ive

veral

lume

refore

s

.

ions

itions

Moisture content is the pore water mass expressed either as percentage of wet bulk

mass or as percentage of the mass of salt-corrected solids:

Wb = Mpw / Mb = (Mb – Md) / Mb (1 – s) (8)

Ws = Mpw / Ms = (Mb – Md) / (Md – s Mb). (9)

Calculation of the bulk volume for method C and volume of solids depend on

volume measurement method used:

Vs(A or B) = Vb(A or B) – Vpw (10)

Vs(C) = Vd(C) – Vsalt (11)

Vb(C) = Vs(C) + Vpw. (12)

Bulk density, ρb, density of solids or grain density, ρs, dry density, ρd, porosity, P,

and void ratio, e, are then calculated accordingly for each method:

ρb(A,B,C) = Mb / Vb(A,B,C) (13)

ρs(A,B,C) = Ms / Vs(A,B,C) (14)

ρd(A,B,C) = Ms / Vb(A,B,C) (15)

P(A,B,C) = Vpw / Vb(A,B,C) (16)

e(A,B,C) = Vpw / Vs(A,B,C). (17)

ENVIRONMENTAL EFFECTS

Core Expansion Cores, particularly sediment cores from a few hundred meters below the seaf

expand upon recovery for a number of reasons, which include

• elastic recovery,

• gas expansion, and

• mechanical stretching.

Expansions of solids can be neglected. Pore water expands by about 4% for

1000 bar (100 MPa) pressure release. This is what the pore water of a seaflo

sample from about 10,000-m water depth would experience, or in ocean drillin

terms, what a sample buried by about 2000 m of water and about 3000 m of

sediment would experience. For the bulk of ODP cores, pore-water expansion

the order of 1% and therefore negligible compared with the analytical error.

Free gas expands by orders of magnitude, according to the simple relationsh

P1V1 = P2V2. A few percent of free gas in the sediment can produce an explos

sediment-gas mixture that has torn apart plastic core liners on the ship on se

occasions. Most gas escapes before the cores are analyzed and can produce

microfractures, which appear as porosity with methods based on core unit-vo

measurements, such as the gamma-ray attenuation bulk density method.

Mechanical stretching may also cause microfracturing. The MAD method

measures the mass and volume of the solid and liquid phases only and is the

not affected by this type of artificial porosity. The original contribution of the ga

to in situ porosity cannot be measured with our routine core analysis program

Composition of Seawater

Different water masses of the world oceans have different chemical composit

and physical properties. For the purpose of correcting oven-dried sediment

specimens for the evaporated salt from the pore water, the standard compos


10 and

n of

lues

ffects

the

22°C

e

n our

-

, the

situ

after Lyman and Fleming (1940) and salt densities after Weast et al. (1985) are

used (Table 2—1).

aLyman and Fleming (1940).

bWeast et al. (1985).

Given the uncertainty in regard to the crystalline structure of some evaporated

components, the average density of the standard seawater salt is between 2.

2.24 g/cm3. A value of 2.20 g/cm3 is used routinely for the MAD calculations.

Density of Pore water Density of pore water is a function of temperature (T), salinity (S), and pressure

(P). Equations of state for seawater (Millero et al., 1980; Millero and Poisson,

1981) can be used to illustrate the variability of pore-water density as a functio

these three parameters (Figure on page 5).

Typical salinity values for pore waters are 30 to 40, although more extreme va

exist. At laboratory pressure and temperature, this range of salinity change a

pore-water density change of less than 1%, which is negligible compared with

analytical uncertainty. We therefore use a standard value of 35 for all MAD

calculations and leave it up to the user to apply corrections if warranted.

The typical temperature change experienced by nonlithified sediment upon

recovery is from about 100°C at depth to few degrees at the seafloor to about

in the laboratory. At standard salinity and laboratory pressure, a 100°C chang

results in about a 2% change in seawater density. These effect is not figured i

MAD calcualations because it is close to the uncertainty.

The effect of pressure change on density is of a similar magnitude. For a high

porosity mud sample (for example, from 100 mbsf) at a water depth of 3000 m

pressure release is about 320 bar (32 MPa). According to Figure 2—1, if the in

Table 2—1 Composition of sea water.

Salt Mass fractiona

(x 103)

Densityb

(g/cm-3)

NaCl 23.476 2.165

MgCl2 4.981 2.316-2.33

Na2SO4 3.917 1.46 (monocl.)2.68 (orthorh.)

CaCl2 1.102 2.15

KCl 0.664 1.984

NaHCO3 0.192 2.159

KBr 0.096 2.75

H3BO3 0.026

SrCl2SrCl2.2H2O

0.024 2.671 (leaf)3.052 (cub.)

NaF 0.003 2.08

Total 34.481

Weighted average 2.10-2.24


era-m 0

reme40°Cndi-

temperature is about the same as laboratory temperature and salinity is 35, pore-

water density decreases by about 1% upon recovery.

Figure 2—1 Density of seawater as a function of pressure, salinity, and tempture, using equations from Millero and Poisson (1981). The pressure range froto 1000 bar covers most ODP situations. Standard salinity of 35 and two extsalinities (0 and 70) are plotted as a function of a temperature between 0º to (experimental temperature range of Poisson and Millero, 1981). The arrow icates standard laboratory conditions.

USE OF MAD DATA

MAD data are the only data that provide a direct estimate of porosity and void

ratio and the average density of the minerals. Porosity variations are controlled by

consolidation and lithification, composition, alteration, and deformation of the

sediments or rocks.

MAD data can be used to calibrate the high-resolution gamma-ray attenuation bulk

density data sampled automatically at much smaller intervals than would be

possible for MAD data. If mineral density can be defined with sufficient precision,

GRA bulk density can be expressed as porosity.

990

1010

1030

1050

1070

1090

0 200 400 600 800 1000

Density (kg/m

3 )

Pressure (bar)

0°C

S = 7

S = 3

S = 0

20°C

40°C

0°C

20°C

40°C

0°C

20°C

40°C


cells

ce

he

ssible

e

tes

g is

imen.

120

n

the

e ideal

ring

l (for

sed. hich ent res a

he

ses.

2.2. Moisture and Density System

EQUIPMENT

Balance Mass is determined with two Scientech 202 electronic balances to compensate for

the ship’s motion. A set of mass standards ranging from 1 to 20 g is used for

calibration and on the reference balance during measurements.

Gas Pycnometer The helium displacement pycnometer with five cells (penta-pycnometer),

manufactured by Quantachrome Corp., employs Archimedes’ principle of fluid

displacement to determine the volume of solid objects. The five measurement

contain custom-fabricated inserts that reduce the chamber to a cylindrical spa

that holds exactly one 10-mL Pyrex beaker. The measurement chamber must

contain as little air space as possible to maximize measurement precision. (T

user should also ensure that the Pyrex beakers are filled as completely as po

with core material.)

Each sample cell of volume VC has an input valve (from the gas tank) and an

output valve (to the pressure transducer). An additional reference cell of volum

VA is located downvent of the sample cells, with an input valve (which separa

VA from the pressure transducer) and a vent valve (Figure on page 7). All cell

volumes must be calibrated periodically (see calibration section). The followin

the operation sequence of the pycnometer during the measurement of a spec

The specimen to be measured is placed in a cell of known volume, VC. It is

pressurized, using helium, to an exactly measured pressure of about 18 psi (~

kPa). The solenoid valve between sample cell and the reference cell of know

volume VA is opened and the helium from the pressurized chamber is ported to

reference cell. The subsequent pressure in the system is measured. Using th

gas law, the sample volume can be calculated from the pressure ratio. The

following is the sequence of operation (Figure 2—2).

1. Gas input valves to all five cells are closed (corresponding light-emittingdisplays [LEDs] on pycnometer are unlit). The five sample cell output valves, the reference cell input valve, and the vent valve are open, ensuthat all cells are at the ambient pressure, Pa.

2. For all cells in use all valves are opened and cells are purged in parallea 1-min minimum). Cells not being used (not identified by the user) are isolated by closing the input and output valves.

3. At the end of the purge period, processing begins on the first cell to be u(Cells are run in ascending numerical order regardless of the order in wthey were specified). When a stable ambient pressure is reached, the vvalve of the reference cell closes and the pycnometer acquires and stozero pressure value.

4. The reference cell input valve is closed to isolate VA from the cell. Approximately 6 s later, the current sample cell input valve opens and tcell is pressurized to approximately 17 psi or until 3 min elapse.

5. When the cell pressurization is complete, the current cell input valve clo


Blue

The pycnometer waits until a stable pressure is detected and then acquires and stores the pressure P1 (LED display: pressure A).

6. The VA input valve opens. This will cause a pressure drop in the sample cell that is proportional to the change in volume because of the introduction of VA. When the pressure stabilizes, the system acquires and stores the cell pressure P2 (LED display: pressure B).

7. The vent valve is opened to return the cell to ambient pressure. After a short vent period, the instrument begins processing the next specified cell (if any) by venting the cell to ambient pressure.

8. After all cells defined for use have been processed, samples may be removed. The pycnometer indicates this by displaying “<RUN COMPLETED>”.

Figure 2—2 Operating sequence of the Quantachrome penta-pycnometer. lines and cells are under ambient pressure Pa (Pa = P0). Red lines and cells are

1

2

3

4

5

VA

1

2

3

4

5

VA

P0

1

2

3

4

5

VA

PpurgeP0

1

2

3

4

5

VA

P0

1

2

3

4

5

VA

P0 1

2

3

4

5

VA

P1

1

2

3

4

5

VA

1

2

3

4

5

VA

P2

1

2

3

4

5

VA

P0 1

2

3

4

5

VA

P0

A

C

E

G

I J

H

F

D

B

P1

Vent valve closed

System idle Purging cells to be used

Equilibrate to ambient

Reference volume isolated Sample cell pressurized

Sample pressure isolated Reference volume added

V l d E ilib i ll


stem

ct the

under system pressure P1 (about 17 psi). Green lines and cells are under areduced system pressure P2.

The sample volume can be calculated using the ideal gas law. By opening the

solenoid valves on one sample cell with volume VC, the system is brought to

ambient pressure Pa after being purged with helium. The state of the system is then

defined as

Pa VC = n R Ta , (18)

where n is the moles of gas occupying volume VC at pressure Pa, R is the gas

constant, and Ta is the ambient temperature in degrees Kelvin.

When the solid sample of volume VS is placed in the sample cell, the equation can

be written as

Pa (VC – VS) = n0 R Ta . (19)

After pressurizing to about 17 psi above ambient pressure, the state of the sy

is given by

P1 (VC – VS) = n1 R Ta . (20)

Here, P1 indicates a pressure above ambient and n1 represents the total moles of

gas contained in the sample cell. When the solenoid valve is opened to conne

added volume VA to that of the cell VC, the pressure falls to the lower value P2

given by

P2 (VC – VS + VA) = n1 R Ta + nA R Ta , (21)

where nA is the moles of gas contained in the added volume when at ambient

pressure.

The term Pa VA can be used in place of nA R Ta in Equation on page 8yielding

P2 (VC – VS + VA) = n1 R Ta + Pa VA . (22)

Substituting P1 (VC – VS) from Equation on page 8 for n1 R Ta:

P2 (VC – VS + VA) = P1 (VC – VS) + Pa VA (23)

(P2 – P1) (VC – VS) = (Pa – P2) VA (24)

VC – VS = (Pa – P2) / (P2 – P1) VA . (25)

Equation on page 8 is further reduced by adding and subtracting Pa from P2 and P1

in the denominator, giving

VS = VC – {[(Pa – P2) VA / [(P2 – Pa) – (P1 – Pa)]} (26)

= VC + VA / {1 – [(P1 – Pa) / (P2 – Pa)]}. (27)

Because Pa is zeroed prior to pressurizing:

VS = VC + VA / [1 – (P1/ P2)]. (28)

This is the working equation employed by the penta-pycnometer.

Convection Oven The convection oven can maintain 105° ± 5°C.


akers

e low

e

y by

nd

re

78 g/

n be

the

This drying process has two main problems: (1) clay mineral interlayer water is

largely lost in addition to interstitial water and (2) specimens dried in a convection

oven become brick hard and are rarely useful for any other analyses that require

substantial sample volumes. Use of freeze-drying would partly eliminate these

problems. In particular, stable isotope analyses on foraminifers would be possible

from freeze-dried samples, but not from oven-dried samples. The convection oven

is used based on advice from the relevant JOIDES advisory panel, because drying

at 105° ± 5°C for 24 hr is a well-established soil science standard.

CALIBRATION

Beaker Mass and Volume

Beaker mass must be measured and entered into the MAD program for all be

to be used on a leg. Beaker volume is not convenient to measure because th

material volume to void ratio in the pycnometer cell gives inaccurate values. W

have therefore determined the density of the Pyrex beaker material accuratel

filling a beaker with chips of other beakers, measuring its mass and volume, a

calculating its density. The density of 2.2 g/cm3 is stored in the MAD program,

which returns the volume corresponding to each beaker mass.

Custom-made aluminum beakers were used until Leg 168. These beakers we

difficult to clean, corroded with time, and were expensive to manufacture. For

historical data migration purposes, those beaker materials had a density of 2.

cm3 (determined by P. Blum, 1996).

Balance Calibration The ship is an environment of cyclically changing gravity, and the measured

weight W of a mass M is significantly affected by the ship's motion. If W is

measured over a period of time several times the periodicity of the ship’s

acceleration a, the average can be related to M. By using two balances, mass

determination can be significantly accelerated. The following two equations ca

written for two balances:

Fs = Ms × a(t) = As + Bs × Vs(t) (29)

Fr = Mr × a(t) = Ar + Br × Vr(t), (30)

where Fs and Fr are average measured weights and Ms and Mr are known mass

standards on the sample and the reference balance, respectively, a is the ship's

average acceleration, V is the average voltage measured, and A and B are constants

characteristic for the balances. The calibration principle is to measure multiple

standards (typically 1, 5, 10, 20, and 30 g) to determine A and B for each balance.

For the calibration, measuring time should be at least 30 s to cover several of

7–8 heave cycles of JOIDES Resolution.

Equations on page 9 and on page 9 can be solved for a(t), which is assumed to be

equal for both balances:

Ms = [As + Bs × Vs(t)] × Mr / [Ar + Br × Vr(t)], (31)

which is identical to

Ms (unknown) / Ms (calculated) = Mr (known) / Mr (calculated). (32)


ion

ken

ation

.

ugh

rate

h

lues

o

n

The first right-hand term in Equation on page 9 is the first approximation to the

calculated sample mass. This value is uncorrected for motion and is returned

instead of 0 if the user sets Mr (known) = 0. The second right-hand term in

Equation on page 9 uses the ratio between a known mass Mr on the reference

balance and its corresponding calculated value to correct the first term for the

ship’s motion.

The MAD program performs the linear regression for multiple standards and

stores the coefficients until a new calibration is performed. A balance calibrat

takes up to 15 min. It is recommended that a few control measurements be ta

after a calibration to verify the correct mean value and a percent standard devi

of less that 1% for 100 or more measurements taken over approximately 30 s

Pycnometer Calibration

The pycnometer has an internal calibration procedure. The user is guided thro

the procedure step by step by the program. First, cell 4 must be used to calib

the reference volume (pressure) VA. Then, the calibration sphere is cycled throug

all five cells to determine the empty cell volume (pressure). The calibration va

are stored in the pycnometer and used until a new calibration is performed. A

pycnometer calibration takes up to 30 min.

The instrument calibrates VA by performing two pressurizations, once with the

sample cell empty (VS = 0) and once with the calibration standard of volume Vstd

in the same sample cell.

Equation on page 8 derived previously for a sample measurement for these tw

conditions can be written as

VS = 0 = VC - VA / [(P'1/P'2) - 1] (33)

and

VS = Vstd = VC - VA / [(P1/ P2) - 1]. (34)

Combining these two equations yields

VA = Vstd / {[1/(P'1/ P'2) - 1)] - [1/(P1/ P2) - 1]}. (35)

The instrument calibrates the volume VC of each cell with one pressurization of

each cell holding the appropriate sample holder and the calibration standard.

Equation on page 10 is then used and can be written as:

VS = Vstd = VC + VA / [1 - (P1/ P2)] (36)

VC = VA {1 / [(P1/ P2) - 1]}. (37)

PERFORMANCE

Precision Standards of 1and 20 g are measured to confirm balance calibration, and the

readings should be within 1 mg, or better than 0.1%. Repeatability of specime

mass at sea should also be within 0.1%.

For the pycnometer, a standard sphere is measured (e.g., 7.0699 cm3) and

precision should be within 0.1% (0.005 cm3 for the sphere mentioned). Repeat

measurements on sediment samples yield a precision of about 1%, probably


the

not

in a

box.

resulting from changes in ambient pressure and temperature and the material

during handling.

Accuracy Mass error: 0.1%.

Volume error: 1%.

MEASUREMENT

The user is guided through data entry by the MAD program, which controls the

balance as well as the pycnometer. The sample ID needs to be entered only once

for the entire process. The pycnometer key pad is not used during measurement.

The following is the general measurement protocol:

1. Typical sampling frequency for MAD measurements is two per section. One per section is considered a minimum; more than two per section on medium- to high-recovery legs is rather demanding with the present staff assignments.

2. Fill a numbered 10-mL Pyrex beaker with sediment to about 3 mm below the rim so that material is not lost during handling of the beaker. The largest errors in MAD measurements probably stem from lost material during the process and from volume measurements with incompletely filled beakers. It is the operator’s responsibility to find the optimum. Place a special PP Styrofoam plug into the hole left from where the sample was taken fromworking-half core.

3. Enter the sample and beaker number into the Sample program at the sampling table. This information will then be in the database; only the beaker number is used at the MAD station to select samples.

4. Measure the mass. Do not let the sample stand without covering it withplastic film, being careful not to lose material.

5. Optionally, measure the wet volume in the pycnometer. However, this isnecessary and years of experience have shown that wet volume measurements (method B) appear to have a large error.

6. Place the sample in the oven at 105° ± 5°C for 24 hr. Place the sampledesiccator after it is removed it from the oven.

7. Measure the mass and volume of the dry sample and beaker.

8. Place the residue in a sample bag, attach a completed label, seal, and

9. Clean the beaker.


DATA SPECIFICATIONS

Database model

Notes: The Sample table is used for all ODP core samples. MAD samples are identified by sampling code; the ODP standard designation is linked through the beaker_id. If method A is used the “fixed_volume” must be set to be TRUE. The MAD calibration history table is a log of calibrations but does not hold the calibration data.

Standard Queries

Table 2—2 MAD database model.

Sample MAD sample data MAD control data MAD beaker historysample_id [PK1] sample_id [PK1] [FK] mad_control_id [PK1] mad_beaker_id [PK1]location [PK2] location [PK2] [FK] run_date_time beaker_date_time [PK2]

sam_section_id . section_id mad_beaker_id ctrl_standard_id beaker_numbersam_archive_working beaker_date_time control_type beaker_type

top_interval fixed_volume expected_value beaker_massbottom_interval mass_wet_and_beaker pyc_cell_no beaker_volumepiece mass_dry_and_beaker measured_value

sub_piece vol_wet_and_beaker measured_stdev MAD beakerbeaker_id . mad_beaker_id vol_wet_and_beaker_stdev mad_beaker_id [PK1]

volume vol_wet_and_beaker_nentered_by vol_wet_and_beaker_cellsample_depth vol_dry_and_beaker MAD calibration history

sample_comment vol_dry_and_beaker _stdev mad_calibratin_id [PK1]sam_repository . repository vol_dry_and_beaker_n calibration_date_time

s_c_leg . leg vol_dry_and_beaker_cell calibration_types_c_sam_code . sam_code commentssam_sample_code_lab . s_c_l sample_date_time

Table 2—3 MAD query A (results).

Short description Description DatabaseSample ID ODP standard sample designation Link through [Sample]sample_idDepth User-selected depth type Link through [Sample]sample_idWb Water content, relative to bulk mass see MAD Query BWs Water content, relative to solid mass see MAD Query BCalculations depend on the volume measurement method used: A, B, or CBulk density Bulk density, method A, B, or C see MAD Query BDry density Dry density, method A or B see MAD Query BGrain density) Grain density, method A or B see MAD Query BPorosity Porosity, method A or B see MAD Query BVoid ratio Void ratio, method A or B see MAD Query B

Table 2—4 MAD query B (results, measurements, and parameters) (to be implemented).

Short description Description DatabaseSample ID ODP standard sample designation Link through [Sample]sample_idDepth User-selected depth type Link through [Sample]sample_idMethod A Indicates if method A was used [MAD Sample Data] fixed_volume Mb+beak Bulk mass of sample + beaker [MAD Sample Data] mass_wet_and_beakerMd+beak Dry mass of sample + beaker [MAD Sample Data] mass_dry_and_beakerVb+beak Bulk volume of sample (+ beaker for B) [MAD Sample Data] vol_wet_and_beakersd(Vb+beak) Std. dev. of n vol. measurements (for B) [MAD Sample Data] vol_wet_and_bkr_sdn(Vb+beak) No. of vol. measurements (for B) [MAD Sample Data] vol_wet_and_bkr_nc(Vb+beak) Cell no. used for vol. measurement (for B) [MAD Sample Data] vol_wet_and_bkr_cellVd+beak Dry volume of sample + beaker [MAD Sample Data] vol_wet_and_beaker


sd(Vd+beak) Std. dev. of n vol. measurements [MAD Sample Data] vol_wet_and_bkr_sdn(Vd+beak) No. of vol. measurements [MAD Sample Data] vol_wet_and_bkr_nc(Vd+beak) Cell no. used for vol. measurement. [MAD Sample Data] vol_wet_and_bkr_cellComments Comments commentsDate/Time Date and time of measurement sample_date_timeBeaker Beaker number [MAD Beaker History] beaker_numberMbeak Mass of beaker [MAD Beaker History] beaker_massVbeak Volume of beaker [MAD Beaker History] beaker_volumeMb Bulk mass = (Mb+beak) - MbeakMd Dry mass (includes evaporated salt) = (Md+beak) - MbeakMpw Mass of porewater = (Mb - Md) / 0.965Ms Mass of solids (salt-corrected) = (Md - 0.035*Mb) / 0.965Vpw Volume of porewater = Mpw / 1.024Msalt Mass of evaporated salt = Mpw - (Mb - Md)Vsalt Volume of evaporated salt = Msalt / 2.20Wb Water content relative to bulk mass = Mpw / MbWs Water content relative to solid mass = Mpw / MsFor volume method AVb(A) Bulk volume (method A) = (Vb+beak)Vs(A) Volume of solids (methods A) = Vb(A) - VpwFor volume method BVb(B) Bulk volume (method B) = (Vb+beak) - VbeakVs(B) Volume of solids (methods B) = Vb(B) - VpwFor volume method CVd(C) Dry volume (method C) = (Vd+beak) - VbeakVs(C) Volume of solids (method C) = Vd(C) - VsaltVb(C) Bulk volume (method C) = Vs(C) + VpwFor volume method A or BBulk density Bulk density, method A or B = Mb / Vb(A,B)Dry density Dry density, method A or B = Ms / Vb(A,B)Grain density) Grain density, method A or B = Ms / Vs(A,B)Porosity Porosity, method A or B = Vpw / Vb(A,B)Void ratio Void ratio, method A or B = Vpw / Vs(A,B)For volume method CBulk density Bulk density, method C = Mb / Vb(C)Dry density Dry density, method C = Ms / Vb(C)Grain density) Grain density, method C = Ms / Vs(C)Porosity Porosity, method C = Vpw / Vb(C)Void ratio Void ratio, method C = Vpw / Vs(C)

Table 2—4 MAD query B (results, measurements, and parameters) (to be implemented).

Table 2—5 MAD query C (control measurements) (to be implemented).

Short description Description DatabaseDate/Time Date/time of control measurement. [MAD Control Data] run_date_timeStandard Standard identification [MAD Control Data] ctrl_standard_idType Type of control meas. (mass or vol.) [MAD Control Data] control_typeExpected Expected value [MAD Control Data] expected_valueCell If pycnometer, cell number used [MAD Control Data] pyc_cell_noMeasured Measured value [MAD Control Data] measured_valueStdev. Std. dev. of multiple vol. meas. [MAD Control Data] measured_stdev

Table 2—6 MAD query D (beaker data) (to be implemented).

Short description Description DatabaseDate/Time Data/time of beaker meas. [MAD Beaker History] beaker_date_time


Beaker Beaker number [MAD Beaker History] beaker_numberType Type of beaker (e.g., Pyrex 10 mL) [MAD Beaker History] beaker_typeMbeak Measured mass of beaker [MAD Beaker History] beaker_massVbeak Calculated volume of beaker [MAD Beaker History] beaker_volume

Table 2—6 MAD query D (beaker data) (to be implemented).

Table 2—7 MAD query E (calibration log) (to be implemented).

Short description Description DatabaseDate/Time Date/time of calibration calibration_date_timeType Type of calibration (mass or vol.) calibration_type



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3. GAMMA-RAY DENSIOMETRY

3.1. Principles

PHYSICAL BACKGROUND

Bulk density of sediments and rocks is estimated from the measurement of

gamma-ray attenuation (GRA) (Tittman and Wahl, 1965; Evans, 1965). The

familiar acronym GRAPE (Evans, 1965) stands for GRA porosity evaluator,

referring to the computer that Evans attached to the density measurement device to

compute porosity using an assumed grain density. The measurement device does

not estimate porosity, and is therefore referred to as GRA densiometer.

The principle is based on the facts that medium-energy gamma rays (0.1–1 M

interact with the formation material mainly by Compton scattering, that the

elements of most rock-forming minerals have similar Compton mass attenuat

coefficients, and that the electron density measured can easily be related to t

material bulk density. The 137Ce source used transmits gamma rays at 660 KeV

scintillation detector measures the gamma-ray beam transmitted through the

material. If the predominant interaction is Compton scattering, transmission o

gamma rays through matter can be related to the electron density by:

Yt = Yi e–nsd, (1)

where Yi is the flux incident on the scatterer of thickness d, Yt is the flux

transmitted through the scatterer, n is the number of scatterers per unit volume or

the electron density, and s is the Compton cross section for scattering per scatterer

in square centimeters per electron. Bulk density ρ of the material is related to the

electron density by

n = ρ NAv (Z/A), (2)

where Z is the atomic number or the number of electrons, A is the atomic mass of

the material, and NAv is the Avogadro number. Bulk density estimates are therefore

accurate for a wide range of lithologies if the Z/A of the constituent elements is

approximately constant. Variations of Z/A are indeed negligible for the most

common rock-forming elements. The GRA coefficient is defined as

µ = (Z/A) NAv × s (cm2/g). (3)

For the medium energy range of gamma rays and for materials with Z/A of about 1/

2, such as the most common minerals, the “Compton µ” is approximately 0.10

cm2/g, increasing with decreasing energy. For water, µ is about 11% higher than

for common minerals at a particular energy (e.g., Harms and Choquette, 1965

Sediments can therefore be regarded as two-phase systems in regard to GRA

(mineral-water mixtures).


Equation on page 1 can now be written in the more frequently referenced form

Yt = Yi × e–ρµd (4)

and the expression for the bulk density becomes

ρ = ln (Yt / Yi) / µd. (5)

If the coefficient µ could be determined with sufficient accuracy, it could be used

directly to compute bulk density. However, µ is a function of detected gamma-ray

energy and is therefore dependent on the particular device, including source,

detector, spectral component used, and the material itself (degree of scattering). A

more practical and accurate method is to calibrate the gamma radiation with bulk

density standards as described later in this chapter.


Attenuation Coefficient of Minerals

An important assumption of this densiometry method is that for a given

measurement system the average attenuation coefficient µ is constant for the

measured materials. For a more accurate density estimate, variations in the average

composition of the material must be taken into consideration. If mineralogical

analysis determines that the average µ1 deviates significantly from the standard µ,

the following correction can be applied:

ρ1 = ρ × µ/µ1 , (6)

where the ratio of average coefficients can be calculated from reference tables.

Core Thickness The GRA routine calculations assume a constant core diameter of 66 mm. If voids

or otherwise incompletely filled core liner segments occur because of gas pressure,

gas escape, or other coring disturbances, the density estimate will be too low. (The

highest values are therefore the most reliable ones in disturbed cores.) Using a

thickness log obtained from core photographs or by other means, density can

easily be corrected for varying core thickness using

ρ1 = ρ × d/d1 . (7)

USE OF GRA DATA

GRA data provide a precise and densely sampled record of bulk density, an

indicator of lithology and porosity changes. The records are frequently used for

core-to-core correlation. Another important application is the calculation of

acoustic impedance and construction of synthetic seismograms.

3.2. MST (Whole-Core) GRA System

EQUIPMENT

Gamma-ray Source The 137Ce source used transmits gamma rays at 660 KeV.


6.6

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Scintillation Counter A standard NaI scintillation detector is used in conjunction with a universal

counter.

CALIBRATION

New Procedure GRA calibration assumes a two-phase system model for sediments and rocks,

where the two phases are the minerals and the interstitial water. Aluminum has an

attenuation coefficient similar to common minerals and is used as the mineral

phase standard. Pure water is used as the interstitial-water phase standard. The

actual standard consists of a telescoping aluminum rod (five elements of varying

thickness) mounted in a piece of core liner and filled with distilled water (Figure

3—1). The standard element i has an average bulk density ρi of

ρi = di /D × ρAl + (D – di)/D × ρwater (8)

where D is the maximum aluminum rod thickness (inner diameter of core liner,

cm), di is the diameter of the aluminum rod in element i, and ρAl and ρwater are the

densities of aluminum and water, respectively. The first element (porosity of 0

has a bulk density of aluminum (2.70 g/cm3) and the last element (porosity of

100%) has a bulk density of water at laboratory temperature (1.00 g/cm3).

Intermediate elements are used to verify the linearity of the ln(Y) to density

relationship, as well as the precise alignement of core and sensor. A linear le

squares fit through three to five calibration points (ln(counts/tcal), ρ) yields the

calibration coefficients m0 (intercept) and m1 (slope, negative). Total measured

counts are automatically divided by the counting time, tcal, to normalize the

coefficients to counts per second. Sample density is then determined:

ρcore = m0 + ln (counts/tsample) × m1 , (9)

where the measured counts are again normalized to counts per second using

sampling period, tsample , before the calibration coefficients are applied.

Old Procedure The present calibration procedure has been implemented only since Leg 169

(August 1996). Before that time, calibration was performed with two aluminum

cylinders of different thickness, but without water. The thinner aluminum rod w

cut to a diameter of 25 mm to give an “aluminum density of 1.00.” The counts

returned from measuring the thin aluminum rod were not compatible with the

Compton attenuation coefficient for water, however, and when measuring wat

the density was about 11% too high. A fluid-correction had to be applied to th

initial density estimate. This procedure is obsolete now, and no fluid correctio

required because water is used in the calibration procedure.


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MEASUREMENT

The GRA is logged downcore automatically..

Figure 3—1 Schematic of GRA calibration. A. Physical standard used. B. Msurement geometery. C. Calibration principle. D. Application of calibration core measurement

PERFORMANCE

Precision Precision is proportional to the square root of the counts measured because

gamma-ray emission is subject to Poisson statistics (see “Natural Gamma

Radiation” chapter for additional explanation). The statistical uncertainty is

t N ± z (t N)1/2, (10)

where N is the count rate (counts per second, cps), t is the sampling period (s), and

z is the number of standard deviations for the normal distribution (0.68 probab

or confidence, for z = 1; 0.95 for z = 1.96, etc.). Measurements with the present

system have typically count rates of 10,000 (dense rock) to 20,000 cps (soft m

If measured for 4 s, the statistical error is therefore less than 40,000 ± 200, or

ln(counts/tcal)

Density (g/cm3)

m0(g/cm3) m1

(g/cm3)

• counts = total measured counts• tcal = calibration counting period (s)

ρcore = ρ'core × dcore / dstandard

S1 S3S2 S4 S5

Distilled water

Aluminum

49 mm2.28 g/cm3

32 mm1.83 g/cm3

66 mm2.72 g/cm3

16 mm1.42 g/cm3

0 mm1.00 g/cm3

Rod thickness:Average density:

Core liner

Center/support disk

Thin rodprovides

alignmentcontrol

GAMMA-RAY ATTENUATION DENSIOMETRY

Scintillationdetector

A

B C

D

137Ceγ source

dcore

Two-phase model: minerals = aluminum; pore water = distilled water

• tsam = sampling period (s)• dcore values are determined separately, standard report assumes full core liner, so that dcore = dstandard (= 66 mm for ODP)

ρ’core = m0 + m1× ln(counts/tsam)


0.5%. This shows that the high flux of the 137Ce source does not require excessive

counting times.

Accuracy Accuracy is limited by the assumption that the measured material has the same

attenuation coefficent as the calibration standards used. For general sediment-

water mixtures, this should be the case and errors should be less than 5%.

Spatial Resolution The GRA system allows high spatial resolution of about 0.5 cm.

DATA SPECIFICATIONS

Database Model

Notes: GRA control 1 are control measurements run the same way as a core section. GRA control 2 are measurement taken before run. GRA control 3 are control measurements from a standard mounted on the core boat.

Standard Queries

Table 3—1 GRA database model.

GRA section GRA control 1 GRA control 3 GRA calibrationgra_id [PK1] gra_ctrl_1_id [PK1] [FK] gra_ctrl_3_id [PK1] density_calibration_id [PK1]section_id run_number run_number calibration_date_time

run_number run_date_time run_date_time run_numberrun_date_time core_status requested_daq_period system_id

core_status liner_status actual_daq_period liner_statusliner_status requested_daq_interval density_calibration_id requested_daq_periodrequested_daq_interval requested_daq_period standard_id density_m0

requested_daq_period density_calibration_id meas_counts density_m1density_calibration_id standard_id density_mse

mst_gra_ctrl_2_id commentsmst_gra_ctrl_3_id GRA control 2

gra_ctrl_2_id [PK1] GRA calibration data

GRA section data GRA control 1 Data run_number density_calibration_id [PK1] [FK]gra_id [PK1] [FK] gra_ctrl_1_id [PK1] [FK] run_date_time mst_top_interval [PK2]

mst_top_interval [PK2] mst_top_interval [PK2] requested_daq_period standard_id [PK3][FK]mst_bottom_interval mst_bottom_interval actual_daq_period mst_bottom_intervalactual_daq_period actual_daq_period density_calibration_id standard_density

meas_counts meas_counts meas_counts actual_daq_periodcore_diameter core_diameter meas_counts

Table 3—2 GRA report.

Short description Description DatabaseA: ResultsSample ID ODP standard sample designation Link through [GRA Section]section_idDepth User-selected depth type Link through [GRA Section]section_idBulk density = [GRA Calibration] density_m0 +

ln ([GRA Section data] meas_counts)/ [GRA Section data] actual_daq_period)* [GRA Calibration] density_m1

B (optional): Parameters and measurementsRun Run number [GRA Section] run_numberDate/Time Run date/time [GRA Section] run_date_timeCore Status HALF or FULL [GRA Section] core_status


Liner Status NONE, HALF or FULL [GRA Section] liner_status Req. Interval User-defined sampling interval (cm) [GRA Section] requested_daq_intervalReq. Period User-defined sampling period (s) [GRA Section] requested_daq_periodPeriod Measured sampling period (s) [GRA Section Data] actual_daq_periodCounts Measured counts (not normalized) [GRA Section Data] meas_countsCore Dia. Core diameter, default = 6.6 cm [GRA Section Data] core_diameterCal. Date/Time Calibration date/time [GRA Calibration] Calibration_date_timeCal. m0 Calibration intercept (g/cm3) [GRA Calibration] density_m0

Cal. m1 Calibration slope ([g/cm3)]/cps) [GRA Calibration] density_m1

Table 3—2 GRA report.

Table 3—3 GRA control 1 measurements (to be implemented).

Short description Description DatabaseBulk density =[GRA Calibration] density_m0 +

ln ([GRA Ctrl 1 Data] meas_counts/ [GRA Ctrl 1 Data] actual_daq_period)* [GRA Calibration] density_m1

Run Run number [GRA Ctrl 1] run_numberDate/Time Run date/time [GRA Ctrl 1] run_date_timeCore Status HALF or FULL [GRA Ctrl 1] core_statusLiner Status NONE, HALF or FULL [GRA Ctrl 1] liner_statusStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Interval Interval top [GRA Ctrl 1 Data] mst_top_intervalReq. Interval User-defined sampling interval (cm) [GRA Ctrl 1] requested_daq_intervalReq. Period User-defined sampling period (s) [GRA Ctrl 1] requested_daq_periodPeriod Measured sampling period (s) [GRA Ctrl 1 Data] actual_daq_periodCounts Measured counts (not normalized) [GRA Ctrl 1 Data] meas_countsCore Dia. Core diameter, default = 6.6 cm [GRA Ctrl 1 Data] core_diameterCal. Date/Time Calibration date/time [GRA Calibration] Calibration_date_timeCal. m0 Calibration intercept (g/cm3) [GRA Calibration] density_m0




ln ([GRA Ctrl 2 Data] meas_counts/ [GRA Ctrl 2 Data] actual_daq_period)* [GRA Calibration] density_m1

Run Run number [GRA Ctrl 2] run_numberDate/Time Run date/time [GRA Ctrl 2] run_date_timeReq. Period User-defined sampling period (s) [GRA Ctrl 2] requested_daq_periodPeriod Measured sampling period (s) [GRA Ctrl 2 Data] actual_daq_periodCounts Measured counts (not normalized) [GRA Ctrl 2 Data] meas_countsCal. Date/Time Calibration date/time [GRA Calibration] Calibration_date_timeCal. m0 Calibration intercept (g/cm3) [GRA Calibration] density_m0




ln ([GRA Ctrl 3 Data] meas_counts/ [GRA Ctrl 3 Data] actual_daq_period)


3.3. Split-core GRA System

ODP has purchased a split-core GRA system that will be implemented as soon as

resources become available. This system must be implemented together with the

latest model GEOTEK P-wave logger which provides the caliper measurement

required to correct split-core GRA measurements for uneven split-core thickness.

* [GRA Calibration] density_m1Run Run number [GRA Ctrl 3] run_numberDate/Time Run date/time [GRA Ctrl 3] run_date_timeStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Req. Period User-defined sampling period (s) [GRA Ctrl 3] requested_daq_periodPeriod Measured sampling period (s) [GRA Ctrl 3 Data] actual_daq_periodCounts Measured counts (not normalized) [GRA Ctrl 3 Data] meas_countsCal. Date/Time Calibration date/time [GRA Calibration] Calibration_date_timeCal. m0 Calibration intercept (g/cm3) [GRA Calibration] density_m0



Table 3—6 GRA calibration data (to be implemented).

Short description Description DatabaseDate/Time Calibration date/time [GRA Calibration] calibration_date_timeCal. m0 Calibration intercept (g/cm3) [GRA Calibration] density_m0


Cal. mse Calibration mean squared error [GRA Calibration] mseRun Run number [GRA Calibration] run_numberLiner Status NONE, HALF or FULL [GRA Calibration] liner_statusReq. Period User-defined sampling period (s) [GRA Calibration] requested_daq_periodComments Comments [GRA Calibration] commentsStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Density Density value from MST control [GRA Calibration Data] standard_densityInterval Interval top [GRA Calibration Data] mst_top_intervalPeriod Measured sampling period (s) [GRA Calibration Data] actual_daq_periodCounts Measured counts (not normalized) [GRA Calibration Data] meas_counts


ral

4. MAGNETIC SUSCEPTIBILITY

4.1. Principles

PHYSICAL BACKGROUND

Magnetic susceptibility is the degree to which a material can be magnetized in an

external magnetic field. If the ratio of the magnetization is expressed per unit

volume, volume susceptibility is defined as

κ = M / H, (1)

where M is the volume magnetization induced in a material of susceptibility κ by

the applied external field H. Volume susceptibility is a dimensionless quantity. The

value depends on the measurement system used:

κ(SI) = 4π κ(cgs) = 4π G Oe–1, (2)

where G and Oe are abbreviations for Gauss and Orstedt, respectively. The SI

system should be used.

Mass, or specific, susceptibility is defined as

χ = κ / ρ , (3)

where ρ is the density of the material. The dimensions of mass susceptibility are

therefore m3/kg.

Magnetic susceptibility measured by the common methods is an apparent value

because of the self-demagnetizing effect associated with anisotropy connected

with the shape of magnetic bodies, such as magnetite grains (Thompson and

Oldfield, 1986). When a substance is magnetized its internal magnetic field is less

than the externally applied field. κi, the intrinsic susceptibility, relates the induced

magnetization to the internal magnetic field, whereas κe, the extrinsic

susceptibility which we actually observe, relates the induced magnetization to the

externally applied field. The relationship between the two susceptibilities can be

shown to be

κe = κi / (1 + Nκi), (4)

where N is the demagnetization factor. For a strongly magnetic mineral, such as

magnetite, Nκi > 1, and κe ~ 1/N. If N is known, there is a simple relationship

between the concentration of ferrimagnetic grains and the magnetic susceptibility.

This is the case for natural samples where the concentration of ferrimagnetic

minerals is a few percent or less. The measured susceptibility κ can be

approximated:

κ = ƒκe ~ ƒ/N , (5)

where ƒ is the volume fraction of ferrimagnetic grains. It is found that for natu

samples N is reasonably constant with a value close to 1/3. Thus, if the grain


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shapes are roughly spherical and the dominant mineral is magnetite, the volume

fraction (ƒ << 1) can be estimated by dividing the volume susceptibility by 3.

The commonly used magnetic susceptibility is measured at very low fields usu

not exceeding 0.5 mT (millitesla). It is therefore also referred to as low-field

susceptibility. For comparison, about 50 mT is required to change orientation

magnetite, and high-field susceptibility is obtained from hysteresis measurem

at fields of a few hundred millitesla.

In practice, volume susceptibility is generally measured with core logging devi

for which calibration factors must be established to account for the specific

geometry and effects of core conveyors and core liners. In the case of discret

specimen measurements, the mass of the specimen can be determined more

accurately than volume and specific susceptibility is directly obtained. If avera

grain density and moisture content of the specimen are known, the specimen

measurements can be compared with core logging measurements. Susceptib

values can then be normalized to mass and volume corrected for porosity. Thi

make susceptibility data more useful for quantitative estimates in conjunction

other mineral phases, such as carbonate, which are always normalized to dry

Susceptibility values for some common minerals and rocks are listed in Table

1.

Table 4—1 Susceptibilities of common minerals and rocks (simplified from Het al., 1995; supplemented with underlined values from Thompson and Oldfie1986).

κ (10-6 SI) χ (10-8 m3/kg)

Non-iron-bearing

Plastic (e.g., perspex, PVC) ~-5 ~-0.5

Ice or water -9 -1/-0.9

Calcite -7.5 to -39 -0.3 to -1.4

Quartz, feldspar, magnesite -13 to -17 -0.5 to -0.6

Kaolinite -50 -2

Halite, gypsum, anhydrite -10 to -60 -0.5 to -2.0

Serpentinite 3,100 to 75,000 120 to 2,900

Iron-bearing minerals

Illite, montmorillonite 330 to 410 5 to 13 to 15

Biotite 1,500 to 2,900 5 to 52 to 95 to 98

Orthopyroxene, olivines, amphiboles 1,500 to 1,800 1 to 43 to 50 to 130

Goethitea 1,100 to 12,000 26 to 70 to 280

Franklinites 450,000 8,700

Irona 3,900,000 50,000 to 2,000,000

Iron sulfides

Chalcopyrite 23 to 400 0.6 to 3 to 10

Pyrite 35-5,000 1 to 30 to 100

Pyrrhotitesa 460 to 1,400,000 10 to 5,000 to 30,000


unt ld,


Cores should be equilibrated to room temperature before measurement.

USE OF MAGNETIC SUSCEPTIBILITY

Magnetic susceptibility is used mostly as a relative proxy indicator for changes in

composition that can be linked to paleoclimate-controlled depositional processes.

The high precision and sensitivity of susceptibility loggers makes this

measurement extremely useful for core-to-core and core-downhole log correlation.

The physical link of magnetic susceptibility to particular sediment components,

ocean or wind current strength and direction, or provenance, usually requires more

detailed magnetic properties studies in a specialized shorebased laboratory.

Iron-titanium oxides

Hematitea 500 to 40,000 10 to 60 to 760

Maghemitea 2,000,000 to 2,500,000 40,000 to 50,000

Ilmenitea 2,200 to 3,800,000 46 to 200 to 80,000

Magnetitea 1,000,000 to 5,700,000 20,000 to 50,000 to 110,000

Titanomagnetite 130,000 to 620,000 2,500 to 12,000

Titanomaghemite 2,200,000 57,000

Ulvospinel 4,800 100

Average rock values

Sandstones, shales, limestones 0 to 25,000 0 to 1,200

Dolomite -10 to -940 -1 to -41

Clay 170 to 250 10 to 15

Coal 25 1.9

Basalt, diabase 250 to 180,000 8.4 - 6,100

Gabbro 1,000 to 90,000 26 to 3,000

Peridotite 96,000 to 200,000 3,000 to 6,200

Granite 0 to 50,000 0 to 1,900

Rhyolite 250 to 38,000 10 to 1,500

Amphibolite 750 25

Gneiss 0 to 25,000 0 to 900

Slate 0 to 38,000 0 to 1,400

Schist, phyllite 26 to 3,000 1 to 110

Serpentine 3,100 to 18,000 110 to 630

aRemanence-carrying minerals

Table 4—1 Susceptibilities of common minerals and rocks (simplified from Het al., 1995; supplemented with underlined values from Thompson and Oldfie1986).


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4.2. Bartington MS2C Coil Sensor for Whole Cores (MSL)

EQUIPMENT

A Bartington Instruments MS2C system is integrated in the ODP MST for whole-

core logging. The main unit is the widely used, versatile MS2 susceptometer for

rapid measurements with a number of sensors. The unit has a measuring range of 1

×10–5 to 9999 × 10–5 (SI, volume specific) or 1 ×10–8 to 9999 × 10–8 (SI, mass

specific). It has five front panel controls: on-off switch, sensitivity range switch, SI

or cgs unit switch, zero button, measure button, and continuous measurement

switch. None of these controls needs to be operated because the instrument is

controlled by the MST program. The unit switch should always be on SI. The

range switch should be on the lower sensitivity (1.0), which allows rapid 1-s

measurements. The MST program allows the collection of multiple 1-s

measurements, which are immediately averaged. This is useful if the sampling

period is set, for example, at 3 s for the GRA measurement and there is time to take

three susceptometer readings simultaneously.

The MS2C loop sensor has an internal diameter of 80 mm, which corresponds to a

coil diameter of 88 mm. It operates at a frequency of 0.565 kHz and an alternating

field (AF) intensity of 80 A/m (= 0.1 mT). Temperature drift is less than 10–5 SI

per hour. The resolution of the loop is 2 × 10–6 SI on the 0.1 range (9 s measuring

time).

Dual-frequency Measurements

Fine-grained magnetic material (single-domain, about 0.003 µm diameter) exh

frequency-dependent susceptibility. The coefficient of frequency dependence

be determined from measurements in dual-frequency mode. The high frequen

used is 5.65 kHz. This mode of measurement is rarely used in general, has n

been requested onboard JOIDES Resolution, and is therefore not implemented for

routine measurements in the MST program.

CALIBRATION

Drift Correction The Bartington instrument is automatically zeroed at the beginning of each ru

before the core enters the loop. Instrument drift may occur during the period o

core section scan. To correct for the drift, a zero-background measurement

(MSbkgd) is taken at the end of a core section log. The drift is corrected under

assumption that it is linear over the time of interest (about 10 min.). The time

elapsed between the zeroing of the instrument at the beginning of the run and

background measurement, tbkgd, is measured. For each measurement within the

core (MSmeas) the elapsed time (t) is also measured, and the background-correct

susceptibility, Mscorr, is calculated as

MScorr = MSmeas + MSbkgd / tbkgd × t . (6)

Absolute Susceptibility Values

The Bartington instrument output values are relative, volume-specific

susceptibilities (κrelative), which must be corrected before they can be reported


2).

nting

SI units. Currently, no correction is implemented for standard queries from the

database. Three ways of correcting the susceptibilities are described here. The

third method is recommended for implementation on JOIDES Resolution in the

near future.

1. Bartington correction factors. Theoretically, the instrument output is in volume-

specific SI units for cores with diameters (d) passing exactly through the coil

diameter (D), i.e., if d/D = 1. Bartington provides a table relating values of d/D to

correction factors that must be applied to the relative susceptibility readings from

the meter. For d = 66 mm and D = 88 mm, d/D is 0.75 and the corresponding

correction factor is 1.48. Then,

κ = κrelative / 1.48 × 10–5 = 0.68 × 10–5 κrelative . (7)

This correction does not take into account other effects such as those from core

liner and core conveyer boat, etc.

2. Calibration with laboratory measurements. Absolute susceptibility is easily

measured on sample cubes in shorebased or shipboard laboratories (Kappabridge).

These measurements can be compared with corresponding readings from the

Bartington instrument. Empirical correlation from Leg 154 and Leg 162 data gave

correction factors of 7.7 × 10–6 and 8.0 × 10–6, respectively. On Leg 154, volumes

of specimens were not exactly determined and may have been slightly smaller than

assumed, which would underestimate the factor.

3. Calibration with core standard (Figure 4—1). The most straightforward

approach is to calibrate the instrument using a piece of core liner (40 cm long)

filled one-half with a homogenous mixture of magnetite (about 0.5%, pseudo-

single domain) and epoxy (κstandard ~ 1000 × 10–6) and one-half with pure water

(κwater = -9 × 10–6). The magnetic susceptibiltiy of the standard core is determined

once and precisely from splits. The instrument response is then related to the actual

volume susceptibiltiy, which also eliminates effects related to core geometry and

the core conveyor system. Once this method is implemented, calibration

coefficients can be routinely applied to future measurements and standard data

queries will return absolute susceptibility in SI units.

PERFORMANCE

Precision Precision is 2 × 10–6 (SI). Susceptibility values in natural, marine sediment

samples over an interval of only a few meters (Milankovitch or millennial scale

cyclicity) can range from a few tens to several thousands of 10–6 SI units.

Typically, variations are 2 to 3 orders of magnitude greater than the precision. This

makes magnetic susceptibility one of the most precise proxies for stratigraphic

changes and extremely useful for core-to-core correlation.

Accuracy Accuracy is 5% (according to Bartington).

Spatial Resolution We determined the full-width-half-maximum (FWHM) response from

measurements of four thin discs with varying amounts of iron dust (Figure 4—

The discs were mounted 20 cm apart from each other in a core liner, represe


cts.

ion.. Cal-

thin strata of high susceptibility. Relative susceptibility values ranged from 40 ×

10–6 to 200 × 10–6. The four widths associated with half-maxima ranged from 4.0

to 4.4 cm. The width along the core axis corresponding to >99% response is about

15 cm. It is recommended that the first and last measurement in each core section

be taken 3–4 cm away from the edge to avoid any deconvolution of edge effe

Figure 4—1 Schematic of proposed magnetic susceptibility logger calibratA. physical standard used (To be implemented). B. Measurement geometry. Cibration principle. D. Application of calibration to core measurement.

MEASUREMENT

The magnetic susceptibility is logged downcore automatically.

Relative volume susceptibilty

Absolute volumesusceptibility

(SI)

m0 (SI)

m1(SI)

20 cm

κcore = κ'core × dcore2 / dstandard2

k1) = 1,000 × 10-6 (SI)k = -9 × 10-6 (SI)

Distilled water

Volume susceptibility:

Core linerPseudo-single domainmagnetite in epoxy;e.g. 0.001 mass fraction

1)To be determined exactly in laboratory from splits of the homogeneous standard material.

20 cm

Induction and measurement loop

MAGNETIC SUSCEPTIBILITY LOGGER

dcore

Core liner

A

B C

D

• xdrift determined using elapsed time since start of core section log• Standard report assumes dcore = dstandard (= 66 mm for ODP).

κ’core = m0 + m1 × (x - xdrift)

(x)

x = total relative susceptibility measured


tem.rious

Figure 4—2 Magnetic susceptibility response curves from the MS2C coil sysThe curves were obtained from the measurement of four thin discs with vaamounts of iron powder mounted in a piece of core liner.

0

5

10

15

20

25

30

-15 -10 -5 0 5 10 15

Rel

ativ

e m

agne

tic v

olum

e su

scep

tibili

ty

Distance from coil plane (cm)

12.4

5.18

3.92.64

Half-maximumvalues


DATA SPECIFICATIONS

Database Model

Notes: MSL control 1 are control measurements run the same way as a core section. MSL control 3 are control measurements from a standard mounted on the core boat.

Standard Queries

Table 4—2 MSL database model.

MSL section MSL control 1 MSL control 3msl_id [PK1] msl_ctrl_1_id [PK1] msl_ctrl_3_id [PK1]

section_id run_number run_numberrun_number run_date_time run_date_time

run_date_time core_status req_daqs_per_samplecore_status liner_status standard_idliner_status requested_daq_interval bkgd_susceptibility

requested_daq_interval req_daqs_per_sample bkgd_elapsed_zero_timereq_daqs_per_sample standard_id core_temperature

bkgd_susceptibility bkgd_susceptibility loop_temperaturebkgd_elapsed_zero_time bkgd_elapsed_zero_time meas_susceptibilty_meancore_temperature core_temperature sample_elapsed_zero_time

loop_temperature loop_temperature actual_daq_period

MSL section data MSL control 1 datamsl_id[PK1] [FK] msl_ctrl_1_id [PK1] [FK]

mst_top_interval [PK2] mst_top_interval [PK2]mst_bottom_interval mst_bottom_interval

meas_susceptibility_mean meas_susceptibility_meansample_elapsed_zero_time sample_elapsed_zero_timeactual_daq_period actual_daq_period

core_diameter core_diameter

Table 4—3 MSL report

Short description Description DatabaseA: ResultsSample ID ODP standard sample designation Link through [MSL Section] section_idDepth User-selected depth type Link through [MSL Section] section_idMag. susc. Drift-corrected magnetic susceptibility =[MSL Section Data] meas_suscept_mean

-[MSL Section] bkgd_susceptibility/ [MSL Section] bkg_elapsed_zero_time* [MSL Section Data] sam_elapsed_zero_time

B (optional): Parameters and measurementsRun Run number [MSL Section] run_numberDate/Time Run date/time [MSL Section] run_date_timeCore Status HALF or FULL [MSL Section] core_statusLiner Status NONE, HALF or FULL [MSL Section] liner_status Req. Interval User-defined sampling interval (cm) [MSL Section] requested_daq_intervalDaqs/sample User-def. data acquisitions per sample [MSL Section] req_daqs_per_sampleBkgd. Susc. Background at end of section run [MSL Section] bkgd_susceptibilityBkgd. Time Time elapsed since start of section. run [MSL Section] bkgd_elapsed_zero_timeCore Temp. Core temperature [MSL Section] core_temperatureLoop Temp. Loop temperature (to be implemented.) [MSL Section] loop_temperatureMag. Susc. Measured magnetic susceptibility [MSL Section Data] meas_suscept_meanElapsed Time time elapsed since start of run (s) [MSL Section Data] sam_elapsed_zero_timePeriod Actual sampling period [MSL Section Data] actual_daq_period Core Dia. Core diameter, default = 6.6 cm [MSL Section Data] core_diameter


4.3. MS2E1 Point Sensor for Split-Core Logger

At the end of 1996, ODP has purchased a magnetic susceptibility probe type MS2F

manufactured by Bartington. This miniature probe is ideally suited for

measurements on splitcore surfaces with roughness <1 mm. The FWHM response

Table 4—4 MSL control 1 measurements (to be implemented).

Short description Description DatabaseMag. susc. =[MSL Ctrl 1 Data] meas_suscept_mean

-[MSL Ctrl 1] bkgd_susceptibility/ [MSL Ctrl 1] bkg_elapsed_zero_time* [MSL Ctrl 1 Data] sam_elapsed_zero_time

Run Run number [MSL Ctrl 1] run_numberDate/Time Run date/time [MSL Ctrl 1] run_date_timeCore Status HALF or FULL [MSL Ctrl 1] core_statusLiner Status NONE, HALF or FULL [MSL Ctrl 1] liner_status Req. Interval User-defined sampling interval (cm) [MSL Ctrl 1] requested_daq_intervalDaqs/sample User-def. data acquisitions per sample [MSL Ctrl 1] req_daqs_per_sampleStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Bkgd. Susc. Background at end of section run [MSL Ctrl 1] bkgd_susceptibilityBkgd. Time Time elapsed since start of section run [MSL Ctrl 1] bkgd_elapsed_zero_timeCore Temp. Core temperature [MSL Ctrl 1] core_temperatureLoop Temp. Loop temperature (to be implemented.) [MSL Ctrl 1] loop_temperatureInterval Interval top [MSL Ctrl 1 Data] mst_top_intervalMag. Susc. Measured magnetic susceptibility [MSL Ctrl 1 Data] meas_suscept_meanElapsed Time time elapsed since start of run (s) [MSL Ctrl 1 Data] sam_elapsed_zero_timePeriod Actual sampling period [MSL Ctrl 1 Data] actual_daq_period Core Dia. Core diameter, default = 6.6 cm [MSL Ctrl 1 Data] core_diameter

Table 4—5 MSL control 3 measurements (to be implemented).

Short description Description DatabaseMag. susc. =[MSL Ctrl 3] meas_suscept_mean

-[MSL Ctrl 3] bkgd_susceptibility/ [MSL Ctrl 3] bkg_elapsed_zero_time* [MSL Ctrl 3] sam_elapsed_zero_time

Run Run number [MSL Ctrl 3] run_numberDate/Time Run date/time [MSL Ctrl 3] run_date_timeDaqs/sample User-def. data acquisitions per sample [MSL Ctrl 3] req_daqs_per_sampleStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Bkgd. Susc. Background at end of section run [MSL Ctrl 3] bkgd_susceptibilityBkgd. Time Time elapsed since start of section run [MSL Ctrl 3] bkgd_elapsed_zero_timeCore Temp. Core temperature [MSL Ctrl 3] core_temperatureLoop Temp. Loop temperature (to be implemented) [MSL Ctrl 3] loop_temperatureMag. Susc. Measured magnetic susceptibility [MSL Ctrl 3] meas_suscept_meanElapsed Time time elapsed since start of run (s) [MSL Ctrl 3] sam_elapsed_zero_timePeriod Actual sampling period [MSL Ctrl 3 Data] actual_daq_period


measured in two axes on the plane of the sensing surface has linear dimensions of

3.8 × 10.5 mm, giving a spatial resolution 1 order of magnitude better than with the

loop sensor (FWHM of 42 mm). The depth response below the surface of

investigation drops to 50% at 1 mm and to 10% at 3.5 mm depth, requiring full

contact with a smooth surface. The sensor operates at a frequency of 2 kHz and has

the same resolution (2 ×10–6 SI on 0.1 range) and slightly larger measuring time

(1.2 s at 1.0 setting) than the coil sensor.

The MS2E1 sensing surface is at the end of a ceramic tube and is protected by a

thin ceramic (aluminum oxide) plate that must be in immediate contact with the

surface of investigation during the measurement. The tube is mounted on a metal

enclosure that houses the electronic circuitry. Soft or wet cores may be protected

by a thin plastic film of a thickness less than 0.05 mm. This also prevents the

pickup of potentially contaminating material that could create inaccuracies.

This sensor will be implemented on either the archive-half or working-half core

logging system. Both systems are in the design stage.


es

,

tes,

e

is

ific

at a

5. NATURAL GAMMA RADIATION

5.1. Principles

PHYSICAL BACKGROUND

Source of radiation Natural gamma radiation (NGR) is a useful lithologic parameter because the

“primeval” emitters are at secular equilibrium; i.e., radiation at characteristic

energies is constant with time (e.g., Adams and Gaspirini, 1970). Radioisotop

with sufficiently long life and that decay to produce an appreciable amount of

gamma rays are potassium (40K) with a half-life of 1.3 × 109 years, thorium

(232Th) with a half-life of 1.4 × 1010 years, and uranium (238U) with a half-life of

4.4 × 109 years. Minerals that fix K, U, and Th, such as clay minerals, are the

principal source of NGR. Other examples include arkosic silt and sandstones

potassium salts, bituminous and alunitic schists, phosphates, certain carbona

some coals, and acid or acido-basic igneous rocks (Serra, 1984).

Units Gamma rays are electromagnetic waves with frequencies between 1019 and 1021

Hz. They are emitted spontaneously from an atomic nucleus during radioactiv

decay, in packets referred to as photons. The energy transported by a photon

related to the wavelength λ or frequency ν by

E = hν = hc/λ (1)

where c is the velocity of light, and h is Planck’s constant (6.626 10–34 joule). The

energy is expressed in eV (electron-volts). For our purposes, the multiples KeV or

MeV are used. Each nuclear species (isotope) emits gamma rays of one or more

specific energies.

Activity, A, is the rate of radioactive decay and decreases exponentially according

to

A = λdN = λd N0 e-λdt (2)

where λd is the decay constant, and N and N0 are the number of atoms at times t

and t0, respectively. The original unit of activity was defined as the number of

disintegrations per second occuring in 1 g of 226Ra. In 1950, the Curie (Ci) was

redefined as exactly 3.7 × 1010 disintegrations per second. For most purposes, the

multiples mCi or µCi are used. Each radioactive species has an intrinsic spec

activity (ISA), which is the activity of a unit mass of the pure material (the

isotope). According to Adams and Weaver (1958), the relative activities of the

elements K, U, and Th, are 1, 1300, and 3600, respectively.

The well-logging industry created an arbitrary NGR activity scale, the GAPI

(gamma-ray, American Petroleum Industry) units. The GAPI scale is defined


m

ition

n the

API

s a

no

aps

ndent

the

are

l

the

l

ld

n, a

1)

calibration pit at the University of Houston, Texas. The pit consists of three zones

of specific mixtures of Th, U, and K: two of low activity and one of high activity

(Belknap et al., 1959). The GAPI is defined as 1/200 of the deflection measured

between the high- and low-activity zones in the calibration pit. Limestones have

readings of 15–20 GAPI while shales vary from 75 to 150 GAPI, with maximu

readings of about 300 GAPI for very radioactive shales (Dewan, 1983). In add

to the master calibration in the test pit, secondary calibrations are carried out i

field.

Until recently, all commercial NGR logs, including the Schlumberger natural

gamma tool (NGT) logs generated during ODP operations, were reported in G

units. The MST NGR apparatus can obviously not be calibrated in the API

calibration pit, although Hoppie et al. (1994) suggested using downhole logs a

relative GAPI standard for core measurements. However, there appears to be

particular need or advantage to converting core measurements to GAPI, perh

because NGR core logging devices are not widely used. MST-NGR data are

therefore reported in counts per second (cps). This measurement unit is depe

on the device and the volume of material measured; i.e., the cps values from

same ODP cores are different if measured on a different instrument, and they

also different if measured in the ODP device but on different core diameters.

Perhaps the most useful absolute quantification of NGR is expressing the tota

activity in terms of the elemental concentrations of K, U, and Th. Quantifying

emitters is most useful for geologic interpretation. Because most well-logging

companies collect spectral NGR data these days, it is common for industry to

report the measurement in K, U, and Th concentrations. However, the spectra

analysis procedures are not standardized and the quality of the elemental yie

estimates may vary significantly. An ODP project is under way to manufacture

custom standards for the MST-NGR device that will allow elemental yield

estimates in the future.

Statistical Error Counting statistics play an important role in the measurement of radioactive

phenomena, which are random and discrete in nature. The Poisson distributio

simplified binomial distribution, is useful to discribe very small probabilities, p, of

individual observations (decay of one particle in our case) and a very large

number, n, of observations (number of particles in the sample). The parameterλ =

np then occurs for a given variable, X, with the probability, P(X;λ), defined by the

Poisson distribution:

P(X;λ) = (λX e–λ) / X!. (3)

In other words, P(X;λ) is the probability of observing X events when λ events are

expected. The distributions for λ = 4, 16, 49, and 100, where λ values represent

expected NGR count rates, are illustrated in Figure 5—1.

If λ >>1, the Poisson distribution approaches a normal distribution (Figure 5—

and is thus characterized by the mean, µ = λ, and the standard deviation, σ. The

important point is that for binomial distributions σ is related to µ, and for the

Poisson distribution:

σ = µ1/2. (4)


or, is

,

ing

re

for

e

0%.

tan-ror the

As a rule of thumb, the approximation to the normal distribution is adequate if µ Š

2σ; i.e., all but the left-most distribution in Figure 5—1 are adequate

approximations. For a normal distribution, the uncertainty, or the probable err

Perror = z σ , (5)

where z is the independent variable of the normal distribution function. We can

state that for about 68% of a large number of samples, the sample mean, y, will be

within the interval µ ± σ (z = 1); about 5% of the estimates will be outside the

interval µ ± 1.96σ (z = 1.96); etc.

In the case of NGR measurements, the sample mean y is the number of counts

observed, or

y = t N, (6)

where t is the sampling period (s) and N is the count rate (cps). The sample mean

y, is an unbiased estimator of µ. Because the value of µ is not known, we cannot

directly compute the error of the estimate N. However, statistical inference as

outlined here allows us to express the uncertainty as

t N ± z (t N)1/2 (7)

or

%error = z (t N)1/2 / t N × 100% = z / (t N)1/2 × 100%. (8)

Equation on page 3 states that the error decreases exponentially with increas

sampling period, t, increasing count rate, N, and decreasing level of confidence, z.

As a standard practice, z = 1. Standard deviations and relative statistical errors a

indicated for the example distributions in Figure 5—1. It should be noted that

the generally low NGR count rates, the sampling time t must be as long as th

measurement routine allows to reduce the statistical error significantly below 1

This is particularly true if spectral analyses are attempted.

Figure 5—1 Poisson distributions for four selected lambda values. One-sdard-deviation intervals are shown. The red line illustrates the relative erdecreasing exponentially with increasing count rate and also corresponds toPoisson distribution for λ = X.

0.00

0.10

0.20

0

10

20

30

40

50

0 20 40 60 80 100 120

P(X

,λ)

%error (one standard deviation)

X (estimated by observed counts, tN)

λ = µ = 16σ = 4

λ = µ = 100σ = 10

λ = µ = 4σ = 2

λ = µ = 49σ = 7

Relative error (%) for one standarddeviation (68% confidence)


ents

is a

tially

s

rs.

well

and

e

ing

mic

ain

eV

out

e or

g a

NGR Total Counts Total counts refers to the integration of all counts over the photon energy range

between 0 and about 3.0 MeV (about 10 to 0.004 Angstrom wavelength). The total

count is a function of the combined contributions by K, U, and Th (particulary

from 0.5 to 3.0 MeV), matrix density resulting from Compton scattering

(particularly 0.1 – 0.6 Mev), and matrix lithology resulting from photoelectric

absorption (particularly 0 – 0.2 MeV).

The average total count rate from the MST-NGR device and terrigenous sedim

is about 30 cps. With a routine sampling time of 30 s, an average statistical

precision for one standard deviation of 900 ± 30, or 3%, may be achieved. This

good result for core-to-core correlation. However, it is practically impossible to

interprete the source of the radiation.

NGR Spectrometry The MST-NGR apparatus acquires 256-channel spectral data that could poten

be used for calculating elemental yields for K, Th, and U. NGR spectra of rock

and soils are composed of one emission peak of 40K, more than a dozen emission

peaks for the 238U series (mainly 214Bi), a similar number of 232Th series peaks

(mainly 208Tl and 228Ac), and background (Figure 5—2 and Figure 5—3). The

dominant background is produced by Compton scattering, photoelectric

absorption, and pair production, as well as by low-intensity, discrete emission

peaks of the 238U and 232Th series that disappear in the scatter. Spectral

background is a function of the abundance and distribution of primeval emitte

The goal of NGR spectrometry is to determine spectral components, peaks as

as parts of the background, which effectively estimate the abundance of K, U,

Th despite the odds of large scatter background and matrix effects.

NGR spectra have been analyzed over the past 30 years, mainly from wirelin

logging and airborne prospecting surveys. Various schemes of spectral stripp

have been proposed and evolved with time as electronic circuitry and sensor

performance improve. A basic concept was proposed by the International Ato

Energy Agency (IAEA, 1976) in which one interval is defined for each of the m

peaks of K, U, and Th, centered at the following characteristic energies: 1.46 M

for 40K, 2.62 MeV for 208Tl (Th), and 1.76 MeV for 214Bi (U) (Figure 5—2). The

problem with this concept is that the three main peak areas of K, Th, and U

represent only about 10% of the total spectrum in terms of counting rates. Ab

90% of the counts come from the low-energy part of the spectrum, which is

degraded by Compton scattering.

The subsequent trend in petroleum industry was to divide the spectrum into fiv

more contiguous windows and establish a calibration matrix that allows solvin

system of equations written as follows:

Wi = AiTh + BiU + CiK + ri, (9)

where Wi is the count rate from a predetermined energy window; Ai, Bi, and Ci are

the calibration coefficients derived empirically; and ri is a factor representing the

statistical error. The equations are then solved by minimizing r2 which is the sum

of all ri2. The initial limitation to five-channel data acquisition was related to

limitations in sensor efficiency and electronic circuitry.


gy

data.

asis.

ples

is

or of

e the

with

e of

rate

at

ents,

ical

be

30 s)

t area

mic

ure

re

e

An earlier version of the MST program collected spectral data in five energy

windows compatible with the Schlumberger NGT tool. The windows were (see

also Figure 5—2)

• Window 1: 0.2 – 0.5 MeV,

• Window 2: 0.5 – 1.1 MeV,

• Window 3: 1.1 – 1.59 KeV,

• Window 4: 1.59 – 2.0 MeV, and

• Window 5: 2.0 – 3.0 MeV.

Over the past few years, further improvements in downhole logging technolo

have allowed all survey companies to move to the acquisition of 256-channel

This makes any a priori spectral stripping unnecessary, as the optimum

information can be extracted from the spectra on a more rigorous statistical b

Blum et al. (1997) analyzed NGR spectra from the MST device using 2-hr sam

and calibrated the measurements with instrumental neutron acrivation analys

(INAA), inductively coupled plasma mass spectrometry (ICPMS), and X-ray

fluorescence (XRF) measurements on corresponding core specimens. The

abundance of K, U, and Th could be estimated with one standard deviation err

14%, 20%, and 25%, respectively. These conservative error estimates includ

error in the reference data. The next step for ODP is to obtain standard cores

known amounts of natural K, U, and Th and to derive a reliable calibration

coefficient through linear inversion that can be used to estimate the abundanc

K, U, and Th on a routine basis.

Spectral analysis requires significantly longer counting times than total count

sampling for a comparable precision. The work by Blum et al. (1997) shows th

the 256-channel spectrum can be subdivided into 11 relevant spectral compon

many of which have count rates of only a few counts per second. If the statist

error is to be kept at a few percent, a sampling period of several minutes will

required. In practice, this may be achieved by integrating shorter period (e.g.,

measurements taken a closer intervals (e.g., 10 cm) over a reasonably long

interval. Of course, the improved statistics will come with a reduced spacial

resolution.


Zero Background We refer to zero background as gamma radiation detected in the measuremen

without core material, which originates from a combinaton of high-energy cos

radiation, impurities in the NaI crystals, and soil contamination in the

measurement area. Zero background must be differentiated from spectral

background, which is a result of scattering within the core (Figure 5—2 and Fig

5—3). The value of zero background is easily determined by measuring a co

liner filled with distilled water, and the resulting spectrum is subtracted from th

total measured spectrum of a core sample.


tem at

ow,

ude

or

t,

the

Figure 5—2 Natural gamma-ray spectrum acquired with the MST-NGR sys(from Blum et al., 1997). The inset shows high-energy portion of spectrumenlarged vertical scale. Counting time was 4 hr on a split core. W = windSCHLUM 1 through 5 are the five Schlumberger tool logging windows.

Core Volume Radiation counts are directly proportional to the volume of material in the

measurement area of the scintillation counters. The MST program can be

configured to avoid edge effects at the top and bottom of a core section. However,

voids within a core section, or narrow-diameter cores in general, are not corrected

for. The user can apply corrections based on core photographs or a high-resolution

volume proxy such as gamma-ray densiometry (e.g., Hoppie et al., 1994).

Pore volume may have some control on the NGR signal if variations in NGR

activity downcore are low. Porosity variations are proportional to the concentration

of the matrix, which may be proportional to the concentration of a radioactive

mineral in the formation. However, bulk density varies by less than a factor of two

in the natural materials with which we are concerned (1.4–2.7 g/cm3), whereas

concentration and activity of radioactive material can vary by 1 order of magnit

(e.g., clay-rich vs. carbonate-rich material).

USE OF NGR DATA

NGR measurements are used for three purposes: (1) correlation of core and/

downhole data sets in single or multiple holes, (2) evaluation of the clay/shale

content of a formation, and (3) abundance estimates for K, U, and Th. The firs

and to some degree the second, goals can be achieved by simply measuring

0

50

100

150

0

1000

2000

3000

0 500 1000 1500 2000 2500 3000

Calculated peak baselineMeasured zero backgroundMeasured countsSmoothed, corrected countsCalculated minima

Cou

nts/

chan

nel

Energy (KeV)

208 T

l (58

4 K

eV)

214 B

i (61

0 K

eV)

228 A

c (9

12 a

nd 9

66 K

eV)

214 B

i (11

20 K

eV)

214 B

i (17

64 K

eV)

208 T

l (26

15 K

eV)

B

2 3 4 5 6 9 11 14 15 160 12 1371 8 101 2 W3 W4 W5 W6 W108 9 W11 W12 W14 W15 16 17W0 W13W7

208 T

l (58

4 K

eV)

214 B

i (61

0 K

eV)

Interval

IAEA 1 IAEA 2 IAEA 3

SCHLUM 1 SCHLUM 2 SCHLUM 3 SCHLUM 4 SCHLUM 5

40K

(14

60 K

eV)


rou-o be7).

4),

T

de

le

ed

.

bulk emission (total counts) of the material. Elemental analysis is a more complex

process that requires spectral data acquisition and longer sampling times.

Figure 5—3 Schematic illustration of A. zero-background (to be subtracted tinely; B. zero-background corrected spectrum; and C. spectral background (tdiscriminated in spectral analysis if warranted). Modified from Blum et al. (199

5.2. MST-NGR System

EQUIPMENT

The MST-NGR device consists of four shielded scintillation counters arranged at

90° angles from each other in a plane orthogonal to the core track (Figure 5—

power supply and amplifiers, automated data acquisition control as part of MS

program, and independent PC with EG&G Maestro software for spectral data

acquisition and analysis. The scintillation counters contain doped sodium iodi

(NaI) crystals (3 × 3 in or 7.6 × 7.6 cm) and photomultipliers to produce countab

pulses. When a gamma ray strikes the crystal, a single photon of light is emitt

and strikes a photocathode made from cesium antimony or silver magnesium

Intensity(counts/channel)

Energy

Zero background-corrected spectrum

Window Wi


Energy interval Ii

Window Wi+1

Calculated minimum

Peak baseline

Selectedwindowboundary

A

B

Peak boundary

Background area Bi

Peak area Pi

Energy

C


Energy

Measured zero-background

Measured counts


GR

Photons hitting the photocathode release bundles of electrons, which are

accelerated in an electric field to strike a series of anodes of successively higher

potential. A final electrode conducts a small current through a measure resistor to

give a voltage pulse, signaling that a gamma ray struck the NaI crystal. Analog

signals are converted to digital signals, and the peak height of each pulse is

measured and stored in the appropriate one of 256 channels. The tool response

depends on two factors: (1) detector efficiency or sensitivity; i.e., the number of

gamma-rays detected per unit concentration; and (2) energy response of the

detector; i.e., the resolution and conversion slope of volts input versus output. All

detector and electronics components of the MST-NGR device were supplied by

EG&G ORTEC, Inc. The apparatus was assembled onboard JOIDES Resolution in

March 1993.

Figure 5—4 Configuration of four natural gamma ray sensors in the MST-Nsystem.

CALIBRATION

Tuning the Amplifiers The NGR system contains four scintillation counters that must be tuned to all

return the same signal level for a particular emission energy. Amplification of

signals from the four counters may drift, and it is therefore necessary to adjust the

gain at least at the beginning of each leg. Currently, the independent MAESTRO

program is used to adjust the gain, and the ODP technician should perform the

tuning. The operator should be familiar with the general character of the potassium

and thorium spectra.

Using a potassium source, the MAESTRO display of the spectrum should show

one sharp peak. If more than one peak or a very broad peak are displayed, the

sensor gains must be adjusted. This is done by disconnecting three of the four

MST bench

MST core pass-through

Photo-multipliers

3 in. × 3 in. NaI crystals

Lead shielding

Cu tubing

CATWALK CORELAB

Sensor 1

Sensor 4 Sensor 3

Sensor 2


fourontrol

m

t be

the

ents

he

ps.

hout a

the

ot

th

ment

s with

sensors from the amplifiers and marking the peak of the connected sensor. Then,

the next counter is connected and all others disconnected and the gain is adjusted

until the peak falls exactly on the marker. The same is done with the remaining two

counters. The connections between sensor numbers, leads, and gain adjustment

knobs on the amplifiers are shown in Figure 5—5.

Figure 5—5 Schematic diagram of the gain control panel used to tune thesensor responses. The numbers indicate how lead connects relate to gain cknobs.

This procedure is tedious because each time the gain is adjusted a new counting

period must be initiated. A “hot” source, such as thorium, accelerates the

procedure some. However, there are several characteristic peaks in the thoriu

spectrum, and operators must be very confident that they can match the

appropriate ones.

Once the four scintillation counter gains are tuned, an energy calibration mus

performed.

Zero-Background Correction

Zero background is the radiation caused by impurities in the system, including

NaI crystal itself, and cosmic radiation by-passing the lead shielding. The

background is measured with a water-filled core liner in the system. Counting

times of 1 min and more provide accurate values. Many background measurem

in 1993 and 1994, some taken with counting times of a few hours, show that t

values are constant throughout the day and over a period of weeks at 8 to 9 c

Standard deviations are less than 1 cps. Background measurements taken wit

water core in the device tend to be higher by 1 to 2 cps, presumably because

water core helps to shield the sensor from external radiation.

The zero background is relatively constant and frequent measurements are n

required. A daily control measurement to check on potential contamination wi

soil is sufficient. The ODP standard query uses the latest background measure

in the database taken prior to the core measurement.

Energy Calibration Radioactive decay events are recorded by 256 channels according to photon

energy. These channels must be calibrated for energy by measuring standard

characteristic emission peaks at known energies. A linear regression yields

3 2

4 1

FINE GAINOne full turn to the right moves apeak several tens of KeV to theright

COARSE GAIN:Leave the following settings:Position 1: gain 2Position 2: gain 2Position 3: gain 4Position 4: gain 2

POSITION NUMBER:Corresponds to sensor number

3

2

4

1

LEADS:Disconnect three offour leads to isolateone sensor response


sed

sed. D.

calibration coefficients that are used by the ODP standard query to convert channel

numbers to energy intervals.

At present, potassium and thorium standards are used with main emission peaks at

1.46 MeV and 2.62 MeV for 40K and 232Th, respectively. These peaks are the most

suitable ones because they span the energy spectrum of general interest. The low-

energy, high-count spectrum may be somewhat distorted because of the non-

linearity of the detection and recording system.

The physical standard illustrated in Figure 5—6 is part of a project that awaits

resource allocation. At present, sources of convenience for K (core filled with

KCl) and Th (Schlumberger calibration pad or small flask with Th oxide) are u

for the calibration.

Figure 5—6 Schematic of NGR energy calibration. A. Physical standard u(To be implemented). B. Measurement geometery. C. Calibration principleApplication of calibration to core measurement.

Channel number i

Known energy ofcharacteristic peak (KeV)

m0 (KeV)

m1(KeV)

NATURAL GAMMA RADIATION: I. ENERGY SPECTRUM

Energy of ith channel = m0 + m1 × i

K ~ 5%; U ~ 10 ppm; Th ~ 20 ppm

Distilled water

Approx. concentrations 1):

Core liner

1)To be determined exactly in the laboratory from aliquots of the standard material.

40 cm long

dcore

Core liner

Homogeneous mixture of natural K, U, and Th, and epoxy matrix

Scintill

ation

det

ecto

r

A

B C

D (Tcps)’core = Σ (cps)channel ii = b

i = a

• Tcps = Total counts per second• a = (0 KeV - m0) / m1 [a ≥ 1]; b = (3000 KeV - m0) / m1 [b ≤ 256]• For standard report: dcore = dstd (= 66 mm for ODP)

(Tcps)core = (Tcps)’core × dcore2 / dstd2

256


R

,

the

ad

s not

ation

the

sors:

cm

ayers

rent

Elemental Yield Calibration

Elemental yield calibration is required only if the goal is to estimate the abundance

of K, U, and Th from spectral analysis. The sampling time must be sufficiently

long for this purpose (at least several minutes). Currently, the calibration standards

required to obtain a reliable estimation matrix do not exist. They have been

specified and will be purchased when funds are available.

PERFORMANCE

Precision Because radioactive emissions are random and discrete, they follow the Poisson

distribution, which in turn allows calculating the measurement precision from the

number of accumulated counts. Equations on page 2 through on page 3 and Figure

5—1 in this chapter explain the principles.

Accuracy Accuracy estimates for K, U, and Th elemental abundance obtained from NG

measurements depend on the accuracy and precision of of the reference data

calibration, and the spectral analysis and statistical procedures used to obtain

abundance estimates. Blum et al. (1997) found that K, U, and Th estimates h

total errors of 16%, 30%, and 20%, respectively. This is a conservative error

estimate that includes the uncertainty in the reference values (3%–7%), and i

based on the best possible optimization procedures. Custom-fabricated calibr

standards and more rigorous inversion methods should lead to more accurate

abundance estimates in the future.

Spatial Resolution The diameter of the NaI crystals is 7.6 cm (3 in) and represents the intrinsic

resolution of the system. However, actual spatial resolution is limited because

geometry of the device allows a longer piece of core to be exposed to the sen

the total response curve has a width of about 40 cm. The HMFW is about 12

and represents perhaps the most reasonable measure of spatial resolution. L

thinner than that can be detected only if they have NGR emissions vastly diffe

from the surrounding core.

MEASUREMENT

NGR is logged downcore automatically.


DATA SPECIFICATIONS

Database Model

Notes: NGR Ctrl 1 are control measurements run the same way as a core section. NGR Ctrl 3 are routine measurements on standards mounted on core boat (pure water, essentially a background measurement). NGR Background is for longer (precise) measurements of the background radiation due to cosmic radiation (imperfect shielding) and contamination of the system (crystal impurities, accumulated dirt). Recommended data acquisition period is 10 min (more for special studies with longer counting times on core material). Spectral data are available over the network on the ship. After the leg, they are transferred to off-line media and made available on request.

Standard Queries

Table 1—1 NGR database model.

NGR section NGR control 1 NGR control 3 NGR calibration

ngr_id [PK1] ngr_ctrl_1_id [PK1] ngr_ctrl_3_id [PK1] energy_calibration_id [PK1]

section_id run_number run_number calibration_date_time

run_number run_date_time run_date_time run_number

run_date_time core_status requested_daq_period system_id

core_status liner_status energy_calibration_id channel_energy_m0

liner_status requested_daq_interval standard_id channel_energy_m1

requested_daq_interval requested_daq_period energy_background_id channel_energy_mse

requested_daq_period energy_calibration_id actual_daq_period comments

energy_calibration_id standard_id

energy_background_id energy_background_id NGR calibration data

mst_ngr_ctrl_3_id energy_calibration_id [PK1] [FK]

channel [PK2]

NGR section data NGR control 1 data isotope

ngr_id [PK1] [FK] ngr_ctrl_1_id [PK1] [FK] energy

mst_top_interval [PK2] mst_top_interval [PK2]

mst_bottom_interval mst_bottom_interval NGR background

actual_daq_period actual_daq_period energy_background_id [PK1]

core_diameter core_diameter run_number

total_counts_sec total_counts_sec run_date_time

standard_id

liner_status

requested_daq_period

energy_calibration_id

total_counts_sec

actual_daq_period

NGR spectra data NGR con. 1 spectra data NGR con. 3 spectra data NGR background spectra

ngr_id [PK1] [FK] ngr_ctrl_1_id [PK1] [FK] ngr_ctrl_3_id [PK1] [FK] energy_background_id [PK1]

mst_top_interval [PK2] [FK] mst_top_interval [PK2] [FK] roi_start_channel [PK2] roi_start_channel [PK2]

roi_start_channel [PK3] roi_start_channel [PK3] roi_length_channel roi_length_channel

roi_length_channel roi_length_channel meas_counts actual_daq_period

meas_counts meas_counts meas_counts

Table 1—2 NGR report.

Short description Description DatabaseA: ResultsSample ID ODP standard sample designation Link through [NGR Section] section_idDepth User-selected depth type Link through [NGR Section] section_idTotal counts Zero-background-corrected total counts = [NGR Section data] total_counts_sec -

[NGR Background] total_counts_sec


B (optional): Parameters and measurementsRun Run number [NGR Section] run_numberDate/Time Run date/time [NGR Section] run_date_timeCore Status HALF or FULL [NGR Section] core_statusLiner Status NONE, HALF or FULL [NGR Section] liner_status Req. Interval User-defined sampling interval (cm) [NGR Section] requested_daq_intervalReq. Period User-defined sampling period (s) [NGR Section] requested_daq_periodPeriod Actual sampling period (s0 [NGR Section Data] actual_daq_periodDiameter Core diameter (default + 6.6 cm) [NGR Section Data] core_diameterCounts Total counts (cps) [NGR Section Data] total_counts_secCal. Date/Time Calibration date/time [NGR Calibration] calibration_date_timeCal. m0 Calibration intercept (KeV) [NGR Calibration] channel_energy_m0Cal. m1 Calibration slope (KeV/channel) [NGR Calibration] channel_energy_m1Cal. mse Calibration mean squared error [NGR Calibration] channel_energy_mseBkgd Background total counts (cps) [NGR Background] total_counts_sec

Table 1—2 NGR report.

Table 1—3 NGR control 1 measurements (to be implemented).

Short description Description DatabaseTotal counts = [NGR Ctrl 1 data] total_counts_sec -

[NGR Background] total_counts_secRun Run number [NGR Ctrl 1] run_numberDate/Time Run date/time [NGR Ctrl 1] run_date_timeCore Status HALF or FULL [NGR Ctrl 1] core_statusLiner Status NONE, HALF or FULL [NGR Ctrl 1] liner_status Req. Interval User-defined sampling interval (cm) [NGR Ctrl 1] requested_daq_intervalReq. Period User-defined sampling period (s) [NGR Ctrl 1] requested_daq_periodStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Interval Interval top [NGR Ctrl 1 Data] mst_top_intervalPeriod Actual sampling period (s) [NGR Ctrl 1 Data] actual_daq_periodDiameter Core diameter (default + 6.6 cm) [NGR Ctrl 1 Data] core_diameterCounts Total counts (cps) [NGR Ctrl 1 Data] total_counts_secCal. Date/Time Calibration date/time [NGR Calibration] calibration_date_timeCal. m0 Calibration intercept (g/cm3) [NGR Calibration] channel_energy_m0

Cal. m1 Calibration slope ([g/cm3)]/cps) [NGR Calibration] channel_energy_m1

Cal. mse Calibration slope ([g/cm3)]/cps) [NGR Calibration] channel_energy_mse

Bkgd Background total counts (cps) [NGR Background] total_counts_sec


Short description Description DatabaseTotal counts =[NGR Ctrl 3] total_counts_sec -

[NGR Background] total_counts_secRun Run number [NGR Ctrl 3] run_numberDate/Time Run date/time [NGR Ctrl 3] run_date_timeReq. Period User-defined sampling period (s) [NGR Ctrl 3] requested_daq_periodPeriod Actual sampling period (s0 [NGR Ctrl 3] actual_daq_periodCounts Total counts (cps) [NGR Ctrl 3] total_counts_secStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Cal. Date/Time Calibration date/time [NGR Calibration] calibration_date_time


Cal. m0 Calibration intercept (g/cm3) [NGR Calibration] channel_energy_m0



Bkgd Background total counts (cps) [NGR Background] total_counts_sec


Table 1—5 NGR calibration data (to be implemented).

Short description Description DatabaseDate/Time Calibration date/time [NGR Calibration] calibration_date_timeRun Run number [NGR Calibration] run_numberCal. m0 Calibration intercept m0 (MeV) [NGR Calibration] channel_energy_m0Cal. m1 Calibration slope m1 (MeV/channel) [NGR Calibration] channel_energy_m1Cal. mse Calibration mean squared error [NGR Calibration] channel_energy_mseComments Comments [NGR Calibration] commentsChannel Channel number [NGR Calibration Data] channelIsotope Characteristic isotope emitting at peak [NGR Calibration Data] isotopeEnergy Energy of emission at peak [NGR Calibration Data] energy

Table 1—6 NGR zero background (to be implemented).

Short description Description DatabaseDate/Time Date/time of background meas. [NGR Background] run_date_timeRun Run number [NGR Background] run_numberLiner Status NONE, HALF or FULL [NGR Background] liner_statusReq. Period User-defined sampling period (s) [NGR Background] requested_daq_periodPeriod Actual sampling period (s) [NGR Background] actual_daq_periodCounts Total background counts (cps) [NGR Background] total_counts_secCal. Date/Time Calibration date/time [NGR Calibration] calibration_date_timeCal. m0 Calibration intercept (g/cm3) [NGR Calibration] channel_energy_m0




s

e

.

nce

6. P-WAVE VELOCITY

6.1. Principles

PHYSICAL BACKGROUND

The basic relationship for sonic velocity is

v = d / t, (1)

where d is the distance traveled through the material (in meters) and t is the travel

time through the material (in seconds). The ODP user can choose among four

measurement systems, each using a piezoelectric transducer pair. The basic

equation is adapted to reflect the particular measurement condition.

The PWL system is mounted on the whole-core MST and measures d and t

horizontally through the whole core, with or without the core liner. The

measurements are anywhere in the x-y plane in the conventional core orientation

system (Figure 6—1). PWS1 and PWS2 transducer pairs are designed to be

inserted into the soft and semiconsolidated sediment of split cores. The two

systems are mounted orthogonal to each other to measure along the core axi

(PWS1, z-direction), and perpendicular to the axis and within the split plane

(PWS2, y-direction). The core liner is not involved in these measurements. Th

PWS3 system allows measurements on split cores in the x-direction, with or

without the core liner. In addition, other directions can be measured with the

PWS3 system on cubic or cylindrical, consolidated or lithified core specimens

Total travel time measured between the transducers includes three types of

“delays”:

• delay related to transducer faces and electronic circuitry (tdelay),

• delay related to the peak detection procedure (tpulse), and

• transit time through the core liner, if applicable (tliner).

These delays are explained in detail in the “Calibration” sections. Travel dista

measurements must also be corrected for the liner wall thickness, dliner, if core

liners are involved.

For routine measurements on whole cores in core liners (PWL system):

vcore = (d’core – 2dliner) / (t0 – tpulse – tdelay – 2tliner) × 1000, (2)

where

vcore = corrected velocity through core (km/s),

d’core = measured diameter of core and liner (mm),d1iner = liner wall thickness (mm), andt0 = measured total travel time (µs).


user.

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For the PWS3 system measuring the transit time through the core material and a

split liner, Equation on page 1 is modified only by the factor of 2 for the core liner

correction:

vcore = (d’core – dliner) / (t0 – tpulse – tdelay – tliner) × 1000. (3)

The liner wall correction is applied by default. If rock cuboids or cylinders are

measured with the PWS3 system, the liner correction must be disabled by the

For the PWS1 and PWS2 systems as well as the PWS3 system measuring d

core specimens:

vcore = d’core / (t0 – tpulse – tdelay) × 1000. (4)

With the PWL and PWS3 systems, d’core is determined for each measurement. Fo

the PWS1 and PWS2 systems, the constant d’core is obtained through calibration in

water.


Core Quality Core quality strongly affects the data quality or the ability to aquire P-wave

velocity data. Even if good acoustic coupling with the core liner is achieved, th

may be insufficient coupling between the core material and the core liner. A

typical observation is that the uppermost sediment (seafloor to 10–50 mbsf) y

good data, presumably because the high porosity and limited elastic expansio

maintains cohesion in the soft sediment. Below this and to depth of a few hund

meters, the signal is often strongly attenuated. This effect is more severe if ga

escape is observed on the core cutting platform. Free gas in the sediment exp

greatly upon recovery and may create voids and microcracks that make P-wave

measurements impossible. Once the sediment becomes sufficiently consolida

and lithified, measurements tend to be more successful.

Signal Strength and Attenuation

The measured signal degenerates because of incompletely filled core liner or

or because of attenuation caused by microcracks that formed during core reco

This degeneration is partly reflected by the gain (signal strength) factor applie

the original signal by the automated gain control. However, signal strength als

represents the grain size of the sediment, and low-strength signals can theref

not simply be interpreted as proportional to attenuation. If a filter is applied fo

data reduction, the relative decrease in signal strength from one sample to the

should be taken into consideration as well as the absolute signal strength.

Temperature Equilibrium

P-wave velocity in water is sensitive to temperature. Cores should therefore b

equilibrium when they are measured. Cores are routinely left to equilibrate for

least 4 hr (ODP technicians monitor the temperature with a thremistor probe).

core temperature should be entered by the operator (see “Data Specifications

section; currently this is a manual operation).

In Situ vs. Core Measurements

Measurements on sediment or rock cores differ from in situ measurements

because cores expand on recovery because of lithostatic rebound and, possib

expansion and other factors. Calibration of the measurements with correspon

sonic well logs is recommended. Velocities from sediment cores originating fr


1

sonic

ution

the

—1).

than

s

in

ition,

ich is

more than a few hundred meters below seafloor typically are compatible with

downhole measurements to within less than 3%, whereas shallower core

measurements tend to be up to 5% lower than corresponding downhole

measurements.

USE OF P-WAVE VELOCITY DATA

P-wave velocity varies with the lithology, porosity, and bulk density of the

material; state of stress, such as lithostatic pressure; and fabric or degree of

fracturing. In marine sediments and rocks, velocity values are also controlled by

the degree of consolidation and lithification, fracturing, occurence and abundance

of free gas and gas hydrate, etc. Together with density measurements, sonic

velocity is used to calculate acoustic impedance, or reflection coefficients, which

can be used to estimate the depth of reflectors observed in seismic profiles and to

construct synthetic seismic profiles. Core measurement should be calibrated with

in situ measurements wherever possible.

6.2. MST (Whole-Core) P-Wave Logger (PWL)

EQUIPMENT

The PWL system was purchased from GEOTEK Ltd. (UK) and modified for the

specific requirements of ODP routine core logging. The core travels between two

piezoelectric transducers mounted in epoxy and stainless-steel housings. The two

transducers are used as a transmitter and receiver. Acoustic coupling is through an

epoxy resin surface and is enhanced by a water film supplied by an automated

sprinkler system. Firm contact is ensured through spring-loaded transducer

housings. Two serially mounted linear variable-displacement transformers (LVDT)

measure the diameter of the core (plus liner). A hydraulic piston system displaces

the transducers by several millimeters at the beginning and end of a core section

log to prevent the end caps from catching on to the transducers.

A 500-kHz pulse (2-µs wave period; 120 V) is produced at a repetition rate of

kHz. The pulse is sent to the transmitter transducer, which generates an ultra

compressional pulse at about 500 kHz. Pulse timing is measured with a resol

of 50 ns. The P-wave propagates through the core, is received by the receiver

transducer, and is amplified by an automatic gain control amplifier to produce

received signal. A delay pulse is generated after the transmit pulse (Figure 6

The delay time must be set (thumbwheel control) to a few microseconds less

the arrival of the signal. A 20-µs gate pulse follows the delay pulse; during thi

period a peak detector senses the peak voltage of the received signal after ga

control. A threshold detector is used for automatic peak detection: it is set low

when a preset fraction of the peak level (the threshold level) is crossed. In add

a zero-crossing detector detects all zero voltage crossings. A count pulse, wh

displayed on the instrument unit in microseconds, is generated at a time


e

e

tion

Use e

t (or the s).

corresponding to the first zero crossing after the first low-threshold event. The

resulting pulse detection delay is one wavelength (2 µs) if the the first peak is

positive and 1.5 wavelengths (3 µs) if the first peak is negative, because the

threshold is always detected on the negative signal. By detecting the travel tim

using a zero-crossing technique, the travel time recorded is independent of th

signal amplitude.

Figure 6—1 Schematic diagram of pulse timing and threshold peak detec(modified after GEOTEK manual).

CALIBRATION

Pulse Detection Pulse detection settings are checked by ODP personnel on a regular basis and

should not require any adjustment by the user. It may become necessary to adjust

the pulse from time to time (e.g., when equipment is replaced or materials of

different geometry are measured). The oscilloscope normally connected for the

user to monitor the received signal can be used for adjusting pulse timing and

threshold detection. The following procedure is modified after the GEOTEK

manual:

1. Place a water core (large signal) between the transducers. Ensure a good (wet) coupling. The “Level” indicator should be high. A clear received pulse should be visible on channel 1 and the delay pulse on channel 2.the thumbwheel to adjust the delay time such that it ends just prior to thstart of the received signal (approximately 35 µs).

2. Check that the count pulse occurs at the first zero-crossing after the firssecond, if wired in the opposite sense) negative excursion. If this is notcase, the threshold voltage level requires adjustment (procedure follow

Transmitter pulse (2 µs)

Delay pulse (0-999.9 µs)Gate pulse (0-999.9 µs)

Oscilloscope trigger

Threshold level

Threshold detector

Zero-crossing detector

Received signal

Count pulse

First zero crossing after first threshold interval

tpulse


irst

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3. Remove the water core and observe the signal through air (small signal; “Level” indicator low). Check again that the count pulse occurs at the fzero-crossing after the first (or second, if wired in the opposite sense) negative excursion. If this is not the case, the threshold voltage level requires adjustment (procedure follows).

4. Check that the travel time shown on the LCD is in approximate agreemwith the travel time shown on the oscilloscope.

If the threshold level requires adjustment:

1. The purpose is to ensure that that the threshold operating level Vop consistently picks first negative excursions over as wide an amplitude raas possible.

2. Place the water core (large signal) between the transducers. Adjust “Sehigh” so that the threshold operates just on the first (or second, if wiredthe opposite sense) negative excursion.

3. With a very small signal (through air with the transducers at their closesposition), adjust the threshold operating voltage using “Set low” such ththe threshold operates above the noise level but detects the first (or secif wired in the opposite sense) real negative excursion.

4. Repeat this procedure until the threshold operates correctly over the enrange of signal levels.

Pulse Time The pulse time is a time constant included in the total time measured as a res

the threshold peak detection procedure used. This constant may not be appa

with peak detection or calibration procedures different from those described h

ODP subtracts this constant from raw measurements of time because (1) it all

more precise monitoring of system performance (pulse time and “hardware de

discussed in the following section) and (2) it renders measured time values th

independent of a particular peak detection procedure.The constant tpulse is

therefore subtracted from the raw measurement of time t0 so that

t’0 = t0 – tpulse. (5)

The important thing to note is that the pulse time value changes depending on

wiring of the system. If the first received peak voltage is positive (Figure 6—1)

pulse time will be one wavelength, or 2 µs, for the 500 kHz transducers. Howe

if the wiring is in the opposite direction, as was the case for the ODP system a

least for some time, the pulse time is 1.5 wavelength, or 3 µs, because the thre

detection is always on the negative signal.

Transducer Displacement and Traveltime Delay

These two calibrations are performed simultaneously in one procedure (Figur

2). They should be executed once per leg on a routine basis. They should als

performed when changes or replacement of equipment have occurred, transd

surfaces have experienced extraordinary wear, or if other problems are suspe

Variation in the thickness of the sediment-filled core liner (d’core) is measured

using an LVDT connected to the spring-loaded transducer housings. Displace

measured in volts must be calibrated to give millimeters. At least three of the

available standard acrylic cylinders are measured. A linear least-squares fit to

points defined by the voltage readings (x-axis) and the known standard thickne


itry

s

is

l

y

tions

ss

in millimeters (y-axis) yields the linear coefficients md1 (mm/V) and md0 (mm).

Then, for any calibration standard or core measurement, respectively:

dcal = md0 + md1V0 (6)

d’core = md0 + md1V0 , (7)

where V0 is the voltage reading.

As previously mentioned, the total travel time (t0) measured between the

transducers includes three types of “delays”:

• delay related to the peak detection procedure (tpulse; see “Pulse Time” section),

• transit time through the core liner, if applicable (tliner; see “Liner Correction” section), and

• undifferentiable delay related to transducer faces and electronic circu(tdelay), which is determined with this procedure.

Although it is not necessary for the routine logging of sediment cores, ODP

differentiates between these types of delay because it allows for more rigorou

system monitoring and more flexibility in measurements. The constant tpulse is

subtracted from the raw measurement of time t0 so that

tcal = t0 – tpulse. (8)

The “hardware delay” of tdelay is then determined from another least-squares

regression. Here, the x-axis is defined by the dcal values of the standards

determined previously, and the y-axis is tcal. The linear coefficient, m1 (µs/mm), is

the inverse of the velocity of the standards (1/vstandard), and the intercept, m0 (µs),

is tdelay (Figure 6—2). Thus, the corrected transit time through a core is

t’core = t0 – tpulse – tdelay. (9)

If no core liner correction must be applied (i.e., if the material to be measured

directly in contact with the transducers), the velocity is calculated as

v’core = d’core / t’core. (10)

Liner correction In most cases (i.e., when logging whole cores in core liners), measured trave

distance and time must be corrected for twice the liner thickness. The liner

calibration is a measurement of thickness and transit time through core liner

material and is performed by ODP personnel. The liner correction is applied b

default (unless disabled by the user), using a constant liner thickness, dliner, and

sonic velocity for the liner material, vliner:

dcore = d’core – 2dliner (11)

tcore = t’core – 2dliner/vliner (12)

vcore = dcore / tcore. (13)

At present, we have no means of routinely measuring and correcting for varia

in liner wall thickness during logging. Vendor specifications for the wall thickne

are 5.64 to 4.70 mm, and we use 5.17 mm, or 2dliner = 1.03 cm.


ea-ion

own

d

Figure 6—2 Schematic of PWL calibration. A. Physical standard used. B. Msurement geometery. C. and D. Calibration principle. E. Application of calibratto core measurement.

PERFORMANCE

Precision Measurements on standard materials (e.g., water, acrylic calibration standards) are

repeatable within ±1 km/s. (Systematic evaluation required.)

Accuracy Accuracy can be evaluated by measuring pure water at varying and exactly kn

temperatures. Past experience shows that for a properly calibrated system an

good acoustic coupling, the disagreement with published v(T) values is less than ±2

km/s. (Systematic evaluation needed.)

MEASUREMENT

P-wave velocity is logged downcore automatically.

dcal (mm)

tcal (µs)

m0 = tdelay(µs)

m1 = vcal-1

(µs/mm)

Potential (V)

dcal (mm)

mV0 (mm)

mV1 (mm/V)

V = potentials from transducer LVDTdcal = known standard thicknesses

dcal = known standard thicknessestcal = t0 - tpulse, (tpulse = λ = 2 µs)

Acrylic cylinders

d1d2 d3

S1 S3S2

Core liner

Ultrasonic transducer pairand pneumatic caliper

dcore

d’core

A

B D

C

E

P-WAVE LOGGER (FULL-CORE)

• dliner and vliner are determined separately• option v’core: no liner correction (e.g., direct rock measurements)

v’core = d’core

= V0 × md1 + md0

t’core t0 - tpulse - tdelayvcore =

dcore =

d’core - 2dlinertcore t’core - 2dliner/vliner


DATA SPECIFICATIONS

Database Model

Notes: PWL Ctrl 1 are control measurements run the same way as a core section. PWL Ctrl 3 are control measurements from a standard mounted on the core boat (pure water).

Standard Queries

Table 6—1 PWL Database Model.

PWL section PWL control 1 PWL control 3 PWL calibration

pwl_id [PK1] pwl_ctrl_1_id [PK1] pwl_ctrl_3_id [PK1] pwl_calibration_id [PK1]section_id run_number run_number calibration_date_time

run_number run_date_time run_date_time run_numberrun_date_time core_status req_daqs_per_sample system_idcore_status liner_status pwl_calibration_id req_daqs_per_sample

liner_status liner_correction standard_id acoustic_signal_thresholdliner_correction requested_daq_interval acoustic_signal_threshold pwl_frequency

liner_standard_id req_daqs_per_sample core temperature pulse_time_correctionrequested_daq_interval pwl_calibration_id core_status separation_m0req_daqs_per_sample standard_id liner_status separation_m1

pwl_calibration_id acoustic_signal_threshold meas_separation_mean separation_mseacoustic_signal_threshold core temperature meas_separation_sd delay_m0

core temperature standard_liner_id meas_time_mean delay_1_m1mst_pwl_ctrl_3_id meas_time_sd delay_mse

acoustic_signal_mean commentsattempted_daqs

PWL section data PWL control 1 data valid_daqs PWL calibration data

pwl_id [PK1] [FK] pwl_ctrl_1_id [PK1] [FK] liner_thickness pwl_calibration_id [PK1] [FK]mst_top_interval [PK2] mst_top_interval [PK2] standard_liner_id standard_id [PK2] [FK]

mst_bottom_interval mst_bottom_interval mst_top_intervalmeas_separation_mean meas_separation_mean mst_bottom_intervalmeas_separation_sd meas_separation_sd standard_length

meas_time_mean meas_time_mean meas_separation_meanmeas_time_sd meas_time_sd meas_separation_sd

acoustic_signal_mean acoustic_signal_mean meas_time_meanattempted_daqs attempted_daqs meas_time_sdvalid_daqs valid_daqs acoustic_signal_mean

liner_thickness liner_thickness attempted_daqsliner_standard_id valid_daqs

Table 6—2 PWL report.

Short description Description DatabaseA: resultsSample ID ODP standard sample designation Link through [PWL Section] section_idDepth User-selected depth type Link through [PWL Section] section_idVelocity Calculated P-wave velocity = ([PWL Section Data] meas_separation_mean

- 2* [PWL Section Data] liner_thickness)/ ([PWL Section Data] meas_time_mean- {2* [PWL Section Data] liner_thickness/ [PP Std Data] liner_velocity}- [PWL Calibration] delay_m0)

B (optional): Parameteres and measurementsRun Run number [PWL Section] run_numberDate/Time Run date/time [PWL Section] run_date_timeCore Status HALF or FULL [PWL Section] core_statusLiner Status NONE, HALF or FULL [PWL Section] liner_status


Liner correction Liner correction (Yes/No) [PWL Section] liner_correctionReq. Interval User-defined sampling interval (cm) [PWL Section] requested_daq_intervalReq. Sample Requested DAQS per sample [PWL Section] requested_daqs_per_sampleSignal Acoustic signal threshold [PWL Section] acoustic_signal_thresholdCore Temp Core temperature [PWL Section] core_temperatureSep. Mean Mean of transducer separation [PWL Section Data] meas_separation_meanSep. S.D. Standard deviation of transducer separation [PWL Section Data] meas_separation_sdTime Mean Mean of transit tme [PWL Section Data] meas_time_meanTime std. dev. Standard deviation of transit time [PWL Section Data] meas_time_sdSignal Mean of acoustic signal [PWL Section Data] acoustic_signal_mean Attempted DAQS Attempted numnber of data acquisitions [PWL Section Data] attempted_daqsValid DAQS Valid number ofdata acquisitions [PWL Section Data] valid_daqsLiner Thickness Liner thickness (entered manually) [PWL Section Data] liner_thicknessStandard name Standard name [Phys. Properties Std.] standard_nameStandard Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Cal. Date/Time Calibration date/time [PWL Calibration] calibration_date_timeCal. Separ. m0 Intercept of transducer separation calibration [PWL Calibration] separation_m0Cal. Separ. m1 Slope of transducer separation calibration [PWL Calibration] separation_m1Cal. Separ. mse Mean squared errorof transducer separation cal. [PWL Calibration] separation_mseCal. Time m0 Intercept of transit time calibration [PWL Calibration] delay_m0Cal. Time m1 Slopeof transit time calibration [PWL Calibration] delay_m1Cal. Time mse Mean squared error of transit time calibration [PWL Calibration] delay_mse

Table 6—2 PWL report.

Table 6—3 PWL control 1 measurements (to be implemented).

Short description Description DatabaseVelocity Calculated P-wave velocity = ([PWL Ctrl 1 Data] meas_separation_mean

- 2* [PWL Ctrl 1 Data] liner_thickness)/ ([PWL Ctrl 1 Data] meas_time_mean- {2* [PWL Ctrl 1 Data] liner_thickness/ [PP Std Data] liner_velocity}- [PWL Calibration] delay_m0)

Run Run number [PWL Ctrl 1] run_numberDate/Time Run date/time [PWL Ctrl 1] run_date_timeCore Status HALF or FULL [PWL Ctrl 1] core_statusLiner Status NONE, HALF or FULL [PWL Ctrl 1] liner_status Liner Corr. Liner correction (Yes/No) [PWL Ctrl 1] liner_correctionReq. Interval User-defined sampling interval (cm) [PWL Ctrl 1] requested_daq_intervalReq. Sample User-defined DAQs per sample [PWL Ctrl 1] requested_daqs_per_sampleSignal Acoustic signal threshold [PWL Ctrl 1] acoustic_signal_thresholdCore Temp Core temperature [PWL Ctrl 1] core_temperatureInterval Interval top [PWL Ctrl 1 Data] mst_top_intervalSep. Mean Separation mean [PWL Ctrl 1 Data] meas_separation_meanSep. S.D. Separation standard deviation [PWL Ctrl 1 Data] meas_separation_sdTime Mean Time mean [PWL Ctrl 1 Data] meas_time_meanTime S.D. Time standard deviation [PWL Ctrl 1 Data] meas_time_sdSignal Acoustic signal mean [PWL Ctrl 1 Data] acoustin_signal_meanDaqs Attempt Attempted data acquisitions [PWL Ctrl 1 Data] attempted_daqsDaqs Valid Valid data acquisitions [PWL Ctrl 1 Data] valid_daqsLiner Thick Liner thickness (entered manually) [PWL Ctrl 1 Data] liner_thicknessStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Cal. Date/Time Cal. date/time [PWL Calibration] calibration_date_timeCal. Separ. m0 Cal. separation intercept m0 [PWL Calibration] separation_m0

Cal. Separ. m1 Cal. separation slope m1 [PWL Calibration] separation_m1


Cal. Separ. mse Cal. separation mean squared error [PWL Calibration] separation_mseCal. Time m0 Cal. time intercept m0 [PWL Calibration] delay_m0

Cal. Time m1 Cal. time slope m1 [PWL Calibration] delay_m1

Cal. Time mse Cal. time mean squared error [PWL Calibration] delay_mse



Short description Description DatabaseVelocity Calculated P-wave velocity = ([PWL Ctrl 3] meas_separation_mean

- 2* [PWL Ctrl 3] liner_thickness)/ ([PWL Ctrl 3] meas_time_mean- {2* [PWL Ctrl 3] liner_thickness/ [PP Std Data] liner_velocity}- [PWL Calibration] delay_m0)

Run Run number [PWL Ctrl 3] run_numberDate/Time Run date/time [PWL Ctrl 3] run_date_timeCore Status HALF or FULL [PWL Ctrl 3] core_statusLiner Status NONE, HALF or FULL [PWL Ctrl 3] liner_status Req. Interval User-defined sampling interval (cm) [PWL Ctrl 3] requested_daq_intervalReq. Sample User-defined DAQs per sample [PWL Ctrl 3] requested_daqs_per_sampleSignal Acoustic signal threshold [PWL Ctrl 3] acoustic_signal_thresholdCore Temp Core temperature [PWL Ctrl 3] core_temperatureSep. Mean Separation mean [PWL Ctrl 3] meas_separation_meanSep. S.D. Separation standard deviation [PWL Ctrl 3] meas_separation_sdTime Mean Time mean [PWL Ctrl 3] meas_time_meanTime S.D. Time standard deviation [PWL Ctrl 3] meas_time_sdSignal Acoustic signal mean [PWL Ctrl 3] acoustic_signal_meanDaqs Attempt Attempted data acquisitions [PWL Ctrl 3] attempted_daqsDaqs Valid Valid data acquisitions [PWL Ctrl 3] valid_daqsLiner Thick Liner thickness (entered manually) [PWL Ctrl 3] liner_thicknessStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_valueCal. Date/Time Cal. date/time [PWL Calibration] calibration_date_timeCal. Separ. m0 Cal. separation intercept m0 [PWL Calibration] separation_m0

Cal. Separ. m1 Cal. separation slope m1 [PWL Calibration] separation_m1

Cal. Separ. mse Cal. separation mean squared error [PWL Calibration] separation_mseCal. Time m0 Cal. time intercept m0 [PWL Calibration] delay_m0Cal. Time m1 Cal. time slope m1 [PWL Calibration] delay_m1

Cal. Time mse Cal. time mean squared error [PWL Calibration] delay_mse

Table 6—5 PWL calibration data (to be implemented).

Short description Description DatabaseDate/Time Cal. date/time [PWL Calibration] calibration_date_timeRun Cal. run number [PWL Calibration] run_numberReq. Sample User-defined DAQs per sample [PWL Calibration] requested_daqs_per_sampleSignal Acoustic signal threshold [PWL Calibration] acoustic_signal_thresholdFrequency PWL frequency [PWL Calibration] pwl_frequencyPulse Time Pulse time correction [PWL Calibration] pule_time_correctionSepar. m0 Cal. separation intercept m0 [PWL Calibration] separation_m0

Separ. m1 Cal. separation slope m1 [PWL Calibration] separation_m1

Separ. mse Cal. separation mean squared error [PWL Calibration] separation_mseTime m0 Cal. time intercept m0 [PWL Calibration] delay_m0Time m1 Cal. time slope m1 [PWL Calibration] delay_m1


6.3. PWS1 and PWS2 Insertion Probe Systems

EQUIPMENT

The current equipment has replaced the Digital Sonic Velocimeter (DSV)

developed at Dalhousie University and the Bedford Institute of Oceanography,

Nova Scotia, which was first used on ODP Leg 138 in 1991 (PWS1 and PWS2).

The principles remain the same, but hardware and computer control have been

improved significantly, and calibration and measurement procedures are simplified

at this upgraded station.

A vise-like frame holds the two transducers pairs. Their use is limited

approximately to the depth range of APC cores (i.e., a maximum depth of 50 to

300 mbsf), depending on the lithology.

A Tectronix signal generator, differential amplifier, and oscilloscope are used to

transmit and receive signals from all three transducer pairs and to digitize analog

waveform data. The instrument can record two voltage inputs with a minimum

sampling time of 5 ns and a digitizing signal to noise ratio of 50 dB.

An external digital thermometer is used to record core temperature. The values are

recorded in the database but are not used for shipboard reporting. Correction

algorithms must be researched, selected, and applied by the user.

CALIBRATION

Delay The distance d between the transducers is measured with calipers once every few

days (or even once per leg) and then assumed to be constant. The distance between

the probe surfaces does not exactly correspond to the distance between the

transducers. In addition, there is some electrical delay. The total “delay” tdelay is

Time mse Cal. time mean squared error [PWL Calibration] delay_mseComments Cal. comments [PWL Calibration] commentsTime mse Cal. time mean squared error [PWL Calibration] delay_mseInterval Interval top [PWL Calibration Data] mst_top_intervalStd. Length Length of standard [PWL Calibration Data] standard_lengthSepar. Mean Mean of transducer separation [PWL Calibration Data] separation_meanSepar. S.D. Standard deviation of transducer separation [PWL Calibration Data] separation_sdTime Mean Mean of transit time [PWL Calibration Data] time_meanTime S.D. Standard deviation of transit time [PWL Calibration Data] time_sdSignal Acoustic signal mean [PWL Calibration Data] acoustic_signal_meanDaqs Attempt Attempted data acquisitions [PWL Calibration Data] attempted_daqsDaqs Valid Valid data acquisitions [PWL Calibration Data] valid_daqsStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Table 6—5 PWL calibration data (to be implemented).


the

determined in this calibration by inserting the probes into a container filled with

distilled water of known temperature and therefore of known sound velocity vwater.

Because d is known, transit time in water, twater, can be computed as

twater = vwater × d. (14)

The measured total transit time, t, is:

t = twater + tdelay. (15)

Combining these equations, the delay can be expressed as:

tdelay = t – vwater × d. (16)

This calibration should be performed when the operator suspects a change in

distance between the probes because of heavy use or from other reasons.

PERFORMANCE

No performance evaluation data exist at present.

MEASUREMENT

An on-line guide is available at the neasurement station.

DATA SPECIFICATIONS

Data Mode

l

Notes: Control 1 measurements are run like core measurements, using a standard of known velocity.

Table 6—6 PWS1 and PWS2 data model.

PWS1/2 section PWS1/2 control 1 PWS1/2 calibration

pws_id [PK1] pws_ctrl_1_id [PK1] pws_calibration_id [PK1]section_id run_number calibration_date_timerun_num run_date_time run_num

run_date_time system_id system_idsystem_id standard_id water_temperaturepws_calibration_id pws_calibration_id standard_velocity

direction direction measured_timecore_temperature core_temperature delayraw_data_collected raw_data_collected freq

transducer_separation commentsmeasured_time

PWS1/2 section data

pws_id [PK1 [FK]pp_top_interval [PK2]measurement_no [PK3]

pp_bottom_intervaltransducer_separationmeasured_time

PWS1/2 raw data PWS1/2 control 1 raw datapws_id [PK1 [FK] pws_ctrl_1_id [PK1 [FK]

pp_top_interval [PK2] [FK] voltage [PK2]measurement_no [PK3] [FK] timevoltage [PK4]

time


Standard queries

Table 6—7 PWS1 or PWS2 report

Short description Description DatabaseA: ResultsSample ID ODP standard sample designation Link through [PWS1/2 Section] section_idDepth User-selected depth type Link through [PWS1/2 Section] section_idDirection Direction (PWS1 = z; PWS2 = y) [PWS1/2 Section] directionVelocity Calculated P-wave velocity = [PWS1/2 Section Data] transd_separation

/ ([PWS1/2 Section Data] measured_time- [PWS1/2 Calibration] delay)

B (optional): Measurement parameters and raw dataRun Run number [PWS1/2 Section] run_numberDate/Time Run date/time [PWS1/2 Section] run_date_timeCore Temperature Core temperature [PWS1/2 Section] core_temperatureRaw Data Raw data collected flag (yes/no) [PWS1/2 Section] raw_data_collectedMeas. No Measurement number [PWS1/2 Section Data] measurement_noSeparation Transducer separation [PWS1/2 Section Data] transducer_separationTime Measured time [PWS1/2 Section Data] measured_timeCal. Date/Time Cal. date/time [PWS1/2 Calibration] calibration_date_timeCal. Delay Cal. delay [PWS1/2 Calibration] delay

Table 6—8 PWS1 or PWS2 control 1 measurements (to be implemented).

Short description Description DatabaseVelocity Calculated P-wave velocity = [PWS1/2 Ctrl 1] transd_separation

/ ([PWS1/2 Ctrl 1] measured_time- [PWS1/2 Calibration] delay)

Run Run number [PWS1/2 Ctrl 1] run_numberDate/Time Run date/time [PWS1/2 Ctrl 1] run_date_timeDirection Direction (PWS1 = z; PWS2 = y) [PWS1/2 Ctrl 1] directionCore Temp Core temperature [PWS1/2 Ctrl 1] core_temperatureRaw Data Raw data collected flag (yes/no) [PWS1/2 Ctrl 1] raw_data_collectedSeparation Transducer separation [PWS1/2 Ctrl 1] transducer_separationTime Measured time [PWS1/2 Ctrl 1] measured_timeCal. Date/Time Cal. date/time [PWS1/2 Calibration] calibration_date_timeCal. Delay Cal. delay [PWS1/2 Calibration] delayStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Table 6—9 PWS1 or PWS2 calibration data (to be implemented).

Short description Description DatabaseDate/Time Cal. date/time [PWS1/2 Calibration] calibration_date_timeRun Cal. run number [PWS1/2 Calibration] run_numberWater Temperature Water temperature [PWS1/2 Calibration] water_temperatureVelocity Velocity of water at given temperature. [PWS1/2 Calibration] standard_velocityTime Measured time [PWS1/2 Calibration Data] measured_timeDelay Delay time derived from calibration. [PWS1/2 Calibration Data] delayFrequency Transducer frequency [PWS1/2 Calibration Data] freqComments Comments [PWS1/2 Calibration Data] commentsStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value


6.4. PWS3 Contact Probe System

EQUIPMENT

The current equipment has replaced the Hamilton Frame used on the ship since the

beginning of ODP. The principles remain the same but the hardware and computer

control have been improved significantly, and calibration and measurement

procedures are simplified at this upgraded station. The PWS3 is equipped with a

digital scale unit that allows rapid, precise determination of sample thickness and

enters the value into the database.

A pressure gauge is built into the monitor, and pressure is applied to the sample

when lowering the transducer onto the specimen or split core in the liner. In the

split core (logging) mode, the core section liner rests on the bottom transducer and

the upper transducer is lowered manually (procedure to be automated soon) onto

the core surface. In the specimen mode, the sample is placed directly between the

two transducers in the desired orientation.

CALIBRATION

Delay This calibration procedure is equivalent to the one employed for the P-wave logger

on the MST. Delay time tdelay is obtained by measuring a standard material of

different thicknesses d1, d2, . . . dn, and total transit times t1, t2, . . . tn. The

coefficient m0 (intercept) obtained from a linear least-squares fit represents the

delay tdelay. The inverse of the coefficient m1 (slope) of that regression is the

velocity of the standard material.

PERFORMANCE

No performance evaluation data exist at present.

Table 6—10 PWS1 or PWS2 wave form data (to be implemented).

Short description Description DatabaseSample ID ODP standard sample designationMeasurement Measurement number [PWS1/2 Raw Data] measurement_noVoltage Voltage [PWS1/2 Raw Data] voltageTime Time [PWS1/2 Raw Data] time

Table 6—11 PWS1 or PWS2 wave form control 1 data (to be implemented).

Short description Description DatabaseDate/Time Run date/time [PWS1/2 Ctrl 1] run_date_timeVoltage Voltage [PWS1/2 Ctrl 1 Raw Data] voltageTime Time [PWS1/2 Ctrl 1 Raw Data] time


MEASUREMENT

An on-line guide is available at the neasurement station.

DATA SPECIFICATIONS

Database Model

Notes: Control 1 measurements are run like core measurements, using a standard of known velocity.

Standard Queries

Table 6—12 PWS3 database model.

PWS3 section PWS3 control 1 PWS3 calibrationpws_id [PK1] pws_ctrl_1_id [PK1] pws_calibration_id [PK1]

section_id run_num calibration_date_timerun_num run_date_time run_numberrun_date_time system_id system_id

system_id standard_id delay_1_over_m1pws_calibration_id pws_calibration_id delay_m0direction direction delay_mse

core_temperature core_temperature freqliner_correction standard_liner_id commentsraw_data_collected raw_data_collected

standard_liner_id transducer_separationmeasured_time

PWS3 section data contact_pressure PWS3 calibration data

pws_id [PK1] [FK] liner_thickness pws_calibration_id [PK1] [FK]pp_top_interval [PK2] standard_id [PK2] [FK]measurement_no [PK3] transducer_separation

pp_bottom_interval measured_timetransducer_separation contact_pressuremeasured_time

contact_pressureliner_thickness

PWS3 raw data PWS3 control 1 raw datapws_id [PK1] [FK] pws_ctrl_1_id [PK1] [FK]pp_top_interval [PK2] [FK] voltage [PK2]

measurement_no [PK3] [FK] timevoltage [PK4]time

Table 6—13 PWS3 report.

Short description Description DatabaseA: ResultsSample ID ODP standard sample designation Link through [PWS3 Section] section_idDepth User-selected depth type Link through [PWS3 Section] section_idVelocity IF (liner_correction = TRUE) = ([PWS3 Section Data] transducer_separation

- [PWS3 Section Data] liner_thickness)/ ([PWS3 Section Data] measured_time- [PWS3 Section Data] liner_thickness/ [PP Std Data] liner_velocity}- [PWS3 Calibration] delay_m0)

Velocity IF (liner_correction =FALSE) = ([PWS3 Section Data] transducer_separation/ ([PWS3 Section Data] measured_time


- [PWS3 Calibration] delay_m0)B (optional): Measurement parameters and raw dataRun Run number [PWS3 Section] run_numberDate/Time Run date/time [PWS3 Section] run_date_timeDirection Direction (PWS1 = z; PWS2 = y) [PWS3 Section] directionCore Temp Core temperature [PWS3 Section] core_temperatureLiner Corr. Liner correction required (yes/no) [PWS3 Section] liner_correctionRaw Data Raw data collected flag (yes/no) [PWS3 Section] raw_data_collectedStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Meas. No Measurement number [PWS3 Section Data] measurement_noSeparation Transducer separation [PWS3 Section Data] transducer_separationTime Measured time [PWS3 Section Data] measured_timePressure Contact pressure applied [PWS3 Section Data] contact pressureLiner Thick Liner thickness [PWS3 Section Data] liner thicknessCal. Date/Time Cal. date/time [PWS3 Calibration] calibration_date_timeCal. m0 Cal. time intercept m0 [PWS3 Calibration] delay_m0

Cal. 1/m1 Cal. time inverse of slope 1/m1 [PWS3 Calibration] delay_1_over_m1

Cal. Time mse Cal. time mean squared error [PWS3 Calibration] delay_mse

Table 6—13 PWS3 report.

Table 6—14 PWS3 control 1 measurements (to be implemented).

Short description Description DatabaseVelocity IF (liner_correction = TRUE) = ([PWS3 Ctrl 1] transducer_separation

- [PWS3 Ctrl 1] liner_thickness)/ ([PWS3 Ctrl 1] measured_time- [PWS3 Ctrl 1] liner_thickness/ [PP Std Data] liner_velocity}- [PWS3 Calibration] delay_m0)

Velocity IF (liner_correction =FALSE) = ([PWS3 Ctrl 1] transducer_separation/ ([PWS3 Ctrl 1] measured_time- [PWS3 Calibration] delay_m0)

Run Run number [PWS3 Ctrl 1] run_numberDate/Time Run date/time [PWS3 Ctrl 1] run_date_timeDirection Direction (PWS1 = z; PWS2 = y) [PWS3 Ctrl 1] directionCore Temperature Core temperature [PWS3 Ctrl 1] core_temperatureLiner Correction Liner correction required (yes/no) [PWS3 Ctrl 1] liner_correctionRaw Data Raw data collected flag (yes/no) [PWS3 Ctrl 1] raw_data_collectedSeparation Transducer separation [PWS3 Ctrl 1] transducer_separationTime Measured time [PWS3 Ctrl 1] measured_timePressure Contact pressure applied [PWS3 Section Data] contact pressureLiner Thick Liner thickness [PWS3 Section Data] liner thicknessCal. Date/Time Cal. date/time [PWS3 Calibration] calibration_date_timeCal. m0 Cal. time intercept m0 [PWS3 Calibration] delay_m0


Cal. Time mse Cal. time mean squared error [PWS3 Calibration] delay_mseStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Table 6—15 PWS3 calibration data (to be implemented).

Short description Description DatabaseDate/Time Cal. date/time [PWS3 Calibration] calibration_date_timeRun Cal. run number [PWS3 Calibration] run_numberCal. m0 Cal. time intercept m0 [PWS3 Calibration] delay_m0



Cal. Time mse Cal. time mean squared error [PWS3 Calibration] delay_mseFrequency Transducer frequency [PWS3 Calibration] freqComments Comments [PWS3 Calibration] commentsSeparation Transducer separation [PWS3 Calibration Data] transducer_separationTime Measured time [PWS3 Calibration Data] measured_timePressure Contact pressure [PWS3 Calibration Data] contact_pressureStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value

Table 6—15 PWS3 calibration data (to be implemented).

Table 6—16 PWS3 wave form data (to be implemented).

Column Head Description DatabaseSample ID ODP standard sample designationMeasurement Measurement number [PWS3 Raw Data] measurement_noVoltage Voltage [PWS3 Raw Data] voltageTime Time [PWS3 Raw Data] time

Table 6—17 PWS3 wave form control 1 data (to be implemented).

Short description Description DatabaseDate/Time Run date/time [PWS3 Ctrl 1] run_date_timeVoltage Voltage [PWS3 Ctrl 1 Raw Data] voltageTime Time [PWS3 Ctrl 1 Raw Data] time


tic

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ro

,

7. REFLECTANCE SPECTROPHOTOMETRY AND COLORIMETRY

7.1. Principles

PHYSICAL BA CKG ROUND

Color is the human eye’s perception of reflected radiation in the visible region of

the electromagnetic spectrum (400–700 nm). It originates from electromagne

energy changes in electron orbitals, caused by the absorption of photons, in th

transition elements contained in the crystal structure of minerals (e.g., Burns,

1970).

One of the most objective ways to measure color is to use diffuse-reflected

spectrophotometry. Light reflected from the material is collected in an integration

sphere, normalized to the source light of the reflectance, and calibrated with t

measurement of a pure white standard (100% reflection) and a black box (ze

reflection) over the entire wavelength spectrum of visible light. For material

studies, near-ultraviolet (250–400 nm) and near-infrared (700–850 nm) have been

shown to be useful.

Reflectance spectra are related to color using established international conventions.

According to the Commission Internationale d'Eclairage (CIE) (1986) method

tristimulus values are derived from the color reflectance spectra as follows:

For 2° standard observer (CIE, 1931) and 400 ≤ λ ≤ 700 (nm): (1)

X = K ∑ S(λ) x(λ) R(λ), (2)

Y = K ∑ S(λ) y(λ) R(λ), (3)

Z = K ∑ S(λ) z(λ) R(λ), (4)

K = 100/ ∑ S(λ) y(λ). (5)

For 10° standard observer (CIE 1964) and 400 ≤ λ ≤ 700 (nm): (6)

X10 = K ∑ S(λ) x10(λ) R(λ), (7)

Y10 = K ∑ S(λ) y10(λ) R(λ), (8)

Z10 = K ∑ S(λ) z10(λ) R(λ), (9)

K = 100/ ∑ S(λ) y10(λ), (10)

where λ is the wavelength at a 10-nm pitch; S(λ) is the relative spectral power

distribution of the illuminant; x(λ), x10(λ), y(λ), y10(λ), z(λ), and z10(λ) are color-

matching functions; and R(λ) is the spectral reflectance of the specimen.


for

es the

zed

Several color spaces have been defined based on the tristimulus values X, Y, Z,

such as the Yxy, L*a*b* and its derivative L*C*H°, Lab, and L*u*v* systems. The

L*a*b* system is presented here in more detail, and its use is recommended

sediment and rock color analyses. It is far superior to and therefore supersed

Munsell color system traditionally used in earth science.

The L*a*b* Color System

The L*a*b* system is also referred to as the CIELAB system. It can be visuali

as a cylindrical coordinate system in which the axis of the cylinder is the lightness

variable L* , ranging from 0% to 100%, and the radii are the chromaticity variables

a* and b*. Variable a* is the green (negative) to red (positive) axis, and variable b*

is the blue (negative) to yellow (positive) axis.

The variables are defined as follows (CIE, 1986; related references: ASTM, 1985a;

ASTM, 1985b; ISO, 1984; DIN, 1980):

• If (X/Xn), (Y/Yn), (Z/Zn) > 0.008856:L* + 116(Y/Yn)1/3 - 16, (11)

a* = 500[(X/Xn)1/3 - (Y/Yn)1/3], (12)

b* = 200[(Y/Yn)1/3 - (Z/Zn)1/3]. (13)

• If (X/Xn), (Y/Yn), (Z/Zn) < 0.008856:L* + 903.29(Y/Yn), (14)

a* = 500{7.787[(X/Xn) + 16/116] - 7.787[(Y/Yn) + 16/116]}, (15)

b* = 200{7.787[(Y/Yn) + 16/116] - 7.787[(Z/Zn) + 16/116]}, (16)

where X, Y, and Z are tristimulus values for the 2° or 10° observer of the specimen,

and Xn, Yn, and Zn are tristimulus values for the 2° or 10° observer of a perfect

reflecting diffuser.

Derived Parameters Various standard parameters can be calculated from the L*a*b* system variables.

If L* , a*, b* are the specimen data, and L* t, a*t, b*t are the target color data,

differences are defined as:

∆L* = L* - L* t' (17)

∆a* = a* - a*t' (18)

∆b* = b* - b*t , (19)

and the color difference between two points is

∆E*ab = [(∆L* )2 + (∆a*)2 + (∆b*)2]1/2. (20)

In the L*C*H° system, the metric chroma parameter, C*, and the metric hue-angle,

H°, are defined as:

C* = [(a*)2 + (b*)2]1/2 (21)

H° = tan–1(b*/a*) (degrees), 0° ≤ H° ≤ 360°. (22)

Differences between specimen and target color are

∆L* = L* - L* t' (23)

∆C* = C* - C* t, = [(∆a*)2 + (∆b*)2]1/2 - [(∆at*)2 + (∆bt*)

2]1/2, (24)

and the metric hue difference, ∆H*, between two points is defined as

∆H* = [(∆E*ab)2 - (∆L* )2 + (∆C*)2]1/2 = [(∆a*)2 - (∆b*)2 + (∆C*)2]1/2 (25)


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Munsell Colors There is no international standard for converting tristimulus values to Munsell

HVC (hue, value, and chroma) notation. Tables have been established to relate

Munsell colors to Yxy data, and the L*C*H° parameters can be related to Muns

colors using such tables. Interpolation is used if necessary to approximate va

not available in the tables. The use of Munsell colors has many disadvantage

different conditions of illumination and viewing angle and variability in human

eye’s response and sensitivity. It is a procedure that is neither highly objective

can the data be analyzed quantitatively. Munsell color classification should

therefore not be used.


Measuring Color of Split-Core Surfaces

Measuring split-core surfaces poses some potential problems affecting the

measurement:

• moisture, or uncontrolled drying of the material,

• surface roughness,

• particle size,

• oxidation, and

• use of protective plastic wrap.

Cores are split with a wire if they are soft enough, and with a saw once wire cu

is no longer effective. The problem is that wire cuts of very soft sediment and q

stiff sediment from farther downhole may create different surface roughnesse

This can affect color reflectance significantly. The problem is mostly solved by

“cleaning” the core surface with a sharp edge such as a knife blade or glass s

The process is tedious and time-consuming, however.

Moisture content affects color reflectance significantly. Automated shipboard

measurements at very small sampling intervals require that the material is

measured at whatever moisture content is present. Both uncontrolled drying a

oxidation that begin as soon as the core is split “lighten” material characterize

organic matter or iron compounds. The oxidation of iron compounds also tend

increase reflectance, particularly at the red end of the spectrum. Unfortunatel

cannot be assumed that the changes caused by drying or oxidation are unifor

over the entire spectrum of visible light. Absolute spectral characteristics for

sediment and rock colors must therefore be established with dry powders. The

no practical solution to the discrepancy between dry and wet material

measurement, and it must be accepted as the inherent analytical error. To kee

variation as systematic as possible, it is a good practice to take color measure

at constant periods after the core has been split, such as about 1 hr.

Nagao and Nakashima (1991) examined the difference between wet and dry

measurements of marine cores in L*a*b* color space. They found that for typi

pelagic sediment of the uppermost meter below seafloor, L* is up to 20% higher in

dried specimens and a* and b* are higher by approximately 1. Fortunately,

however, color parameter profiles, such as L*a*b*, do not change their charac

as a function of moisture content (Nagao and Nakashima, 1992). These autho

also examined and discussed the effects of grain size, addition of water to dri

samples, and oxidation. They concluded that L* values are controlled mainly by


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water content with a small grain-size and homogenization effect; a* and b* values

are controlled by water content, oxidation of greenish materials, and grain size.

Pore-water composition may have an effect, as shown by the difference in

measurements of samples remolded with pure water from original values.

Balsam et al. (1997) performed factor analyses on spectral data from Leg 155

cores obtained with the shipboard Minolta CM-2002 on wet split cores and with a

Perkin-Elmer Lambda 6 on corresponding dried and powdered samples. Shipboard

data yield a four-factor solution to explain 99% of the cumulative variance,

whereas shore-based data produced at least seven interpretable factors to explain

99% of the cumulative variance. The four factors identified in the ship data are

also present in the shore data. This and other statistics indicate that measurements

from dried sediments are significantly more sensitive to subtle variations in the

data set than measurements on wet cores, which appear to contain less

information. However, differences in instrumentation and the fact that wet cores

were measured through a film of Saran Wrap may also have affected the sensitivity

of these measurements.

USE OF COLOR DATA

The two most common uses of color reflectance data are (1) color parameters such

as L*a*b* provide detailed time series of relative changes in the composition of

the bulk material and are frequently used to correlate sections from core to core or

hole to hole and to analyze the cyclicity of lithologic changes; and (2) spectral data

can be used to estimate the abundance of certain compounds. The first type of

investigation, referred to as colorimetry, is simple and straightforward. Spectral

analysis of visual light spectra (VIS) provides semiquantitative estimates of

hematite and goethite with a sensitivity that is at least 1 order of magnitude better

than from XRD analysis (Deaton and Balsam, 1991). Carbonate, opal, organic

matter, chlorite, and some combinations of clay minerals can also be detected,

although near-ultraviolet (NUV) and near-infrared (NIR) data (which cannot be

obtained with the Minolta CM-2002) should or must be included for at least some

of these analysis (Balsam and Deaton, 1991, 1996; Nakashima et al., 1992; Balsam

and Wolhart, 1993; Balsam and Otto-Bliesner, 1995).

The spectra of marine sediments are typically smooth and show small peaks and

valleys. A common statistical method to enhance relative changes is to use the first

derivative of the measurement intervals. This “boundary hunting” method reve

the maximum rate of change in the original spectrum or the shoulders of the

original absorption peaks, which occur at characteristic wavelengths. The add

advantage of using first derivatives is that problems inherent in core surface

measurement (moisture, oxidation, use of plastic wrap, surface texture and g

size, etc.) or the difference between measurements using different instrumen

(Balsam et al., 1997) are minimized. Yet, several effects must be considered

using first derivatives for quantitative estimation: matrix composition has a sev

effect on peak height and the exact wavelength of a peak depends on the

concentration of a component (Deaton and Balsam, 1991; Balsam and Wolh

1993) and the grain size of a component may influence the reflectivity and

absolute band intensity (Gaffey, 1986).


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Results from many ODP legs have shown that the correlation between L* and

carbonate content is usually the best and most obvious one and also is similar at

many sites. Parameters a* and b* do not seem to yield much characteristic

information or cyclic variations. They are, however, sensitive to clay mineralogy,

nannofossil content, etc. Ratios such as a*/b*, or parameters of the L*C*H°

system, may distinguish these variations better than a* and b*.

7.2. Minolta CM-2002 System

EQUIPMENT

The Minolta Photospectrometer CM-2002 is a compact, hand-held instrumen

measuring the spectral reflectance of surfaces with a diameter of more than 8

The instrument combines measurement, data processing, and display function

single unit. Ultracompact spectral sensors developed by Minolta, hybrid IC an

circuitry, and a 32-bit, 16-MHz microcomputer provide high-speed, high-accur

measurements of spectral reflectance from 400 to 700 nm. To ensure accurac

CM-2002 uses a double-beam feedback system, monitoring the illumination on

specimen at the time of measurement and automatically compensating for an

changes in the intensity or spectral distribution of the light.

Objects are illuminated diffusely with a pulsed xenon arc light and viewed at a

angle to the normal to the object’s surface (standard observer, Commission

Internationale d'Eclairage, CIE). The width of the viewing beam is 7.4°. This

geometry meets the specification for diffuse illumination and 0° viewing angle

(CIE, 1986) as well as the specification for diffuse illumination and 8° viewing

angle (ISO, 1984; DIN, 1980). In addition, the instrument’s geometry and des

allow for the specular component to be included (SCI setting) or to be exclude

(SCE setting). The SCE setting is the recommended mode of operation for

sediments in which the light reflected at a certain angle (angle of specular

reflection) is trapped and absorbed at the light trap position on the integration

sphere. Specular reflectance is perfect reflectance, or glare, and including it

provides a better estimate of color as seen by the human eye. However, glare

not contribute to the spectrum, and Minolta recommends the SCE setting for

general purposes (the SCI setting is useful for color mixing or computer color

matching). Also, the SCE setting is favored for comparison with laboratory da

based only on diffuse light (Balsam et al., 1997).

Light reflected from the surface of the specimen at an angle of 8° to the norm

enters the optical fiber cable and is transmitted to spectral sensor 1. At the sa

time, the light inside the integration sphere illuminating the specimen is

transmitted to spectral sensor 2. The light from each optical fiber cable is divid

by wavelength at a 10-nm pitch (400–700 nm) before striking the segments o

silicon photodiode array of the spectral sensors. The sensors convert the ligh

electrical currents proportional to the intensity of the light. The currents are th

passed to the analog control circuits and converted into digital signals.


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Measurements can be calculated based on either the 2° or 10° standard obse

and any of 11 illuminants (CIE standard illuminants A, C, D50, and D65 and

fluorescent illuminants F2, F6, F7, F8, F10, F11, and F12). Measurement resu

can then be displayed in a variety of ways: graphically as spectral reflectance

color difference, or as numerical absolute and/or difference values for XYZ, Y

L*a*b*, L*C*H°, Hunter Lab, or L*u*v* color spaces; metamerism index,

Munsell notation; CMC (2:1) or (1:1); FMC-2; whiteness index (ASTM E 313 o

CIE); or yellowness index (ASTM D 1925 or ASTM E 313). The standard outp

for the ODP database includes the full, 31-channel spectra, X, Y, Z; L*a*b*

parameters; and Munsell notation.

Do not expose this instrument to heat > 55°C (e.g., lights, direct sunlight) or

magnetic fields (e.g., speakers).

CALIBRATION

Loading Calibration Data

A white ceramic attachment (cap) is supplied with the Minolta CM-2002 as a

standard accessory. The cap is a transfer calibration standard that was factor

calibrated over 31 intervals of 10-nm length between 400 and 700 nm agains

primary standard consisting of pressed BaSO4 (ISO 7724/2) at the National

Physical Laboratory in the United Kingdom. The calibration coefficients from t

primary calibration are supplied with the Minolta CM-2002 memory card. Whe

new camera or standard is purchased, the calibration data must be loaded by

installing the new memory card. The data remain in memory until they are

changed.

The life time of the lithium battery on the memory card is approximately 2 yea

Zero Calibration Zero calibration is performed to compensate for the effects of stray light owing

the flare characteristics of the optical system. Flare characteristics may chang

over time because of dust, stains, etc., in the optical system. In addition, zero

calibration may also eliminate variations resulting from changes in ambient or

internal temperature. At the time of shipment, zero calibration data measured

Minolta are stored in an EEPROM in the CM-2002. These data should be upd

routinely.

The calibration is performed by removing the protective cap or any other

attachment from the aperture and aiming the aperture into the air so that no o

are within 1 m and no light source is aimed at. A zero-calibration box can also

used, but it is not available on the ship.

Zero calibration must be performed under the same conditions as the

measurements are taken (SCE setting, ambient temperature, etc.). Zero-calib

data will remain in memory even if the power is switched off. If a zero calibrat

is performed it must be followed immediately by a white calibration.

White Calibration The white calibration sets the maximum reflectance to 100%. Each time the

camera is switched on, or after a zero calibration has been performed, white

calibration must be performed before measurements are taken. In addition,

changes in ambient or internal temperature may affect the accuracy of the


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measurement. The white calibration should therefore be performed regularly,

meaning every few hours if the instrument is used around the clock.

White calibration must be performed under the same conditions as the

measurements are taken (SCE setting, ambient temperature, etc.). The calibration

data for the cap have been obtained at temperatures of 23° ± 1°C. For highes

accuracy, the instrument should always be operated at this temperature.

Do not apply plastic wrap to the standard. Although this may make relative se

on the ship, such a calibration would not allow comparison with correspondin

data obtained in other laboratories. Always cover the standard with a protectiv

cap when not in use because the color may change, even in normal room ligh

The calibration standard can be cleaned with lens-cleaning fluid. Wipe the sur

clean with a soft cloth moistened with water and let dry before use. If the whit

calibration cap becomes scratched or stained, a new standard must be purch

In this case, the new calibration data must be loaded (refer to the preceding

“Loading Calibration Data” section, and the manufacturer’s manual).

Calibration Procedure

Before measuring a new core:

1. Switch the instrument off and on. This brings you automatically to the calibration mode.

2. Remove any attachment from the aperture. Aim the aperture away fromlight sources and at least 1 m away from any object (zero-calibration boshould be available in the future).

3. Press ZERO CALIB. Wait until three measurements have been taken (tlight flashes, ~10 s). The CM-2002 automatically returns to calibration mode.

4. Attach the white calibration cap. Do not cover the white calibration padwith the plastic wrap because this reduces its validity as a factory-calibratransfer standard.

5. Press MEAS WHITE CALIB. Wait until three measurements have beentaken (three light flashes, ~10 s). The CM-2002 automatically returns toMENU mode.

6. Select DATA OUT mode from the menu. This sets up the system for measurements controlled by the external computer.

7. It is recommended that a control measurement be taken with the whitecalibration cap on.

8. Remove the calibration cap and start core measurement.

Automation of color measurements in the near future may allow semiautomat

calibration at the beginning of each core, and automatic control measuremen

would monitor drift at the beginning and the end of each section scan.

PERFORMANCE

Precision (repeatability)

Spectral reflectance: standard deviation within 0.1%.

Chromaticity value: ýE*ab within 0.03.


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Accuracy Accuracy depends strongly on the calibration routine. The error should be less than

1% if calibration is performed regularly. The calibration data for the white

calibration cap were obtained at a temperature of 23° ± 1°C. For the highest

accuracy, the instrument should always be operated at this temperature.

MEASUREMENT

At present, the Minolta CM-2002 is operated manually, using an external data

capture program. Thus, data quality depends largely on the operator.

1. Perform a zero calibration followed by a white calibration before measura new core (see procedure in “Calibration” section). Perform zero and wcalibrations at least once per shift (every 12 hr; see “Calibration” sectio

2. Use a consistent time lag after core splitting for the color measurementabout 1 hr is standard. Surface moisture variations from splitting and subsequent exposure of the split surface are largest during the first half to hour.

3. Cover the split-core with GLAD Cling Wrap crystal clear polyethylene, which transmits light uniformly over the spectrum of visible light and haminimal effect on the spectra (Balsam et al., 1997).

4. Do not use the optional granular-materials cover (part CM-A40).

5. Exclude the specular component (SCE setting). Although the specular component, which is essentially glare, provides a better estimate of coloseen by the human eye, it does not contribute to the spectrum reflectedsediment. Using the SCE setting should reduce the effect of varying moisture on the core surface.

6. Set the number of measurements per position. One measurement per position is sufficiently precise, but three measurements are better. Denssampling should not be compromised for multiple measurements.

7. Set the appropriate core identifier and sampling interval on the externacomputer program.

8. Take a control measurement using the white calibration attachment. Thcurrent program expects you to take a control measurement at the beginand the end of a core, which are written to the data file. (The future progwill write these measurements to a separate file.)

9. Set the photospectrometer gently on the split-core surface, and hold it orthogonal to the core surface.

10. Avoid measuring cracks because the measurement result will be useleswill degrade the value of the color profile.

11. Press the measurement button and wait until a flash occurs.

12. Set the instrument at the next interval; the program increments the prespecified interval automatically.

13. Take another control measurement when the core is measured.

14. Repeat steps 2 through 12.

15. Regularly clean the protective glass cover on the aperture.


DATA SPECIFICATIONS

“Spectralog” Output File

Spectralog is the original ODP data acquisition program for the Minolta CM-2002

which outputs the spectral measurements as well as scores of calculated

parameters. The list below shows the ODP-customized Spectralog output,

consisting of space-delimited records written to one file per hole. Column headers

are not written by that program. During past legs, the output has produced three

columns at the end of the file which are not defined.

The Spectralog program is being replaced by an updated version which will also

acquire the tristimulus values X, Y, Z. The database model is designed accordingly.

Table 7—1 Spectralog (expected) output.

Short description Description Output file designationLeg Leg [Spectralog 1-4] legSite Site [Spectralog 8-11] siteHole Hole [Spectralog 13] holeCore Core [Spectralog 15-17] coreCore type Core type [Spectralog 19] core_typeSection Section [Spectralog 21-22] section_or_stdTop Interval top (cm) [Spectralog 24-28] interval_topBottom Interval bottom (cm) [Spectralog 30-34] interval_bottom(Depth) Empty for depth [Spectralog 36-42]L* Calculated L* [Spectralog] l_stara* Calculated a* [Spectralog] a_starb* Calculated b* [Spectralog] b_starMunsell HVC Calculated Munsell hue-value/chroma [Spectralog] munsell_hvc395-405 395–405 nm bin [Spectralog] 395–405_nm405–415 405–415 nm bin [Spectralog] 405–415_nm415–425 415–425 nm bin [Spectralog] 415–425_nm... etc. etc.695–705 695–705 nm bin [Spectralog] 695-705_nm

Undefined valueUndefined valueUndefined value


Database Model

Standard Queries

Table 7—2 RSC database model

RSC Section RSC Control RSC Run RSC Calibrationsection_id [PK1] [FK] standard_id [PK1] [FK] leg [PK1] [FK] rsc_calib_date_time [PK1]

leg [PK2] [FK] leg [PK2] [FK] rsc_run_num [PK2] rsc_commentrsc_run_num [PK3] [FK] rsc_run_num [PK3] [FK] rsc_num_meas rsc_illumination_condition

rsc_run_date_time rsc_num_meas

rsc_calib_date_time rsc_observer_anglersc_reflectance_corr

RSC Run Data rsc_specular_statusleg [PK1] [FK] rsc_zero_calib_flagrsc_run_num [PK2] [FK] system_id

top_interval [PK3]bottom_interval

rsc_cielab_l_starrsc_cielab_a_starrsc_cielab_b_star

rsc_heightrsc_height_assumed_flag

rsc_munsell_hvcrsc_tristimulus_xrsc_tristimulus_y

rsc_tristimulus_zrsc_first_channel

rsc_last_channelrsc_channel_incrementrsc_spectra

Table 7—3 RSC report.

Short description Description DatabaseA: Colorimetry resultsSample ID ODP standard sample designation Link through [RSC Section] section_idDepth User-selected depth type Link through [RSC Section] section_idL* First L*a*b* parameter [RSC Run Data] rsc_cielab_l_stara* Second L*a*b* parameter [RSC Run Data] rsc_cielab_a_starb* Third L*a*b* parameter [RSC Run Data] rsc_cielab_b_starMunsell Munsell hue, value, chroma [RSC Run Data] rsc_munsell_hvcX Tristimulus value X [RSC Run Data] rsc_tristimulus_xY Tristimulus value Y [RSC Run Data] rsc_tristimulus_yZ Tristimulus value Z [RSC Run Data] rsc_tristimulus_zB (optional): Spectral resultsSpectrum String of 31 spectral reflectance values (% intensity) [RSC Run Data] rsc_spectraC (optional): Measurement parametersRun Run number on a leg [RSC Run] rsc_run_numberDate/time Run data and time [RSC Run] rsc_run_date_timeNo. of Meas. Number of measurements for each data point [RSC Run] rsc_num_measCal. date/time Date and time of last calibration [RSC Run] rsc_calib_date_timeHeight Distance between aperture and core surface [RSC Run Data] rsc_heightFirst lambda Wavelength of first channel [RSC Run Data] rsc_first_channelLast lambda Wavelength of last channel [RSC Run Data] rsc_last_channelIncrement lambda Wavelength increment between channels [RSC Run Data] rsc_channel_increment


Table 7—4 RSC control 1 data (to be implemented).

Short description Description DatabaseRun Run number on a leg [RSC Run] rsc_run_numberDate/time Run data and time [RSC Run] rsc_run_date_timeNo. of Meas. Number of measurements for each data point [RSC Run] rsc_num_measCal. date/time Date and time of last calibration [RSC Run] rsc_calib_date_timeHeight Distance between aperture and core surface [RSC Run Data] rsc_heightFirst lambda Wavelength of first channel [RSC Run Data] rsc_first_channelLast lambda Wavelength of last channel [RSC Run Data] rsc_last_channelIncrement lambda Wavelength increment between channels [RSC Run Data] rsc_channel_incrementL* First L*a*b* parameter [RSC Run Data] rsc_cielab_l_stara* Second L*a*b* parameter [RSC Run Data] rsc_cielab_a_starb* Third L*a*b* parameter [RSC Run Data] rsc_cielab_b_starMunsell Munsell hue, value, chroma [RSC Run Data] rsc_munsell_hvcX Tristimulus value X [RSC Run Data] rsc_tristimulus_xY Tristimulus value Y [RSC Run Data] rsc_tristimulus_yZ Tristimulus value Z [RSC Run Data] rsc_tristimulus_zSpectrum String of 31 spectral reflectance values (% intensity) [RSC Run Data] rsc_spectra

Table 7—5 RSC calibration data (to be implemented).

Short description Description DatabaseDate/time Calibration date and time [RSC Calib] rsc_calib_date_timeComments Number of measurements for each data point [RSC Calib] rsc_commentIllumination Illumination type/condition [RSC Calib] rsc_illumination_conditionNo. of Meas. Number of measurements averaged [RSC Calib] rsc_num_measObserver angle Observer angle [RSC Calib] rsc_observer_anglelCorrection Reflectance correction applied or not [RSC Calib] rsc_reflectance_corrSpecular Specular components measured or not [RSC Calib] rsc_specular_statusZero calib. Zero (black) calibration performed or not [RSC Calib] rsc_zero_calib_flag


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8. THERMAL CONDUCTIVITY

8.1. Principles

PHYSICAL BACKGROUND

The coefficient of thermal conductivity, k [W/(m·K)], is a measure of the rate q

(W) at which heat flows through a material. It is the coefficient of heat transfer

across a steady-state temperature difference (T2 – T1) over a distance (x2 – x1), or

q = k (∆T/∆x). (1)

Thermal conductivity can be measured by transient heating of a material with

known heating power generated from a source of known geometry and meas

the temperature change with time. The method assumes isotropic materials.

Theoretical discussion for measuring thermal conductivity with cylindrical sour

is found in Blackwell (1954), Carslaw and Jaeger (1959), De Vries et al. (1958

Von Herzen and Maxwell (1959), Kristiansen (1982), and Vacquier (1985).

For a full-space needle probe, the length L can be assumed to be infinite and the

problem is reduced to two dimensions. Given the resistance R of a looped wire in a

needle, the generated heat is

q = 2 i2 R / L, (2)

where R/L is the resistance of the needle per unit length. At any time t after heating

has started, the temperature T is related to the thermal conductivity k by

T = (q / 4πk) ln(t) + C, (3)

where q is the heat input per unit length and unit time and C is a constant. A simple

way of calculating the thermal conductivity coefficient k is picking T1 and T2 at

times t1 and t2, respectively, from the temperature versus time measurement cu

(see also ASTM, 1993):

ka(t) = q / 4π [ln(t2) - ln(t1)] / (T2 - T1). (4)

ka(t) is the apparent thermal conductivity because the true conductivity, k, is

approached only by a sufficiently large heating duration. This method assume

the measurement curve is linear and ignores the imperfections of the experim

expressed in the constant C.

In practice, the correct choice of a time interval is difficult. During the early sta

of heating, the source temperature is affected by the contact resistance betwe

source and the surrounding material. During the later stage of heating, bound

effects of the finite length of the source affect the measurement. The position o

optimum interval generally differs from measurement to measurement. The tw

systems presently available on the ship employ different procedures to select

time interval: the older Thermcon-85 system relies on operator judgment base

visual examination of the ln(t) vs. T plot; the newer TK04 system uses an


32

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and

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algorithm that automatically finds the optimal time interval (Erbas, 1985). More

information is provided about each in the following sections.


In situ thermal conductivity is a function of in situ temperature and pressure

conditions. Corrections may be applied to laboratory measurements on cores,

based on in situ information and theoretical and empirical relationships. Data in

the ODP database are not corrected for in situ conditions.

USE OF THERMAL CONDUCTIVITY

Thermal conductivity is an intrinsic material property for which the values depend

on the chemical composition, porosity, density, structure, and fabric of the material

(e.g., Jumikis, 1966). In marine geophysics, mainly thermal conductivity profiles

of sediment and rock sections are used, along with temperature measurements, to

determine heat flow. Heat flow is not only characteristic of the material, but an

indicator of type and age of ocean crust and fluid circulation processes at shallow

and great depths.

8.2. Thermcon-85 System

EQUIPMENT

The Thermcon-85 system consists of the following components:

• Thermcon-85 unit,

• calibrated needle probes,

• personal computer,

• TC-PC control and data reduction program, and

• calibration file for TC-PC.

The Thermcon-85 unit was purchased from Woods Hole Oceanographic

Institution. It is under the control of PROM-based programming, and an RS-2

serial interface is available. One to five needle probes can be connected to th

panel. An eight-channel multiplexer selects the appropriate input for each

measurement. See the Thermcon-85 manual for more details.

The needle probes are either assembled at ODP or purchased preassembled

either case, they contain factory-calibrated thermistors.

The TC-PC program was developed at ODP in 1991 using Quick Basic (v. 4.5)

runs on a PC clone. The following programs are involved:

• TCMENU: controls the overall data acquisition process;

• COLLECT: communicates with the Thermcon-85; performs drift studycollects raw data and writes raw data file; monitors “bad data conditio(warnings not written to data file);


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• PROCESS: allows selection of probe positions; allows for optional correction for temperature drift at drift study termination; allows selectiof “optimal” interval; reduces the raw data and calculates thermal conductivity; writes to a results file; and

• PROBES: used to enter thermistor calibration coefficients for new proand “secondary” probe calibration constants into the PROBES.DAT fi

The user normally runs TCMENU. Interaction with the COLLECT and PROCE

programs is accomplished via menu selection. The calibration data must be en

into the PROBES.DAT file when appropriate.

CALIBRATION

Power Supply, Digital Volt Meter, and Heater Current

Calibration must be periodically performed by an ODP Electronics Technician

Refer to the Thermcon-85 manual for details.

Needle Probe Resistance

The thermistors in each needle probe are calibrated at the factory over a rang

temperatures (usually 15° to 75°C) and fit to an equation of the form

T-1 = alpha + beta ln(R) + gamma (ln(R))3, (5)

where T is the temperature in degrees Kelvin, R is the thermistor resistance in

ohms, and alpha, beta, and gamma are constants. The error in this procedure is f

smaller than the general uncertainty in thermal conductivity measurements. T

constants are available to the data reduction program and are used for conve

of measured resistance into temperature. Electronics Technicians are respon

for entering the constants of a new resistor into the program. Do not attempt t

calibrate the thermistors—a specialized facility is required.

Needle Probe Secondary Calibration

ODP procedure with the Thermcon-85 system includes a calibration of each

needle probe using standard materials of “known” thermal conductivity values

(Table 8—1). These values were established on Legs 127, 129, and 131 and

subsequent legs using this same instrument. This calibration should be viewe

relative one that makes ODP shipboard data a little more consistent.

The standard measurements must be entered into a separate spreadsheet an

liner coefficients (slope, intercept) determined. The coefficients are then enter

into the PROBES.DAT file using the PROBES program utility. The thermal

conductivity values returned by the PC-TC program are subsequently correct

using these coefficients.

Table 8—1 Standard materials used for calibrations and control measureme

Standard material Thermal conductivity [W/(m·K)]

Black rubber 0.54

Red rubber 0.96

Macor 1.61


PERFORMANCE

Precision About 5%. (Systematic evaluation is required.)

Accuracy About 5%. (Systematic evaluation is required.)

MEASUREMENT

1. Bring cores to temperature equilibrium (about 4 hr). Hard-rock specimens should be placed in a water bath to equilibrate.

2. Soft sediment: drill holes into core liner. Also drill a small hole in semiconsolidated sediment if necessary. Apply thermal joint compound if necessary. Insert full-space probes carefully into sediment. Rocks: prepare smooth surface on a split-core specimen at least 5 cm long. Treat the needles gently, and store them properly when not in use.

3. Insert one probe into a standard material for a control measurement, to be used for later corrections if necessary.

4. Start the TCMENU program and follow the prompts for parameters. Default values are provided for each prompt.

5. Press the reset button on the Thermcon-85 unit to start the drift study. After a couple of minutes, the drift data will be displayed. The drift study is performed in phases of 25 minutes, the maximum time the box can be programmed. The drift study is terminated if all positions are equilibrated or if the user overrides the drift study.

6. Press the reset button twice to start the process of heating, data acquisition, and creation of the raw data file. Messages will be displayed if there are data or hardware problems.

7. The user has the option of acquiring more data and processing batches of data later or processing the data collected immediately. It is recommended to process the data immediately.

8. Load the PROCESS program from the TCMENU screen. The run just completed will appear as the default run to be processed. Accept or change it.

9. Select the position to be processed and the drift correction. The ln(t) vs. T graph will be displayed.

10. Select the time interval to be processed by moving the cross hairs on the screen. For routine processing, use the same interval used for secondary probe calibration. Adjust if necessary. Press enter to calculate conductivity and the fit parameter. Warnings will come up if the nonlinear component is considered too large, the fit is poor, the segment is considered too short, etc.

11. Press enter twice to write the conductivity of a segment to the Results file.

DATA PROCESSING

Data reduction with the TC-PC program written for the Thermcon-85 system is

based on a least-squares fit of the measured temperatures to the following

equation, which is a variation of Equation XXX(107?):

T = (q / 4¼k) ln(t) + At + B. (6)


d for

s are

ssible.

sed,

re

the

t

and

The constant A is the temperature drift rate (also including edge effect, asymmetry,

nonzero epoxy conductivity, etc.) during measurement and is expressed in K/min.

The constant B represents other imperfections in the experiment. The unknowns in

this system are k, A, and B, so when more than three data pairs are acquired the

system is overdetermined. Using the previous equation for the rate of heating, the

coefficient k can be determined at any time increment dt as

k = [2 i2 R / L dln(t)] / 4¼ (dT - At - B)], or (7)

k = (i2 R / 2¼L) [dln(t) / (dT)]. (8)

The first group of terms in these equations is an instrument constant including

generated heat and needle geometry. The second group of terms is calculate

each measurement.

The optimum time segment for calculating thermal conductivity is selected

interactively by the user by placing cross hairs on a ln(t) vs. T plot of the data.

Information on the quality of the fit is updated on the screen as the cross-hair

moved. The curve-fit parameter is the root mean square of the temperature

deviation and should not exceed 0.04°C/min. However, it is more important to

choose a consistent sampling time than it is to reduce the drift as much as po

DATA SPECIFICATIONS

TC-PC Output Files At present, the TC-PC data are not integrated in the new ODP database. The

following two program output files are archived: the “Processed Data” or

“Results” (*.DAT) files and the “Raw Data” (*.TC) files.

Data in the *.DAT files are fixed format, mixed string, and numeric, with one

record (line) per position per TC run. If a given position on a run is not proces

then there is no entry in this file. However, if a given position is processed mo

than once, there are multiple lines in this file for that position. The file name is

hole identifier.

Data in the *.RAW files are free-format in which each line represents an outpu

string from the program. If a position was not used, some strings are omitted

some return zero values. The file name is a combination of hole ID and run

number.

Table 8—2 TC-PC “Processed Data” file.

Short description Description Data file designationsLeg Leg [TC-PC Results 1-4] legSite Site [TC-PC Results 8-11] siteHole Hole [TC-PC Results 13] holeCore Core [TC-PC Results 15-17] coreCore type Core type [TC-PC Results 19] core_typeSection Section [TC-PC Results 21-22] section_or_stdTop Interval top (cm) [TC-PC Results 24-28] interval_topBottom Interval bottom (cm) [TC-PC Results 30-34] interval_bottomSpace Space model [TC-PC Results 49] full_or_halfRun No. Run number [TC-PC Results 51-53] run_numberProbe Probe number [TC-PC Results 55-57] probe_numberPosition Position number [TC-PC Results 59] position_numberTC uncorr. Uncorr. thermal conductivity. [W/(m·K)] [TC-PC Results 61-67] calculated_tc


Notes: The numbers following the file name (TC-PC Results . . .) are positions in the fixed-space format of the output file. Corrected thermal conductivity is corrected using the secondary probe calibration coefficients m1 and m0 obtained from standard measurements. Corrected thermal conductivity is added only if the user selects this option when specifying data reduction. If correction is not selected, the position numbers are reduced by 8 spaces starting with the “Standard error” field.

TC corr. Corr. thermal conductivity. [W/(m·K)] [TC-PC Results 69-75] corrected_tcR2 Standard error R2 [TC-PC Results 77-87] standard_error

Drift Calculated drift (°C/s) [TC-PC Results 89-97] calculated_driftLower end Lower end point used [TC-PC Results 99-100] lower_end_pointFirst time Time at lower end point (s) [TC-PC Results 102-104] time_at_first_pointUpper end Upper end point used [TC-PC Results 106-107] upper_end_pointLast time Time at upper end point (s) [TC-PC Results 109-111] time_at_last_pointDrift status Drift study status [TC-PC Results 113-126] drift_statusT drift Temp. at drift study termination (°C) [TC-PC Results 128-132] drift_temperatureDrift rate Drift rate at termination (°C/s) [TC-PC Results 134-142] drift_rateDrift fit Least-squares fit for drift [TC-PC Results 144-151] drift_fitRun status Run status (NORMAL, ...) [TC-PC Results 153-160] run_statusAlpha Probe alpha constant [TC-PC Results 162-180] probe_alphaBeta Probe beta constant [TC-PC Results 182-200] probe_betaGamma Probe gamma constant [TC-PC Results 202-220] probe_gammaResistance Probe wire resistance (ohm/cm) [TC-PC Results 222-227] probe_wire_resistanceHalf space Probe half-space flag (1 = true) [TC-PC Results 229-230] half_space_flagProbe m1 Probe secondary calibration slope [TC-PC Results 232=238] probe_m1Probe m0 Probe secondary calibration intercept [TC-PC Results 240-246] probe_m0Lower end Upper end point, probe calibration (s) [TC-PC Results 248-250] time_at_first_pointUpper end Lower end point, probe calibration (s) [TC-PC Results 252-254] time_at_last_pointDrift corr. Drift correction status [TC-PC Results 256-268] drift_correction_statusVersion Version of TC-PC program [TC-PC Results 270-274] tcpc_versionComment Comment [TC-PC Results 276-356] comment

Table 8—2 TC-PC “Processed Data” file.

Table 8—3 TC-PC “Raw Data” file (free format).

Short description Description Data file designationsRun parametersTitle Title string [TC-PC Raw 1] titleRun Run number [TC-PC Raw 2] run_numberPositions No. of positions used; length (min.) [TC-PC Raw 3] no_of_positions_lengthParameters for first positionSample ID ODP sample identification [TC-PC Raw 4] sample_idPiece Piece [TC-PC Raw 5] pieceSubpiece Subpiece [TC-PC Raw 5] sub_pieceSpace Space model [TC-PC Raw 7] full_or_halfPosition no. Position number [TC-PC Raw 8] position_numberAlpha Probe alpha constant [TC-PC Raw 9.1] probe_alphaBeta Probe beta constant [TC-PC Raw 9.2] probe_betaGamma Probe gamma constant [TC-PC Raw 9.3] probe_gammaResistance Probe wire resistance (ohm/cm) [TC-PC Raw 9.4] probe_wire_resistanceHalf space Probe half-space flag (1 = half) [TC-PC Raw 9.5] half_space_flagProbe m1 Probe secondary calibr. slope [TC-PC Raw 9.6] probe_m1

Probe m0 Probe secondary calibr. intercept [TC-PC Raw 9.7] probe_m0

Lower end Lower end point, probe calib. (s) [TC-PC Raw 9.8] time_at_first_pointUpper end Upper end point, probe calib. (s) [TC-PC Raw 9.9] time_at_last_pointComment Position-specific comment [TC-PC Raw 10] comment

Parameters repeated for other positionsa

Drift time Drift: no. of readings; length(s) [TC-PC Raw one line, two values]Drift study for first positionDrift t-T String of time-temperature pairs [TC-PC Raw one line, unlimited pairs]Drift end Temp., rate., fit, at end of drift study [TC-PC Raw one line, three values]

Drift study repeated for other positionsb


Notes: aThe probe parameters of lines 4–10 are written for subsequent positions only if the positions were used, otherwise the lines are omitted. bThe drift study data lines (two lines per position) are always written to the file regardless whether positions were used or not. If a position was not used, all values are zero. cData are written on one line for each measurement cycle. On each line, there are the following readings separated in time by 3 s (hard-coded in the program): (1) cycle number; (2) internal reference voltage; (3) to (7) up to five probe voltage readings (no reading for unused positions); (8) heater current. Total time for one cycle is (2 + <number of positions used>) times 3 s (2 stands for reference and heater current readings). It varies between 6 s (no position used) and 21 s (five positions used).

Database Model

Standard Queries The standard queries will be defined once the upload routine has been

implemented.

Drift status Drift status (OK; OVERRIDE) [TC-PC Raw one line, one alpha string]Data for positions 1–5Data Cycle #; ref. volt; I1 to I5; current [TC-PC Raw multiple lines, 3-8 values per line)

Data repeated for each meas. cyclec

Run status Run status (NORMAL...) [TC-PC Raw one line, one alpha string]

Table 8—3 TC-PC “Raw Data” file (free format).

Table 8—4 Database model

TCON section TCON probe proc. data TCON runtcon_id [PK1] [FK] tcon_id [PK1] [FK] tcon_id [PK1]

tcon_probe_num [PK2] [FK] tcon_probe_num [PK2] tcon_run_minutestop_interval tcon_comment tcon_run_numberbottom_interval tcon_meas_calib_m0 tcon_run_status

section_id tcon_meas_calib_m1tcon_meas_calib_time_first

tcon_meas_calib_time_last TCON cycleTCON control tcon_meas_drift_lsq_fit tcon_id [PK1] [FK]tcon_id [PK1] [FK] tcon_meas_drift_rate_final tcon_cycle_num [PK2]

tcon_probe_num [PK2] [FK] tcon_meas_drift_temp_final tcon_raw_heater_currentstandard_id [PK3] [FK] tcon_probe_alpha tcon_raw_heater_curr_time

tcon_probe_beta tcon_raw_rel_voltagetcon_probe_gamma tcon_raw_rel_voltage_time

TCON drift raw data tcon_probe_half_full

tcon_id [PK1] [FK] tcon_probe_specific_restcon_probe_num [PK2] [FK] tcon_proc_drift_corr_flag TCON probe cycle

tcon_raw_drift_time [PK3] tcon_proc_point_first tcon_id [PK1] [FK]tcon_raw_drift_temp tcon_proc_point_last tcon_cycle_num [PK2] [FK]

tcon_proc_thermcon tcon_probe_num [PK3]

tcon_proc_time_first tcon_raw_timetcon_proc_time_last tcon_raw_voltage

tcon_raw_drift_statustcon_raw_pos_num


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8.3. TK04 System

EQUIPMENT

ODP purchased the TK04 system in late 1995 and deployed it permanently on the

ship on Leg 168 (1996). The system was to replace the ailing Thermcon-85 device,

built at the Woods Hole Oceanographic Institution (WHOI) and in service on the

ship for many years. Currently, both systems are available to the user on the ship.

The TK04 was built by the Berlin company Teka based on an apparatus that had

been developed at the Technische Universität Berlin. It was used successfully

thousands of measurements on material from the Continental Deep Drilling

Program (KTB). The TK04 consists of

• automatic self-test, heating, and measurement unit TK04,

• full-space (VLQ) and half-space (HLQ) needle probes,

• vice and manual hydraulic pump for half-space contact measurementrocks, and

• Macor standards for both types of needle probes.

The TK04 measuring system features a self-test at the beginning of each

measuring cycle (including probe number validation), registration of the sourc

temperature and its drift, and calculation of the heating power used.

The following executable programs are used to operate the system:

• TKMEAS.EXE to acquire time-temperature data series (creating *.DWfiles),

• TKEVA for standard (<5% uncertainty) or special (<2% uncertainty) reevaluation of data, creating short *.DAT or long *.ERG lists and parameter files, and

• TKGRAPH to display all solutions and assess the quality of the calculated solutions.

In addition, the following parameter files are used:

• TKMEAS.MNU, a list of standard menu settings for TKMEAS.EXE,

• *.INI, list of parameters for probes, where “*” is the number engraved the probe, and

• TKEVA.INI, list of user-modifiable parameters required for TKEVA.EXE.

Multiple measurements can be taken under identical conditions. The instrume

cycles through the measurements automatically, creating files with the user-

defined root name (e.g., Core-Section-Interval; only six characters allowed) a

two-digit serial number incrementing by one for each measurement within a cy

The following files are created by the TK04 system:

• <Rootname-SerialNo>.DWL, (if “Save data” was selected); contains measurement parameters and temperature-time series (raw data), reqfor extended evaluations; it is not necessary, but strongly recommendto save the heating curves for routine evaluation. These files allow latextended evaluation and graphical display of the solutions.

• <Rootname->.LST, short list of results from evaluating one root-name-batch of *.DWL files using either the “special approximation method”


le is

ted

at

ch

alue.

res

time

the

ated

(SAM) or conventional (CON) method; contains evaluation parameters and the optimal calculated thermal conductivity value. This is the standard results file.

• TC-LIST.DAT, multiline short list (optional); contains the same information as previous file <Rootname->.LST but for multiple root names. This file is updated as new evaluations are performed. This ficreated only by the optional extended evaluation.

• <Rootname>.ERG, long lists of results from evaluating *.DWL files withthe SAM method; contains evaluation parameters and all valid calculathermal conductivity values. This file is optional and required only if graphical evaluation of all valid solutions is desired. It can be createdany time if the *.DWL files are saved. This file is created only by the optional extended evaluation.

CALIBRATION

No calibration is required. The unit conducts a self-test at the beginning of ea

measurement cycle. Macor standards are used to confirm the 1.65 W/(m·K) v

DATA PROCESSING

The Special Approximation Method (SAM)

The main advantage of the Teka data reduction program is the SAM that ensu

that only results of physical significance are considered. The critical choice of

interval for calculation of conductivity, selected manually by the user with the

Thermcon-85 system, is accomplished by an algorithm that automatically finds

optimal time interval. The solution can be judged in great detail and the data

reevaluated with different boundary parameters if warranted. The following

explanations are modified from the Teka user manual.

The first evaluation step is an approximation to the solution of a constantly he

line source (Kristiansen, 1982):

T(t) = A1 + A2ln(t) + A3[ln(t)/t] + A4(1/t). (9)

The coefficients Ai are calculated with the least-squares method. A1, A3, and A4 are

related to source geometry and thermal properties. A2 is calculated by

A2 = q / 4πk, (10)

where q is the heating power (Wm) and k [W/(m·K)] is the thermal conductivity. If

the coefficients Ai are determined, T(t) can be expressed analytically and the

apparent thermal conductivity Ka(t) can be calculated by differentiating Equation

on page 9 with respect to ln(t):

ka(t) = dT/dln(t) = q/4π {A2 + A3[1/t – ln(t)/t] + A4/t}. (11)

It can be shown that the desired value k is at ka(tmax), where tmax is the “extreme

time.” The requirement for the maximum is

d/dt[ka(tmax)] = 0, (12)

and tmax is

tmax = e(2A3–A4)/A3, A3 > 0. (13)

The logarithm of the extreme time (LET) becomes


t be

g for

ng

T

LET = ln(tmax) = (2A3 - A4) / A3. (14)

The time-dependent terms in previous equation are:

T(tmax) = A2ln(tmax) + A3[ln(tmax)/tmax] + A4/tmax. (15)

A4 can be substituted with (previous) Equation (118?) to give

T(tmax) = A2ln(tmax) + 2A3[ln(tmax)/tmax]. (16)

This equation shows that the purely logarithmic dependence of the approximated

temperature (required by the theory) is stronger the larger tmax gets. For large tmax,

the second term in Equation on page 10 approaches zero.

The evaluation procedure approximates the heating curve in as many time

intervals as possible and examines each interval for its suitability for thermal

conductivity calculation using the following criteria:

1. ka(t) is located above a given value of time defined by LET,

2. standard deviation of the function for A2 is below a given value,

3. ka(t) is a maximum: A3 > 0, and

4. derivation ka(t) is continuous for t = tmax: A2tmax – A3 - 0.

If these criteria are met, thermal conductivity can be calculated as

k = q / (4πA2). (17)

The evaluation interval is restricted by the dimension of the line source. It mus

within the interval of 20 to 80 s to avoid boundary effects, and at least 25 s lon

a stable calculation of the coefficients. The input parameters for standard

evaluation are

• minimum duration of approximation interval: 25 s,

• start of first approximation interval: 20 s,

• end of last approximation interval: 80 s,

• lower limit for LET: 4, and

• maximum standard deviation of calculated temperature curve from measured heating curve: 0.0003.

With the default parameters, the heating curve is approximated for the followi

time intervals:

[20,45] [20,46] [20,47] . . . [20,78] [20,79] [20,80][21,46] [21,47] . . . [21,78] [21,79] [21,80][22,47] . . . [22,78] [22,79] [22,80]. . .[53,78] [53,79] [53,80][54,79] [54,80][55,80]

Among all time intervals that fulfill the listed criteria, the one with the largest LE

is used to calculate thermal conductivity. No solutions may be found if the

measurement is disturbed by poor sample condition or ambient temperature

changes.

Extended Evaluation An extended evaluation is required if


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ted),

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th

alid

stem

ses,

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ure

• the valid solutions are to be plotted against the calculation parameterjudge the results graphically, or

• the measurements are to be reevaluated with different parameters (estronger criterion for the LET).

In both cases, the *.DWL files containing the temperature-time data are requi

The *.ERG files (long result lists) that can be created contain all valid solutions

the thermal conductivity, and a line entry in the TC-LIST.DAT file is created wi

the asymptotic (optimal) thermal conductivity value. There are three options fo

extended evaluation:

• single evaluation: typing <TKSAM> prompts for filename,

• batch mode with filename as parameter: typing <TKSAM filename> starts evaluation using the standard parameters (no *.ERG file is creaand

• Batch mode evaluating a sequence of data files: after typing TKSAM, type return instead of a filename; all *.DWL files in the directory will bevaluated.

The manufacturer’s manual should be consulted for details in regard to file pa

requirements, data quality issues, etc.

Graphical Evaluation The program TKGRAPH can be used to visualize and judge the quality of all v

SAM evaluation results for thermal conductivity. *.ERG files are required for

plotting. Four graphs are presented for each measurement:

• thermal conductivity vs. LET,

• thermal conductivity vs. interval duration,

• thermal conductivity vs. start of interval, and

• thermal conductivity vs. end of interval.

A series of files can also be viewed. Consult the manufacturer’s manual for sy

configuration, practical hints, guidance for the judgment of results, etc.

Evaluation with Conventional Method

Under certain experimental circumstances (e.g., porous material, high water

content) the SAM evaluation may not accept any results because the

measurements are too disturbed for the sensitive approximations. In these ca

results may be obtained using the conventional evaluation method in which

thermal conductivity is calculated from the inverse slope of the heating curve

section of logarithmic linearity. In general, a heating duration > 80 s becomes

necessary. Accuracy of conventional evaluations is not as good as that of SA

evaluations and the quality cannot be verified graphically.

The program TKCON.EXE is used for the conventional evaluation. The struct

and application is similar to the TKSAM.EXE program. The configuration file

TKCON.INI includes the following standard parameters:

• minimum duration of interval: 30 s,

• start time: 30 s,

• end time: 120 s, and

• standard deviation of fit: 0.003.


ed

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if at

Existing data can be evaluated later with the conventional method (i.e., after the

SAM method has failed to yield solutions). Automatic Evaluation with TKCON

can be set by typing

TKMEAS/EVA=CON

or if the option

TKMEAS/DCL=20/EVA=CON

is entered. Calling TKMEAS without the /EVA option invokes evaluation with

TKSAM.EXE.

A short list of results is created by TKCON with similar structure as the file

created by TKSAM. The difference is that instead of LET the standard deviation is

reported. The evaluation method used (SAM; CON) is indicated in each line of the

file. A long list of results for each measurement can be produced by typing, prior

to starting TKMEAS:

set TKCON=ON

The long list includes the calculated values of thermal conductivity, standard

deviation, and the start, duration, and end of each interval.

Half-Space Measurements

For the half-space needle probe (HLQ) it is expected that the total amount of

produced heat penetrates into the sample. The thermal conductivity is thus

calculated with twice the heating power used for the full-space solution. This

assumption is justified if the thermal conductivity of the samples is not lower than

about 1 W/(m·K); at lower values an error arises because some of the produc

heat is penetrating the probe half-space, in which case it is necessary to dete

correction factors to compensate for the heat loss.

PERFORMANCE

Precision Extended evaluation, using special parameters adapted to circumstances, yie

uncertainty of less than 2%. This is clearly smaller than variations caused by

sample preparation and inhomogeneities in rocks and sediments, and specia

evaluations are appropriate only for standard materials and fundamental mate

investigations.

Accuracy Random variations of thermal conductivity in natural materials such as sedim

and rocks typically give an uncertainty of about 5%. Routine evaluation using

TKEVA.EXE has an accuracy of about 5% and is therefore appropriate.

MEASUREMENT

Standard Settings for Data Acquisition

1. Bring cores to temperature equilibrium (about 4 hr). Hard-rock specimeshould be placed in a water bath to equilibrate.

2. Soft sediment: drill holes into core liner. Also drill a small hole in semiconsolidated sediment if necessary. Apply thermal joint compoundnecessary. Insert full-space probes carefully into sediment. Hard-rocks:prepare smooth surface on a half-core specimen at least 5 cm long. Treneedles gently, store them properly when not in use.


3. On the computer, change to directory containing the TKMEAS.EXE file, press enter.

4. Type TKERG = ON, press enter.

5. Type the command tkmeas, press enter.

6. Set the parameters on the screen. Heating power should be about 5 W/m (adjust if necessary); measuring time should be about 80 s; enter Y to save time-temperature data.

DATA SPECIFICATIONS

TK04 Output Files Currently, TK04 data are not integrated in the new ODP database. The following

program output files are archived.

Table 8—5 TK04 “raw data file”: <Rootname-Serial>.DWL.

Short description Description Data file designationHeaderFilename Root name (custom sample id), serial [TK04 Raw Data] rootname_serialProbe Probe ID, TK04, date [TK04 Raw Data] probeComment Comment, used to identify sample [TK04 Raw Data] commentHeat Heating power (W/m) [TK04 Raw Data] heating_powerFit Slope, Std. dev., temperature [TK04 Raw Data] fit?Something ?’Reserved’ [TK04 Raw Data] ?something?Value1 ?Some (drift?) value 1 [TK04 Raw Data] ?value1?Value2 ?Some (drift?) value 2 [TK04 Raw Data] ?value2DataTemp Temperature (°C) [TK04 Raw Data] temperatureTime Time (s) [TK04 Raw Data] timeResistance Resistance (ohm) [TK04 Raw Data] resistance

Table 8—6 TK04 “results short list”: <Rootname>.LST (one rootname batch).

Short description Description Data file designationFilename Root name + serial (sample ID) [TK04 Results] rootname_serialTC Calculated thermal conductivity [TK04 Results] calculated_tcLET/STD LET (SAM) of std. dev. (CON) [TK04 Results] let_or_sdSolutions No. of solutions found [TK04 Results] solutionsStart time Start of approx. time interval (s) [TK04 Results] time_startTime Length of approx. time interval (s) [TK04 Results] time_lengthEnd time End of optimal time interval (s) [TK04 Results] time_endEval. Evaluation method (SAM or CON) [TK04 Results] eval_methodHints Comments (from *.DWL file) [TK04 Results] hints

Table 8—7 *TK04 “appended results short list”: <Rootname>.LST (all rootnames).

Short description Description Data file designationFilename Root name + serial (sample id) [TK04 Results] rootname_serialTC Calculated thermal conductivity [TK04 Results] calculated_tcLET/STD LET (SAM) of std. dev. (CON) [TK04 Results] let_or_sdSolutions Number of solutions found [TK04 Results] solutionsStart time Start of approximate time interval (s) [TK04 Results] time_startTime Length of approx. time interval (s) [TK04 Results] time_lengthEnd time End of optimal time interval (s) [TK04 Results] time_endEval. Evaluation method (SAM or CON) [TK04 Results] eval_methodHints Comments (from *.DWL file) [TK04 Results] hints


Notes: *ERG files are optional. They are created by extended evaluation and are required only for graphical evaluation. They can be recreated from *.DWL files at any time.

Database Model A database model and integration into the database are difficult to implement

without writing an ODP sample identification routine linked to the TK04 output. A

better approach is to write an entirely new user interface for the system, preferably

for an upgraded version with multiple-channel capability.

Table 8—8 *TK04 “extended results file”: *.ERG files.

Short description Description Data file designationHeader: SAM Evaluation Parameters TKSAM.EXEFilename Root name + serial (sample ID) [TK04 Results] rootname_serialComment Comment, used to identify sample [TK04 Raw Data] commentTime Time interval minimum (s) [TK04 Results] eval_interval_minStart time Start of evaluation (s) [TK04 Results] eval_time_startEnd time End of optimal time interval (s) [TK04 Results] eval_time_endLET Nat. log. of time [TK04 Results] eval_letStd. Dev. Limit of std. dev. (optional; 0.0003) [TK04 Results] eval_limit_sd

Table 8—9 Valid solutions.

Short description Description Data file designationTC Calculated thermal conductivity [TK04 Results] calculated_tcLET Natural logarithm of time at max. therm.al condition [TK04 Results] letStart time Start of approx. time interval (s) [TK04 Results] time_startTime Length of approx. time interval (s) [TK04 Results] time_lengthEnd time End of optimal time interval (s) [TK04 Results] time_endStd. Dev. Standard deviation of fit [TK04 Results] std-deviation


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9. STRENGTH

9.1. Principles

PHYSICAL BACKGROUND

Definition of Sediment Strength

Most soils and rocks are visco-elastic materials. Well-developed mathematical

theories are available only for linear visco-elasticity, whereas soils and rocks have

highly nonlinear stress-strain-time behavior. Therefore, time-independent elasto-

plastic theory is often used to describe the stress-strain relationships of natural

materials: the material is linearly elastic up to the yield point, and then it becomes

perfectly plastic (Holtz and Kovacs, 1981). Some materials are brittle and exhibit

little stress when strained (rocks); others are work-hardening (e.g., compacted

clays and loose sands) or work-softening. The latter model is particularly

applicable to clayey, soft, saturated, marine sediments, such as those usually

measured with the instruments described in this chapter: stress decreases as the

sediment is strained beyond a peak stress. The sediment yields (fails) at the peak

stress, which can be defined as the sediment’s strength.

Mohr-Coulomb Failure Criterion

According to Mohr, the shear stress on a failure plane at failure reaches some

unique function of the normal stress on that plane, or

τƒƒ = ƒ(σƒƒ), (1)

where τ is the shear stress and σ is the normal stress. The first subscript ƒ refers

the failure plane and the second ƒ means “at failure.” This function can graphic

be expressed by the Mohr failure envelope, the tangent to Mohr circles at diffe

τ and σ at failure. The Mohr failure hypothesis states that the point of tangenc

the Mohr failure envelope with the Mohr circle at failure determines the

inclination of the failure plane.

Coulomb found that there was a stress-independent component of shear stre

and a stress-dependent component. He called the latter the internal angle of

friction, φ, and the former seems to be related to the intrinsic cohesion and is

denoted by the symbol c. The Coulomb equation is then

τƒ = σ tanφ + c, (2)

where τƒ is the shear strength of the soil, σ is the applied normal stress, and φand c

are the strength parameters. Both parameters are not inherent properties of the

material tested, but also depend on the test conditions.

The Mohr-Coulomb strength criterion is the combination Mohr failure envelope,

approximated by linear intervals over certain stress ranges, and the Coulomb

strength parameters:

τƒƒ = σƒƒ tanφ + c. (3)


This is the only failure criterion that predicts the stresses on the failure plane at

failure, which is relevant to potential sliding surfaces in geotechnical applicatons.

Drained and Undrained Shear

When sediment is sheared under a load or applied stress, excess pore pressure is

produced that may or may not escape depending on the permeability of the

sediment and the time available. If the pore pressure can dissipate, the sediment is

most likely work-hardened. Therefore, from an experimental standpoint (triaxial

testing), undrained shear (total stress analysis) or drained shear (effective stress

analysis) can be applied to the sediment.

In the undrained shear scenario, volume changes translate into pore pressure

changes, and the assumption is made that the pore pressure and therefore the

effective stress (= total stress minus pore pressure) are indentical to those in the

field. The total, or the undrained shear strength, is used for the stress analysis.

Tests must be conducted rapidly enough so that undrained conditions prevail if

draining is possible in the experimental setup.

In the second, drained scenario, shear stress is used in terms of effective stresses.

The excess hydrostatic pressure must be measured or estimated. Knowing the

initial and the applied (total) stresses, the effective stress acting in the sediment can

be calculated. The volume change depends on the relative density and the

confining pressure. This approach is philosphically more satisfying because pore

water cannot carry any shear stress; i.e., shear strength is thought to be controlled

by the effective stresses (Holtz and Kovacs, 1981). Drained shear can ordinarily be

determined only in the laboratory and the procedure is not popular because there

are serious practical problems. Particularly in low-permeability material, the rate

of loading must be sufficiently slow to avoid the development of excessive pore

pressure, which can cause a test to take many days or weeks, and valve, seal, and

membrane leaks may become a problem.

Testing for Shear Strength

There are three limiting conditions of consolidation (happens before shear) and

drainage (happens during shear) that model real field situations: consolidated-

drained (CD), consolidated-undrained (CU), and unconsolidated-undrained (UU).

Unconsolidated-drained is not a meaningful condition because drainage would

occur during shear and the effects of confining pressure and shear could not be

separated. A special case of the UU test is the unconfined compression (labeled

here informally as UUU) test, where the confining pressure equals zero

(atmospheric pressure). This is by far the most common laboratory strength test

used in geotechnical engineering today (Holtz and Kovacs, 1981). The effective

stress at failure, and therefore the strength, is identical for the UU and UUU tests.

In practical terms, the following conditions must be satisfied for this to be true:

1. 100% saturation,

2. specimen (core interval) must be intact and homogenous,

3. material must be fine-grained (clay), and

4. specimen must be sheared rapidly to failure to avoid draining and evaporation.

Direct shear test and triaxial tests are the common laboratory shear strength tests.

Addiitonal special tests are for direct simple shear, ring shear, plain strain, and true


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triaxial test. These tests allow independent control and measurement of at least

principle stresses, σ1 and σ3, and changes in void ratio and pore pressure. The

results can be analyzed in the σ-τ diagram (Mohr circle), p-q diagram (stress

path), and other methods (e.g., Lambe and Whitman, 1979; Holtz and Kovacs,

1981). However, all these tests are too complex to be conducted in the shipboard

laboratory. Instead, ODP provides two rapid and simple tests, the vane shear tests

and the penetrometer test. These tests should be used as a guide only because t

are many reasons why the results are only approximate (e.g., Lambe and Whitman,

1979). Particularly the influence of pore pressure changes during the undraine

experiment cannot be estimated.

Vane Shear Test Undrained shear strength can be determined using a vane that is inserted into soft

sediment and rotated until the sediment fails. The torque, T, required to shear the

sediment along the vertical and horizontal edges of the vane is a relatively direct

measure of the shear strength. It must be normalized to the vane constant, K, which

is a function of the vane size and geometry:

τƒ ~ su = T / K, (4)

where su is a common notation for the vane shear strength (e.g., Lambe and

Whitman, 1979). Shear strength has the units of pascals (= N/m2), torque has the

units of newton·meters (N·m), and K has the units of meters cubed (m3). Two

systems are available onboard JOIDES Resolution to determine vane shear

strength. The automated vane shear system measures angular deflection of spri

that were calibrated for torque. The hand-held Torvane directly returns a measure

of shear strength from calibrated springs.

Penetrometer Test Failure can be defined as the maximum principal stress difference, which is the

same as the (unconfined) compressive strength of the specimen, σ1 – σ3. At a

prescribed strain, shear strength, τƒ, is related to compressive strength, ∆σƒ , by

τƒ ~ τmax = (σ1 – σ3) / 2 = ∆σƒ / 2. (5)

If ∆σƒ is determined in a UUU test by reading off the vertical strain, such as with

the pocket penetrometer, the value must be divided by 2 to obtain the shear

strength.


If there is visible core disturbance, measurements should not be taken. Moist

loss while the split core is being processed affects the shear strength

measurements.

USE OF SHEAR STRENGTH

Shear strength, or shear resistance, of sediments is the most important aspec

slope stability. However, the shear strength values obtained onboard do not alone

allow any slope stability analysis. They represent merely a relative strength profile.


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For clay-rich marine sediments, the stress-strain behavior is greatly dependent on

the stress history of the sample. The latter can be estimated in a semiquantitative

way by the ratio of measured shear strength to in situ overburden stress, σov:

h = su / σov. (6)

For normally consolidated, fine-grained, cohesive soils, h has a value of about

0.25. Larger values indicate overconsolidaion, smaller values indicate

underconsolidation. Marine sediments are typically overconsolidated in the

uppermost few to several meters and slightly or strongly underconsolidated in th

subjacent 100–200 m and deeper.

9.2. Automated Vane Shear (AVS) System

EQUIPMENT

Vane shear strength, Su, of soft sediment at laboratory conditions is determined

using a motorized miniature vane shear apparatus, following the ASTM D 4648-87

procedure (ASTM, 1987). A four-bladed vane is inserted into the split core and

rotated at a constant rate of 90°/min to determine the torque required to caus

cylindrical surface to be sheared by the vane. The difference in rotational strain

between the top and bottom of a linear spring is measured using digital shaft

encoders. Maximum spring deflection at peak strength is determined by the AVS

program and can easily be verified or adjusted by the user.

Undrained shear strength is

Su = T / K = (∆ / B) / K, (7)

where Su is in pascals (N/m2), T is torque (N·m), K is the vane constant (m3), ∆ is

the maximum torque angle at failure (°), and B is the spring constant that relates

the deflection angle to the torque (°/[Nm]). This simple relationship applies only if

all the terms have been converted to SI units; otherwise, conversion factors must be

used appropriately.

Potential sources of error using the motorized vane shear device are fracturing,

particularly at Su greater than 100–150 kPa, sand- and gravel-sized material (e.g.,

ice-rafted debris in glacial sediments), and surface drying of the core.

The moderately destructive measurements are done in the working half, with th

rotation axis parallel to the bedding plane. Typical sampling rates are one per cor

section until the sediment becomes too firm for instrument penetration.

The motorized vane shear apparatus and springs were purchased from Wykeham

Farrance Engineering, Ltd.

The vanes are usually manufactured by ODP.


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CALIBRATION

No routine calibration is performed by the user. However, spring constant B and

vane geometry K are important coefficients that must be verified and measured if

new specimens are purchased or manufactured.

Vane Calibration When a new AVS blade is produced or purchased, the vane blade constant K must

be determined. ODP personnel are responsible for this occasional calibrationK is

a geometrical factor and is calculated as

K = πD2 H/2 (1 + D / 3H) ×10–9, (8)

where D and H are the vane diameter (maximum width of two wings) and height

millimeters and K has the units of cubic meters. The procedure is as follows:

1. Take multiple measurements of vane height and diameter, and enter them in the program utility available at the AVS station.

2. Press “Calibrate” in the calibration utility; the program calculates the mevalue, standard deviation, number of measurements, and vane constant. The new constants are automatically used by the measurement program.

3. Initiate upload of the calibration statistics and vane constant into the ODP database.

Spring Calibration The springs used to measure torque must be calibrated to the angles of rotat

ODP personnel are responsible for this occasional calibration. The spring constant,

B, is defined as

B = ∆/T, (9)

where T is the torque (provided in kg·cm by the manufacturer) and ∆ is the

corresponding deflection angle. ODP personnel enter the data into a calibrati

utility that converts the data to N·m and determines the regression slope that

corresponds to B. The conversion is

T (N·m) = 0.0981 × T (kg·cm). (10)

The calibration procedure is as follows

1. Enter the factory-supplied angle and torque data in the program utility available at the AVS station.

2. Press “Calibrate” in the calibration utility; the program calculates the regression coefficients.

3. Update the spring constant for the measurement program.

4. Initiate upload of the calibration statistics and spring constant into the Odatabase.

In 1995, the following springs and constants were used (they are presumably based

on regression of torque values in kg-1cm-1):

1. 0.0092109,2. 0.018857,3. 0.030852, and4. 0.045146.


PERFORMANCE

Precision Repeatability of torque measurement in the exactly same material is estimated to

be better than 5%.

Accuracy This depends on the reference method used (e.g., common triaxial test) and the

material measured (e.g., sand vs. soft clay) and includes uncertainties resulting

from pore pressure developed during the measurement and the lack of confining

pressure. For large vane shear field tests, Lambe and Whitman (1979) estimated

that results are accurate to 20% at best.

MEASUREMENT

The user is guided through the measurements by the AVS program. The position of

the measurement in the core section is entered automatically in the program.

Measured strain is plotted against calculated torque. The principal measurement

steps are

1. Choose and mount the appropriate spring and vane and ensure that the corresponding identifiers are selected in the program.

2. Insert the vane until it is completely immersed in the sediment and start the program. It is crucially important for the relative precision and accuracy of the measurement that the vane is always inserted completely.

3. When the run has terminated, withdraw the vane and clean it.


DATA SPECIFICATIONS

Database Model

Notes: All values in the database should be in SI units (general rule). Vane and spring constants should be converted during the calibration procedure so that conversion factors do not have to be applied in standard queries.

Standard Queries

Table 9—1 AVS database model.

AVS section AVS vane calibration AVS spring calibrationavs_id [PK1] vane_calibration_id [PK1] spring_calibration_id [PK1]

section_id calibration_date_time calibration_date_timerun_num vane_id spring_id

run_date_time vane_constant spring_constant_m1system_id diameter_mean spring_m0spring_calibration_id diameter_sd spring_mse

vane_calibration_id number_of_dia_meas commentsdirection height_mean

rotation_rate height_sd AVS spring calibr. dataraw_data_collected number_of_height_meas spring_calibration_id [PK1] [FK]

comments torque_angle [PK2]

AVS section data pp_torqueavs_id[PK1] [FK]

pp_top_interval [PK2]pp_bottom_intervalmax_torque_angle

residual_torque_angle

AVS raw dataavs_id [PK1] [FK]pp_top_interval [PK2] [FK]

avs_record_number [PK3]torque_angle [PK4]

strain_angle

Table 9—2 AVS query A (results, measurements, and parameters) (to be implemented).

Short description Description DatabaseSample ID ODP standard sample designation Link through [Sample]sample_idDepth User-selected depth type Link through [Sample]sample_idSu Shear strength Su = [AVS Section Data] max_torque_angle

/ [AVS Spring Calibration] spring_constant_m1/ [AVS Vane Calibration] vane_constant

Max. Angle Maximum torque angle (at failure) [AVS Section Data] max_torque_angleRes. Angle Residual torque angle [AVS Section Data] residual_torque_angleRun Run number [AVS Section] run_numberDateTime Date and time of measurement [AVS Section] run_date_timeDirection Direction of measurement (usually x) [AVS Section] directionRaw Data Flags if raw data were saved [AVS Section] raw_data_collectedVane Vane identification [AVS Vane Calibration] vane_idSpring Spring identification [AVS Spring Calibration] spring_id

Table 9—3 AVS query B (raw data) (to be implemented).

Short description Description DatabaseTorque Torque angle [AVS Raw Data] torque_angleStrain Strain angle [AVS Raw Data] strain_angleSample ID ODP standard sample designation Link through [Sample]sample_id


9.3. Torvane

EQUIPMENT

The Torvane is a hand-held instrument with attachments calibrated to shear

strength for different ranges (stiffness of sediment; Table on page 8). It is rarely

used because the automated vane shear device available has a larger range, better

precision, and presumably superior accuracy.

Table 9—4 AVS query C (vane calibration) (to be implemented).

Short description Description DatabaseDateTime Calibration date/time [AVS Vane Calibration] calibration_date_timeVane ID Vane identification [AVS Vane Calibration] vane_idVane Const. Vane constant [AVS Vane Calibration] vane_constantDia. mean Diameter, mean of measurements [AVS Vane Calibration] diameter_meanDia. s.d. Diameter, std. dev. of measurements [AVS Vane Calibration] diameter_sdDia. n Diameter, no. of measurements [AVS Vane Calibration] number_of_dia_measHeight mean Height, mean of measurements [AVS Vane Calibration] height_meanHeight s.d. Height, std. dev. of measurements [AVS Vane Calibration] height_sdHeight n Height, no. of measurements [AVS Vane Calibration] height_of_dia_measComments Comments [AVS Vane Calibration] comments

Table 9—5 AVS query D (spring calibration) (to be implemented).

Short description Description DatabaseDateTime Calibration date/time [AVS Spring Calibration] calibration_date_timeSpring ID Spring identification [AVS Spring Calibration] spring_idSpring m1 Spring m1 (spring constant; slope) [AVS Spring Calibration] spring_constant_m1

Spring m0 Spring m0 (intercept) [AVS Spring Calibration] spring _m0

R square Mean squared error (mse) [AVS Spring Calibration] spring _mseComments Comments [AVS Spring Calibration] comments

Table 9—6 AVS query E (spring calibration data) (to be implemented).

Short description Description DatabaseAngle Angle [AVS Spring Calibration] torque_angleTorque Calibration torque at angle [AVS Spring Calibration] pp_torqueDateTime Calibration date/time [AVS Spring Calibration] calibration_date_timeSpring ID Spring identification [AVS Spring Calibration] spring_id

Table 9—7 Specifications of Torvane attachments.

Diameter (mm) Height of vanes (mm) Maximum τƒ (kPa)

19 3 250

25 5 100

48 5 20


DATA SPECIFICATIONS

Database Model

Standard Queries

9.4. Pocket Penetrometer

EQUIPMENT

The penetrometer is a flat-footed, cylindrical probe that is pushed 6.4 mm deep

below the split-core surface. The resulting resistance is the unconfined

compressive strength or 2Su. The mechanical scale is in units of kilograms per

square centimeter, which are converted into units of kilopascals by

2τƒ (kPa) = 98.1 × 2τƒ (kg/cm2). (11)

The maximum τƒ that can be measured with the pocket penetrometer is 220 kPa.

Table 9—8 Database model.

TOR section data TOR sample datator_id [PK1] tor_id [PK1] [FK]

sys_id pp_top_interval [PK2]section_id measurement_no [PK3]

run_date_time pp_bottom_intervaldirection strength_readingcore_temperature comments

rangecomments

Table 9—9 AVS query A (results and more) (to be implemented).

Short description Description DatabaseSample ID ODP standard sample designation Link through [Sample]sample_idDepth User-selected depth type Link through [Sample]sample_idStrength Strength reading (at failure) [TOR Sample Data] strength_readingDateTime Date and time of measurement [TOR Section Data] run_date_timeDirection Direction of measurement (usually x) [TOR Section Data] directionRange Sensitivity range [TOR Section Data] rangeComments Comments [TOR Sample Data] comments


DATA SPECIFICATIONS

Database Model

Standard Queries

Table 9—10 Database model.

PEN section data PEN sample datapen_id [PK1] pen_id [PK1] [FK]

sys_id pp_top_interval [PK2]section_id measurement_no [PK3]

run_date_time pp_bottom_intervaldirection strength_readingcore_temperature comments

adapter_usedcomments

Table 9—11 AVS query A (results and more) (to be implemented).

Short description Description DatabaseSample ID ODP standard sample designation Link through [Sample]sample_idDepth User-selected depth type Link through [Sample]sample_idStrength Strength reading (at failure) [PEN Sample Data] strength_readingDateTime Date and time of measurement [PEN Section Data] run_date_timeDirection Direction of meas (usually x) [PEN Section Data] directionAdaptor Adaptor used (sensitivity range) [PEN Section Data] adapter_usedComments Comments [PEN Sample Data] comments


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linear heat source on the plane surface of a poorly conducting body. Earth and Plan.

Sci. Letters, 74: 275–279.

Von Herzen, R., and Maxwell, E.A. (1959). The measurement of thermal conductivity o

deep sea sediments by a needle probe method. J. Geophys. Res., 64: 1557–1563.

Weast, R.C., Astle, M.J., and Beyer, W.H., 1985. CRC Handbook of Chemistry and Physics:

Boca Raton, FL (CRC Press).

References—4 PP Handbook , Peter Blum , November, 1997

e SI

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A-1

APPENDIX I. PHYSICAL UNITS

Unit systems

A coherent system of physical units is based on a certain set of base units (e.g.,

meter, kilogram, second) that are well defined in terms of actual physical

phenomena (e.g., length, mass, and time, respectively). Derived units in a coherent

system are formed as products of powers of base units without introducing

numerical factors. Their algebraic expressions in terms of the base units can be

replaced by special names and their symbols (e.g., 1 N = 1 mkgs–2). Derived units

can themselves be used to form other derived units and their symbols (e.g., 1 Pa =

1 Nm–2). Values of dimensionless quantities are expressed by pure numbers. The

corresponding unit is the ratio of a unit to itself, or the dimensionless unit of the

coherent system, and may be expressed by the number 1 (Weast et al., 1985).

There are two commonly known coherent systems of units: the Système

International d’Unités (SI) and the centimeter-gram-second (CGS) system. Th

is the only internationally recommended system and should be used througho

ODP physical measurements and analyses.

The obsolescent “electrostatic CGS” and “electromagnetic CGS” units cannot

strictly be compared to the corresponding units of the SI. This is because the

electromagnetic CGS system is a three-dimensional system of units in which

electric and magnetic quantities are considered to be derived from the centim

gram, and second as base units, whereas the SI has four dimensions for thes

quantities (meter, kilogram, second, and ampere). The complexities involved

such conversions are well known to those working with rock magnetic propert

SI UNITS

The SI name was adopted by the Conference des Poids et Mesure (CGPM) i

1960. It is based on the seven base units (CGPM 1960, 1971) listed in Table

(see Weast et al., 1985, for definitions).

Table Appendix—1SI base units.

Base quantity Name Symbol

Length meter m

Mass kilogram kg

Time second s

Electric current ampere A

Thermodynamic temperature kelvin K

Amount of substance mole mol

Luminous intensity candela cd

Appendix—1PP Handbook , Peter Blum , November, 1997

I

nits

s of

ity,

the

The base unit of mass is the only one with a name that, for historical reasons,

contains a prefix. Several subsystems of the SI are used in different fields of

science (e.g., the meter-kilogram-second [MKS] system in mechanics).

Derived SI units are listed in Table A-2.

The radian and steradian actually belong to a third class of “supplementary S

units” for which the CGPM (1960) declined to state whether they were base u

or derived units.

In addition to the set of formal derived units listed in Table A-2, there are score

additional SI derived units and unit symbols for other quantities (volume, dens

velocity, magnetic field strength, etc.). These are either trivial or defined within

Table Appendix—2Derived SI units.

Quantity Name Symbol Base unit Other SI

Plane angle radian rad m m-1

Solid angle steradian sr m2 m-2

Frequency hertz Hz s-1

Force newton N m kg s-2 J/m

Pressure,stress

pascal Pa m-1 kg s-2 N/m2

Energy,work,quantity of heat

joule J m2 kg s-2 N m

Power, radiant flux

watt W m2 kg s-2 J/s

Quantity of electricity,electric charge

coulomb C s A A s

Electric potential, potential difference, electromotive force

volt V m2 kg s-3 A-1 W/A

Capacitance farad F m-2 kg-1 s4 A2 C/V

Electric resistance ohm W m2 kg s-3 A-2 V/A

Conductance siemens S m-2 kg-1 s3 A2 A/V

Magnetic flux weber Wb m2 kg s-2 A-1 V s

Magnetic flux density tesla T kg s-2 A-1 Wb/m2

Inductance henry H m2 kg s-2 A-2 Wb/A

Luminous flux lumen lm cd sr

Illuminance lux lx m-2 cd sr

Activity becquerel Bq s-1

Absorbed dose gray Gy m2 s-2 J/kg

Appendix—2 PP Handbook , Peter Blum , November, 1997

appropriate context. Other units exactly defined in terms of SI units, but not part of

the SI, are listed in Table A-3.

Similarly, the use of certain decimal fractions and multiples of SI units, including

those listed in Table A-4, is considered appropriate.

OBSOLESCENT UNITS

Table A-5 lists a selection of units used in the ODP and which are to be abandoned.

The conversion to SI units is also listed.

Table Appendix—3Units exactly defined in terms of SI units.

Quantity Name Symbol Base unit

Time minute min 60 s

hour h 3,600 s

day d 86,400 s

Angle degree ° (π/180) rad

minute ’ (π/10,800) rad

second ’’ (π/648,000) rad

Temperature degree Celsius °C = T(K) - 273.15 K

Table Appendix—4 Accepted decimal multiples and fractions of SI units.

Quantity Name Symbol SI base unit

Length ångström A 10-10 m

Cross section barn b 10-28 m2

Volume liter lL 10-3 m3

Mass tonne t 103 kg

Pressure bar bar 105 Pa

Table Appendix—5Obsolescent units and their conversion to SI units.

Quantity Name Symbol SI base unit

Length inch in 2.54 x 10-2 m

foot ft 0.3048 m

mile mi 1609 m

Mass pound lb 0.453592 kg

short ton - 907.2 kg

Force kilogram-force kgf 9.80665 N

pound lb 4.448 N

kilo-pound kip 4.448 kN

Pressure kg/m2 kg/m2 9.8067 Pa

dyne/cm2 dyne/cm2 0.1 Pa

atmosphere atm 1.0133 x 105 Pa

mm Hg (0°C) mm Hg 1.3332 x 102 Pa

Appendix—3PP Handbook , Peter Blum , November, 1997

pounds per square inch psi 6.8948 x 103 Pa

pounds per square foot psf 47.880 Pa

tons per square foot tsf 9.5761 x 104 Pa

Magnetic flux density gauss G 10-4 T = 10-4 kgs-2A-1

electromag. units/cm3 emu/cm3 1.257 10-3 T = 10-3 Am2

Magnetic force oersted oe equivalent to 10-4 T

Table Appendix—5Obsolescent units and their conversion to SI units.

Appendix—4 PP Handbook , Peter Blum , November, 1997

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