GRADISTAT-1 (Danau)

GRADISTATVersion 4.0

A Grain Size Distribution and Statistics Package for the Analysis of

Unconsolidated Sediments by Sieving or Laser Granulometer

Developed by Simon Blott

Surface Processes and Modern Environments Research Group

Department of Geology

Royal Holloway

University of London

Egham

Surrey TW20 0EX

E-mail: [email protected]

Tel/Fax: +44 (0)1784 414168

The development of this program was inspired by Dave Thornley and John Jack atPostgraduate Research Institute for Sedimentology at the University of Reading, UK, andDepartment of Geology at Royal Holloway University of London, UK. It is provided in Microsoft format to allow both spreadsheet and graphical output. The program is best suited to analyseobtained from sieve or laser granulometer analysis. The user is required to input the masspercentage of sediment retained on sieves spaced at any intervals, or the percentage of sedimentdetected in each bin of a Laser Granulometer. The following sample statistics are then calculatedthe Method of Moments in Microsoft Visual Basic programming language: mean, mode(s), sorting(standard deviation), skewness, kurtosis, D10, D50, D90, D90/D10, D90-D10, D75/D25 and D75-D25. Grainparameters are calculated arithmetically and geometrically (in microns) and logarithmically (using thescale) (Krumbein and Pettijohn, 19381; Table 1). Linear interpolation is also used to calculate statisticalparameters by the Folk and Ward (1957)2 graphical method and derive physical descriptions (such“very coarse sand” and “moderately sorted”). The program also provides a physical description textural group which the sample belongs to and the sediment name (such as “fine gravelly coarse sand”)after Folk (1954)3. Also included is a table giving the percentage of grains falling into each size fraction,modified from Udden (1914)4 and Wentworth (1922)5 (see Table 2). In terms of graphical output,program provides graphs of the grain size distribution and cumulative distribution of the data inmetric and phi units, and displays the sample grain size on triangular diagrams. Samples mayanalysed singularly, or up to 250 samples may be analysed together. The program is ideal for the rapid analysis of sieve data and is freely available from the authorthe above address. Please note that the copyright for the program is held by author, and any distributionor use of the program should be acknowledged to him. S. Blott October 2000 1Krumbein, W.C. and Pettijohn, F.J. (1938) Manual of Sedimentary Petrography. Appleton-Century-Crofts, New York.

2Folk, R.L. and Ward, W.C. (1957) Brazos River bar: a study in the significance of grain size parameters. Journal of Sedimentary

Petrology, 27, 3-26. 3Folk, R.L. (1954) The distinction between grain size and mineral composition in sedimentary-rock nomenclature. Journal

Geology, 62, 344-359. 4Udden, J.A. (1914) Mechanical composition of clastic sediments. Bulletin of the Geological Society of America, 25, 655-744

5Wentworth, C.K. (1922) A scale of grade and class terms for clastic sediments. Journal of Geology, 30, 377-392.

Instructions on the Use of the GRADISTAT Program

Single Sample Analysis 1. Switch to the "Single Sample Data Input" sheet if it is not already active. Enter the aperture sizesthe sieves or Laser Granulometer bins used in the analysis into the cells in column B. Sizes mayentered either in ascending or descending numerical order. For convenience, you can click on onethe standard sieve or Laser Granulometer size intervals and GRADISTAT will enter the size classesyou. Any non-standard sieve sizes can be used, although some of the statistics may not be calculatedyou have not used sieves with at least whole phi intervals. See the section below if the sample containsunanalysed sediment, such as material retained in the pan after sieving. At least one size class than the largest particles in the sample should also be entered. A large area to the right of thecolumns is provided for the cut and paste of data from other spreadsheets, such as the import of Granulometer data. 2. Enter the weight or percentage of sample beside each size class in column C. When youfinished, make sure there are no data further down the spreadsheet which could cause an error.program will accept data down to row 230. 3. Enter the sample identity, analyst, date and initial sample weight (optional) at the top of the "SingleSample Data Input" sheet. 4. Click the "Calculate Statistics" button and wait a few moments for the program to finish runningWhen the dialog box appears, click "OK". 5. The results are summarised on the "Single Sample Statistics" sheet, which includes a distributionhistogram of the sample. Select "Print..." from the file menu to print the Summary Statistics pagedata is also shown on triangular diagrams on the "Gravel Sand Mud" and "Sand Silt Clay" sheetsFurther cumulative and distribution plots are given on other sheets. Multiple Sample Analysis 1. Switch to the "Multiple Sample Data Input" sheet. Enter the aperture sizes of the sieves or Granulometer bins used in the analysis into the cells in column B. The aperture sizes must be the for all the samples. Sizes may be entered either in ascending or descending numerical orderconvenience, you can click on one of the standard sieve or Laser Granulometer size intervalsGRADISTAT will enter the size classes for you. Any non-standard sieve sizes can be used, althoughsome of the statistics may not be calculated if you have not used sieves with at least whole phi intervalsSee the section below if samples contain unanalysed sediment, such as material retained in theafter sieving. At least one size class larger than the largest particles in the sample should alsoentered. 2. Enter the weight or percentage of sample in column C onwards. Make sure there are no data furtherdown the spreadsheet which could cause an error. The program will accept data down to row 230. 3. Enter the sample identity, analyst, date and initial sample weight (optional) in the green cells aboveeach sample listing. 4. If you require a Summary Statistics printout for each sample, select a tick in the option box. 5. Click the "Calculate Statistics" button and wait for the program to finish running (this may take severalminutes). GRADISTAT will give a warning if it detects a sample whose combined weight is greaterthe given sample weight. Click "OK" when prompted on the dialog boxes.

the given sample weight. Click "OK" when prompted on the dialog boxes. 6. The resulting statistics for all samples are summarised on the "Multiple Sample Statistics" sheetdata for each sample included in the analysis are also shown on triangular diagrams on the "GravelSand Mud" and "Sand Silt Clay" sheets. Cumulative and distribution plots will show the results forlast sample in the analysis. If graphical plots for other samples are required, use separate single sampleanalyses (above). Unanalysed Sediment Occasionally, samples may contain sediment in a size fraction of unspecified size, such as materialretained in the pan after sieving. Ideally, the whole size range in a sample should be analysed, andmay require further analysis of sediment remaining in the pan after sieving. The larger the quantitysediment remaining in the pan, the less accurate the calculation of grain size statistics, with statisticscalculated by the Method of Moments being most susceptible. Errors in Folk and Ward parametersbecome significant only when more than 5% of the sample is undetermined. If the sample containssediment in the pan the user should do one of the following: 1. Enter the weight or percentage of sample in the pan with a class size of zero (or leave the class

blank). GRADISTAT calculates the statistics assuming all sediment in the pan is larger than 10 f (1The grain size distribution graphs do not however plot the quantity of sediment in the pan. 2. Enter the weight or percentage of sample in the pan with a class size which the user considersthe lower size limit of sediment in the pan. GRADISTAT calculates the statistics assuming all sedimentin the pan is larger than this value and plots this quantity on the grain size distribution graphs. The above two options are recommended where there is less than 1% of the sample remaining pan. 3. Do not enter the quantity of sediment in the pan at all. GRADISTAT calculates the statistics ignoringthe sediment in the pan as if it were not present in the sample. This is recommended where theremore than 1% of the sample remaining in the pan. Samples containing more than 5% of sediment in the pan should ideally be analysed using a differenttechnique, such as sedimentation or laser granulometry. Great care must however be taken merging data obtained by different methods. Graph Scales The size scale used in graphical plots is dependent upon the range of sizes specified on the sampleinput sheets: the first and last values provide the extreme values on the graphs. While one sizelarger than the largest particles in the sample should be entered, other size classes outside thesize range of the sample have no influence on the statistical calculations. These classes may be deletedto narrow the size scale on graphs. Note that unused size classes within the size range of the sampleshould also be deleted, otherwise GRADISTAT assumes that zero sample weight was present in size classes. © Copyright Simon Blott (2000)

Table 1. Statistical formulae used in the calculation of grain size parameters.

f is the frequency in percent; m is the mid-point of each class interval in metric (mm) or

phi (mf) units; Px and fx are grain diameters, in metric or phi units respectively, at the

cumulative percentile value of x.

(a) Arithmetic Method of Moments

Mean Standard Deviation Skewness Kurtosis

100 = m

a

fmx

S

100

)( =

2

ama

xmf -Ss 3

3

100

)( =

a

ama

xmfSk

s

-S 4

4

100

)( =

a

ama

xmfK

s

-S

(b) Geometric Method of Moments


100

lnexp = m

g

mfx

S

100

)ln(lnexp =

2

gm

g

xmf -Ss 3

3

ln100

)ln(ln =

g

gm

g

xmfSk

s

-S

4

4

ln100

)ln(ln =

g

gm

g

xmfK

s

-S

Sorting (sg) Skewness (Skg) Kurtosis (Kg)

Very well sorted

Well sorted

Moderately well sorted

Moderately sorted

Poorly sorted

Very poorly sorted

Extremely poorly sorted

< 1.27

1.27 – 1.41

1.41 – 1.62

1.62 – 2.00

2.00 – 4.00

4.00 – 16.00

> 16.00

Very fine skewed

Fine skewed

Symmetrical

Coarse skewed

Very coarse skewed

< -1.30 -1.30 – -0.43 -0.43 – +0.43 +0.43 – +1.30

> +1.30

Very platykurtic

Platykurtic

Mesokurtic

Leptokurtic

Very leptokurtic

< 1.70

1.70 – 2.55

2.55 – 3.70

3.70 – 7.40

> 7.40

(c) Logarithmic Method of Moments


100 =

f

f

fmx

S

100

)( =

2

ff

fsxmf -S

3

3

100

)( =

f

ff

fs

xmfSk

-S

4

4

100

)( =

f

ff

fs

xmfK

-S

Sorting (sf) Skewness (Skf) Kurtosis (Kf)

Very well sorted

Well sorted


Moderately sorted

Poorly sorted Very poorly sorted


< 0.35

0.35 – 0.50

0.50 – 0.70

0.70 – 1.00

1.00 – 2.00 2.00 – 4.00

> 4.00

Very fine skewed

Fine skewed

Symmetrical

Coarse skewed

Very coarse skewed

> +1.30 +0.43 – +1.30 -0.43 – +0.43 -0.43 – -1.30

< -1.30

Very platykurtic

Platykurtic

Mesokurtic

Leptokurtic

Very leptokurtic

< 1.70

1.70 – 2.55

2.55 – 3.70

3.70 – 7.40

> 7.40







100 = m

a

fmx

S

100

)( =

2

ama

xmf -Ss 3

3

100

)( =

a

ama

xmfSk

s

-S 4

4

100

)( =

a

ama

xmfK

s

-S



100

lnexp = m

g

mfx

S

100

)ln(lnexp =

2

gm

g

xmf -Ss 3

3

ln100

)ln(ln =

g

gm

g

xmfSk

s

-S

4

4

ln100

)ln(ln =

g

gm

g

xmfK

s

-S


Very well sorted

Well sorted


Moderately sorted

Poorly sorted

Very poorly sorted


< 1.27

1.27 – 1.41

1.41 – 1.62

1.62 – 2.00

2.00 – 4.00

4.00 – 16.00

> 16.00

Very fine skewed

Fine skewed

Symmetrical

Coarse skewed

Very coarse skewed

< -1.30 -1.30 – -0.43 -0.43 – +0.43 +0.43 – +1.30

> +1.30

Very platykurtic

Platykurtic

Mesokurtic

Leptokurtic

Very leptokurtic

< 1.70

1.70 – 2.55

2.55 – 3.70

3.70 – 7.40

> 7.40



100 =

f

f

fmx

S

100

)( =

2

ff

fsxmf -S

3

3

100

)( =

f

ff

fs

xmfSk

-S

4

4

100

)( =

f

ff

fs

xmfK

-S


Very well sorted

Well sorted


Moderately sorted



< 0.35

0.35 – 0.50

0.50 – 0.70

0.70 – 1.00

1.00 – 2.00 2.00 – 4.00

> 4.00

Very fine skewed

Fine skewed

Symmetrical

Coarse skewed

Very coarse skewed

> +1.30 +0.43 – +1.30 -0.43 – +0.43 -0.43 – -1.30

< -1.30

Very platykurtic

Platykurtic

Mesokurtic

Leptokurtic

Very leptokurtic

< 1.70

1.70 – 2.55

2.55 – 3.70

3.70 – 7.40

> 7.40

(d) Logarithmic (Original) Folk and Ward (1957) Graphical Measures


3

845016 fff ++=ZM

6.64

5951684 ffffs

-+

-=I

( )1684

508416

2

2

ff

fff

-

-+=ISk

( )595

50955

2

2

ff

fff

-

-++

( )2575

595

44.2 ff

ff

-

-=GK

Sorting (sI) Skewness (SkI) Kurtosis (KG)

Very well sorted

Well sorted Moderately well sorted

Moderately sorted

Poorly sorted

Very poorly sorted


< 0.35

0.35 – 0.50 0.50 – 0.70

0.70 – 1.00

1.00 – 2.00

2.00 – 4.00

> 4.00

Very fine skewed

Fine skewed Symmetrical

Coarse skewed

Very coarse skewed

+0.3 to +1.0 +0.1 to +0.3 +0.1 to -0.1 -0.1 to -0.3 -0.3 to -1.0

Very platykurtic

Platykurtic Mesokurtic

Leptokurtic

Very leptokurtic

Extremely

leptokurtic

< 0.67

0.67 – 0.90 0.90 – 1.11

1.11 – 1.50

1.50 – 3.00

> 3.00

(e) Geometric Folk and Ward (1957) Graphical Measures

Mean Standard Deviation

3

lnlnlnexp 845016 PPP

M G

++=

÷ø

öçè

æ -+

-=

6.6

lnln

4

lnlnexp 9558416 PPPP

Gs

Skewness Kurtosis

( )( )

( )( )525

50955

1684

508416

lnln2

ln2lnln

lnln2

ln2lnln

PP

PPP

PP

PPPSkG

-

-++

-

-+=

( )7525

955

lnln44.2

lnln

PP

PPKG

-

-=

Sorting (sG) Skewness (SkG) Kurtosis (KG)

Very well sorted

Well sorted


Moderately sorted

Poorly sorted

Very poorly sorted Extremely poorly sorted

< 1.27

1.27 – 1.41

1.41 – 1.62

1.62 – 2.00

2.00 – 4.00

4.00 – 16.00 > 16.00

Very fine skewed

Fine skewed

Symmetrical

Coarse skewed

Very coarse skewed

-0.3 to -1.0 -0.1 to -0.3 -0.1 to +0.1 +0.1 to +0.3 +0.3 to +1.0

Very platykurtic

Platykurtic

Mesokurtic

Leptokurtic

Very leptokurtic

Extremely leptokurtic

< 0.67

0.67 – 0.90

0.90 – 1.11

1.11 – 1.50

1.50 – 3.00

> 3.00

Table 2. Size scale adopted in the GRADISTAT program, modified from Udden

(1914) and Wentworth (1922).

Grain Size

phi mm

Descriptive term

Very Large

-10 1024

Large

-9 512

Medium

-8 256

Small

-7 128

Very small

Boulder

-6 64 Very coarse

-5 32

Coarse

-4 16

Medium

-3 8

Fine

-2 4

Very fine

Gravel

-1 2

Very coarse 0 1

microns Coarse

1 500

Medium

2 250

Fine

3 125

Very fine

Sand

4 63

Very coarse

5 31

Coarse 6 16

Medium

7 8

Fine

8 4

Very fine

Silt

9 2

Clay



3

845016 fff ++=ZM

6.64

5951684 ffffs

-+

-=I

( )1684

508416

2

2

ff

fff

-

-+=ISk

( )595

50955

2

2

ff

fff

-

-++

( )2575

595

44.2 ff

ff

-

-=GK


Very well sorted


Moderately sorted

Poorly sorted

Very poorly sorted


< 0.35

0.35 – 0.50 0.50 – 0.70

0.70 – 1.00

1.00 – 2.00

2.00 – 4.00

> 4.00

Very fine skewed


Coarse skewed

Very coarse skewed

+0.3 to +1.0 +0.1 to +0.3 +0.1 to -0.1 -0.1 to -0.3 -0.3 to -1.0

Very platykurtic


Leptokurtic

Very leptokurtic

Extremely

leptokurtic

< 0.67

0.67 – 0.90 0.90 – 1.11

1.11 – 1.50

1.50 – 3.00

> 3.00



3


M G

++=

÷ø

öçè

æ -+

-=

6.6

lnln

4


Gs

Skewness Kurtosis

( )( )

( )( )525

50955

1684

508416

lnln2

ln2lnln

lnln2

ln2lnln

PP

PPP

PP

PPPSkG

-

-++

-

-+=

( )7525

955

lnln44.2

lnln

PP

PPKG

-

-=


Very well sorted

Well sorted


Moderately sorted

Poorly sorted


< 1.27

1.27 – 1.41

1.41 – 1.62

1.62 – 2.00

2.00 – 4.00

4.00 – 16.00 > 16.00

Very fine skewed

Fine skewed

Symmetrical

Coarse skewed

Very coarse skewed

-0.3 to -1.0 -0.1 to -0.3 -0.1 to +0.1 +0.1 to +0.3 +0.3 to +1.0

Very platykurtic

Platykurtic

Mesokurtic

Leptokurtic

Very leptokurtic


< 0.67

0.67 – 0.90

0.90 – 1.11

1.11 – 1.50

1.50 – 3.00

> 3.00



Grain Size

phi mm

Descriptive term

Very Large

-10 1024

Large

-9 512

Medium

-8 256

Small

-7 128

Very small

Boulder

-6 64 Very coarse

-5 32

Coarse

-4 16

Medium

-3 8

Fine

-2 4

Very fine

Gravel

-1 2

Very coarse 0 1

microns Coarse

1 500

Medium

2 250

Fine

3 125

Very fine

Sand

4 63

Very coarse

5 31

Coarse 6 16

Medium

7 8

Fine

8 4

Very fine

Silt

9 2

Clay

Jack at the and the

Microsoft Excel analyse data

mass or sediment

calculated using mode(s), sorting

Grain size (using the phi

statistical (such as

description of the coarse sand”) size fraction,

output, the data in both

Samples may be

author at distribution

Sedimentary

Journal of

655-744.

aperture sizes of Sizes may be

on one of classes for

calculated if contains

class larger the data of Laser

you have error. The

the "Single

running.

distribution page. The

Clay" sheets.

or Laser the same

order. For intervals and

although intervals.

in the pan should also be

data further 230.

cells above

take several greater than

sheet. The the "Gravel

results for the single sample

material analysed, and this

quantity of statistics

parameters contains

class size

f (1 mm).

considers to be sediment

remaining in the

statistics ignoring where there is

different taken when

the sample size class the grain

be deleted the sample

present in those







100 = m

a

fmx

S

100

)( =

2

ama

xmf -Ss 3

3

100

)( =

a

ama

xmfSk

s

-S 4

4

100

)( =

a

ama

xmfK

s

-S



100

lnexp = m

g

mfx

S

100

)ln(lnexp =

2

gm

g

xmf -Ss 3

3

ln100

)ln(ln =

g

gm

g

xmfSk

s

-S

4

4

ln100

)ln(ln =

g

gm

g

xmfK

s

-S


Very well sorted

Well sorted


Moderately sorted

Poorly sorted

Very poorly sorted


< 1.27

1.27 – 1.41

1.41 – 1.62

1.62 – 2.00

2.00 – 4.00

4.00 – 16.00

> 16.00

Very fine skewed

Fine skewed

Symmetrical

Coarse skewed

Very coarse skewed

< -1.30 -1.30 – -0.43 -0.43 – +0.43 +0.43 – +1.30

> +1.30

Very platykurtic

Platykurtic

Mesokurtic

Leptokurtic

Very leptokurtic

< 1.70

1.70 – 2.55

2.55 – 3.70

3.70 – 7.40

> 7.40



100 =

f

f

fmx

S

100

)( =

2

ff

fsxmf -S

3

3

100

)( =

f

ff

fs

xmfSk

-S

4

4

100

)( =

f

ff

fs

xmfK

-S


Very well sorted

Well sorted


Moderately sorted



< 0.35

0.35 – 0.50

0.50 – 0.70

0.70 – 1.00

1.00 – 2.00 2.00 – 4.00

> 4.00

Very fine skewed

Fine skewed

Symmetrical

Coarse skewed

Very coarse skewed

> +1.30 +0.43 – +1.30 -0.43 – +0.43 -0.43 – -1.30

< -1.30

Very platykurtic

Platykurtic

Mesokurtic

Leptokurtic

Very leptokurtic

< 1.70

1.70 – 2.55

2.55 – 3.70

3.70 – 7.40

> 7.40







100 = m

a

fmx

S

100

)( =

2

ama

xmf -Ss 3

3

100

)( =

a

ama

xmfSk

s

-S 4

4

100

)( =

a

ama

xmfK

s

-S



100

lnexp = m

g

mfx

S

100

)ln(lnexp =

2

gm

g

xmf -Ss 3

3

ln100

)ln(ln =

g

gm

g

xmfSk

s

-S

4

4

ln100

)ln(ln =

g

gm

g

xmfK

s

-S


Very well sorted

Well sorted


Moderately sorted

Poorly sorted

Very poorly sorted


< 1.27

1.27 – 1.41

1.41 – 1.62

1.62 – 2.00

2.00 – 4.00

4.00 – 16.00

> 16.00

Very fine skewed

Fine skewed

Symmetrical

Coarse skewed

Very coarse skewed

< -1.30 -1.30 – -0.43 -0.43 – +0.43 +0.43 – +1.30

> +1.30

Very platykurtic

Platykurtic

Mesokurtic

Leptokurtic

Very leptokurtic

< 1.70

1.70 – 2.55

2.55 – 3.70

3.70 – 7.40

> 7.40



100 =

f

f

fmx

S

100

)( =

2

ff

fsxmf -S

3

3

100

)( =

f

ff

fs

xmfSk

-S

4

4

100

)( =

f

ff

fs

xmfK

-S


Very well sorted

Well sorted


Moderately sorted



< 0.35

0.35 – 0.50

0.50 – 0.70

0.70 – 1.00

1.00 – 2.00 2.00 – 4.00

> 4.00

Very fine skewed

Fine skewed

Symmetrical

Coarse skewed

Very coarse skewed

> +1.30 +0.43 – +1.30 -0.43 – +0.43 -0.43 – -1.30

< -1.30

Very platykurtic

Platykurtic

Mesokurtic

Leptokurtic

Very leptokurtic

< 1.70

1.70 – 2.55

2.55 – 3.70

3.70 – 7.40

> 7.40



3

845016 fff ++=ZM

6.64

5951684 ffffs

-+

-=I

( )1684

508416

2

2

ff

fff

-

-+=ISk

( )595

50955

2

2

ff

fff

-

-++

( )2575

595

44.2 ff

ff

-

-=GK


Very well sorted


Moderately sorted

Poorly sorted

Very poorly sorted


< 0.35

0.35 – 0.50 0.50 – 0.70

0.70 – 1.00

1.00 – 2.00

2.00 – 4.00

> 4.00

Very fine skewed


Coarse skewed

Very coarse skewed

+0.3 to +1.0 +0.1 to +0.3 +0.1 to -0.1 -0.1 to -0.3 -0.3 to -1.0

Very platykurtic


Leptokurtic

Very leptokurtic

Extremely

leptokurtic

< 0.67

0.67 – 0.90 0.90 – 1.11

1.11 – 1.50

1.50 – 3.00

> 3.00



3


M G

++=

÷ø

öçè

æ -+

-=

6.6

lnln

4


Gs

Skewness Kurtosis

( )( )

( )( )525

50955

1684

508416

lnln2

ln2lnln

lnln2

ln2lnln

PP

PPP

PP

PPPSkG

-

-++

-

-+=

( )7525

955

lnln44.2

lnln

PP

PPKG

-

-=


Very well sorted

Well sorted


Moderately sorted

Poorly sorted


< 1.27

1.27 – 1.41

1.41 – 1.62

1.62 – 2.00

2.00 – 4.00

4.00 – 16.00 > 16.00

Very fine skewed

Fine skewed

Symmetrical

Coarse skewed

Very coarse skewed

-0.3 to -1.0 -0.1 to -0.3 -0.1 to +0.1 +0.1 to +0.3 +0.3 to +1.0

Very platykurtic

Platykurtic

Mesokurtic

Leptokurtic

Very leptokurtic


< 0.67

0.67 – 0.90

0.90 – 1.11

1.11 – 1.50

1.50 – 3.00

> 3.00



Grain Size

phi mm

Descriptive term

Very Large

-10 1024

Large

-9 512

Medium

-8 256

Small

-7 128

Very small

Boulder

-6 64 Very coarse

-5 32

Coarse

-4 16

Medium

-3 8

Fine

-2 4

Very fine

Gravel

-1 2

Very coarse 0 1

microns Coarse

1 500

Medium

2 250

Fine

3 125

Very fine

Sand

4 63

Very coarse

5 31

Coarse 6 16

Medium

7 8

Fine

8 4

Very fine

Silt

9 2

Clay



3

845016 fff ++=ZM

6.64

5951684 ffffs

-+

-=I

( )1684

508416

2

2

ff

fff

-

-+=ISk

( )595

50955

2

2

ff

fff

-

-++

( )2575

595

44.2 ff

ff

-

-=GK


Very well sorted


Moderately sorted

Poorly sorted

Very poorly sorted


< 0.35

0.35 – 0.50 0.50 – 0.70

0.70 – 1.00

1.00 – 2.00

2.00 – 4.00

> 4.00

Very fine skewed


Coarse skewed

Very coarse skewed

+0.3 to +1.0 +0.1 to +0.3 +0.1 to -0.1 -0.1 to -0.3 -0.3 to -1.0

Very platykurtic


Leptokurtic

Very leptokurtic

Extremely

leptokurtic

< 0.67

0.67 – 0.90 0.90 – 1.11

1.11 – 1.50

1.50 – 3.00

> 3.00



3


M G

++=

÷ø

öçè

æ -+

-=

6.6

lnln

4


Gs

Skewness Kurtosis

( )( )

( )( )525

50955

1684

508416

lnln2

ln2lnln

lnln2

ln2lnln

PP

PPP

PP

PPPSkG

-

-++

-

-+=

( )7525

955

lnln44.2

lnln

PP

PPKG

-

-=


Very well sorted

Well sorted


Moderately sorted

Poorly sorted


< 1.27

1.27 – 1.41

1.41 – 1.62

1.62 – 2.00

2.00 – 4.00

4.00 – 16.00 > 16.00

Very fine skewed

Fine skewed

Symmetrical

Coarse skewed

Very coarse skewed

-0.3 to -1.0 -0.1 to -0.3 -0.1 to +0.1 +0.1 to +0.3 +0.3 to +1.0

Very platykurtic

Platykurtic

Mesokurtic

Leptokurtic

Very leptokurtic


< 0.67

0.67 – 0.90

0.90 – 1.11

1.11 – 1.50

1.50 – 3.00

> 3.00



Grain Size

phi mm

Descriptive term

Very Large

-10 1024

Large

-9 512

Medium

-8 256

Small

-7 128

Very small

Boulder

-6 64 Very coarse

-5 32

Coarse

-4 16

Medium

-3 8

Fine

-2 4

Very fine

Gravel

-1 2

Very coarse 0 1

microns Coarse

1 500

Medium

2 250

Fine

3 125

Very fine

Sand

4 63

Very coarse

5 31

Coarse 6 16

Medium

7 8

Fine

8 4

Very fine

Silt

9 2

Clay

Single Sample Data Input Screen Enter your data in the columns below, and then click the "Calculate

Statistics" button. See the "Information" sheet for more information.

Sample Identity: VF-74-103

Analyst:

Date: Auto. add

Initial Sample Weight: (optional) aperturesat:

Aperture Class Weight

(microns) Retained (g or %)

2000 6.1970

1000 2.6600

500 6.8418

250 1.3150

100 5.0753

50 0.1088

20 0.12123

5 0.07942

2 0.02336

0.5 0.10104

Enter your data in the columns below, and then click the "Calculate

Statistics" button. See the "Information" sheet for more information.

SAMPLE STATISTICS

SAMPLE IDENTITY: VF-74-103 ANALYST & DATE: ,

SAMPLE TYPE: Bimodal, Poorly Sorted TEXTURAL GROUP: Sand

SEDIMENT NAME: Poorly Sorted Very Coarse Sand

GRAIN SIZE DISTRIBUTION

MODE 1: GRAVEL: COARSE SAND: 30.4%

MODE 2: SAND: MEDIUM SAND: 5.8%

MODE 3: MUD: FINE SAND: 17.0%

D10: V FINE SAND: 5.8%

MEDIAN or D50: V COARSE GRAVEL: V COARSE SILT: 0.4%

D90: COARSE GRAVEL: COARSE SILT: 0.3%

(D90 / D10): MEDIUM GRAVEL: MEDIUM SILT: 0.2%

(D90 - D10): FINE GRAVEL: FINE SILT: 0.1%

(D75 / D25): V FINE GRAVEL: V FINE SILT: 0.1%

(D75 - D25): V COARSE SAND: CLAY: 0.4%

Logarithmic

f

MEAN : 0.902

SORTING (s): 1.377

SKEWNESS (Sk ): 2.148

KURTOSIS (K ): 11.65

483.5

4130.6

METHOD OF MOMENTS

f

0.500

2.661

30.75

-2.094

0.351

0.901

2.780

mm

750.0

175.0

138.9

783.8

4269.4

2.816

-1.360

4.942

Geometric

mm

79.99

Arithmetic

mm

466.9

7.043

0.484

17.07

-0.761

1.828

2.417

-0.536

0.484 Very Platykurtic

Description

Coarse Sand

Poorly Sorted

39.3%

Geometric Logarithmic

Very Fine Skewed

f

0.536

FOLK & WARD METHOD

0.0%

98.4%

1.6%

0.0%

0.0%

1610.7

-2.099

2.848

0.0%

0.0%

0.0%

592.4 0.755

1.273

mm

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

-2.00.02.04.06.08.010.012.0

Cla

ss

We

igh

t (%

)

Particle Diameter (f)

1 10 100 1000

Particle Diameter (mm)

)(x

Multiple Sample Data Input Screen Enter your data in the columns below, and then click the

"Calculate Statistics" button. Enter Sample Info in the green cells.

Aperture Class Weight Retained (g or %) in Different Samples

(microns)

Sample Identity: GU/S-COI 12/1/96 - AverageGU/S AF 13/11/03 - AverageGU/S AF 17/12/03 - AverageGU/S COI 25/11/94 - AverageGU/S COI 1500-68 - Average

Analyst:

Date:

Initial Sample Weight:

2000

1400

1000 0 0 0 0 0

710 0 0 0 0 0

500 0 0 0 0 0

355 0.004014 4.675681 0 0.166187 0

250 0.098238 12.99385 0 2.363564 0

180 0.076329 17.273932 1.20501 5.030106 0

125 0.430358 15.738308 4.639825 7.028166 3.987615

90 3.035325 11.668246 8.123842 7.742225 11.953984

63 6.565712 8.669724 11.566525 7.740544 16.974026

44 10.052864 6.974656 14.529959 7.761027 17.514639

31 12.489373 5.337089 15.974208 7.953405 14.464243

22 13.148105 3.518258 14.819778 8.11849 10.116833

15.6 12.150551 2.175727 11.21269 8.095567 6.474093

11 10.123945 1.638512 6.764591 7.763282 4.220675

7.8 7.809071 1.602272 3.311222 7.045425 3.069594

5.5 5.718063 1.604948 1.53618 5.917719 2.45906

3.9 4.191694 1.466198 1.004309 4.608086 2.035881

2.76 3.313934 1.25158 0.977263 3.45722 1.691093

1.95 2.848277 1.035399 0.993582 2.61212 1.393471

1.38 2.515149 0.845574 0.940187 2.05931 1.147202

0.98 2.06598 0.64693 0.818224 1.618779 0.911418

0.69 1.569905 0.461032 0.685058 1.261867 0.711995

0.49 1.153072 0.313739 0.561466 0.993002 0.557877

0.35 0.612441 0.108344 0.330773 0.577167 0.31154

0.24 0.0276 0 0.005306 0.086743 0.004761

0.17

Print summary sheets for each sample?

Auto. add apertures at:

Enter your data in the columns below, and then click the

"Calculate Statistics" button. Enter Sample Info in the green cells.

GU/S COI 1500-68 b - AverageGU/S 1116 - AverageGU/S AF-2 12/1/75 b - AverageGU/S AF-1 12/1/75 - AverageGU/S 1501-68 - AverageGU/S 1501-68 b - Average

0 0 0 0 0 0.159655

0 0 0 0 0.02729 0.911598

0 0 0 0.124762 2.092459 3.649185

0 0 0 3.062886 7.077386 7.105325

0 0 0.065557 6.489483 9.012115 8.595659

0 0 1.767711 8.054194 7.541572 7.411983

3.604557 3.160243 4.100812 7.931425 5.285283 5.330171

11.495766 13.149202 6.172086 6.775624 4.468872 4.419444

16.642773 24.071202 7.420751 5.610307 5.21201 5.014277

17.477981 26.930851 7.861467 5.060921 6.274257 6.003723

14.714564 18.736395 7.967071 5.094733 6.777713 6.499957

10.473305 7.467365 8.131159 5.423187 6.690072 6.437459

6.748492 1.27392 8.417578 5.839905 6.346932 6.128438

4.344437 0.043211 8.565385 6.167096 5.933774 5.742582

3.083241 0.602259 8.259365 6.232338 5.433718 5.263824

2.438432 0.921616 7.325703 5.886676 4.762462 4.61913

2.033051 0.743472 6.012912 5.231523 4.019686 3.90446

1.717682 0.534033 4.765061 4.503152 3.382727 3.288541

1.435813 0.506627 3.774262 3.77232 2.856974 2.781564

1.18958 0.55053 3.041448 3.059401 2.377375 2.326399

0.946728 0.511933 2.368833 2.284067 1.817072 1.795226

0.740848 0.400658 1.785294 1.603087 1.282018 1.279635

0.581399 0.289489 1.344274 1.128058 0.882235 0.884398

0.326333 0.106993 0.749643 0.595343 0.439634 0.440971

0.005017 0 0.103629 0.069511 0.006364 0.006397

GU/S-COI 30/11/98 - Average

0

0

0

0.529458

0.854595

0.455526

0.747293

2.632942

5.928417

9.435427

11.869631

12.536753

11.572834

9.647779

7.543366

5.736241

4.467094

3.7471

3.327422

2.95619

2.398056

1.764383

1.23028

0.610376

0.008838

SAMPLE STATISTICS

ANALYST AND DATE:

SIEVING ERROR:

SAMPLE TYPE:

TEXTURAL GROUP:

SEDIMENT NAME:

METHOD OF MEAN

MOMENTS SORTING

Arithmetic (mm) SKEWNESS

KURTOSIS

METHOD OF MEAN

MOMENTS SORTING

Geometric (mm) SKEWNESS

KURTOSIS

METHOD OF MEAN

MOMENTS SORTING

Logarithmic (f) SKEWNESS

KURTOSIS

FOLK AND MEAN

WARD METHOD SORTING

(mm) SKEWNESS

KURTOSIS

FOLK AND MEAN

WARD METHOD SORTING

(f) SKEWNESS

KURTOSIS

FOLK AND MEAN:

WARD METHOD SORTING:

(Description) SKEWNESS:

KURTOSIS:

MODE 1 (mm):

MODE 2 (mm):

MODE 3 (mm):

MODE 1 (f):

MODE 2 (f):

MODE 3 (f):

D10 (mm):

D50 (mm):

D90 (mm):

(D90 / D10) (mm):

(D90 - D10) (mm):

(D75 / D25) (mm):

(D75 - D25) (mm):

D10 (f):

D50 (f):

D90 (f):

(D90 / D10) (f):

(D90 - D10) (f):

(D75 / D25) (f):

(D75 - D25) (f):

:)( as

:)( ax

:)( aSk

:)( aK

:)( gx

:)( gs

:)( gSk

:)( gK

:)( fx

:)( fs

:)fSk(

:)( fK

:)( ZM

:)( Is

:)( ISk

:)( GK

:)( GK

:)( GM

:)( Gs

:)( GSk

% GRAVEL:

% SAND:

% MUD:

% V COARSE GRAVEL:

% COARSE GRAVEL:

% MEDIUM GRAVEL:

% FINE GRAVEL:

% V FINE GRAVEL:

% V COARSE SAND:

% COARSE SAND:

% MEDIUM SAND:

% FINE SAND:

% V FINE SAND:

% V COARSE SILT:

% COARSE SILT:

% MEDIUM SILT:

% FINE SILT:

% V FINE SILT:

% CLAY:

Triangular Diagram

Sand Mud

Gravel

80%

30%

5%

Trace

1:1 1:9 9:1 Sand:Mud Ratio

Gravel %

Gravel

Muddy Gravel Muddy Sandy Gravel

Gravelly Mud Gravelly Muddy Sand

Slightly Gravelly

Mud

Slightly Gravelly Sandy Mud

Slightly Gravelly Muddy Sand

Mud Sandy Mud Muddy Sand Sand

Slightly Gravelly

Sand

Gravelly Sand

Sandy Gravel

Gravel:

Sand:

Mud:

0.0%

98.4%

1.6%

Coarse Gravel:

Medium Gravel:

Fine Gravel:

Coarse Sand:

Medium Sand:

Fine Sand:

Very Fine Sand:

Very Coarse Silt:

0.0%

0.0%

0.0%

30.4%

17.0%

5.8%

0.4%

5.8%

Very Coarse Gravel: 0.0%

Very Coarse Sand:

Coarse Silt:

Clay:

Fine Silt:

Very Fine Gravel:

39.3%

0.0%

Medium Silt:

Very Fine Silt:

0.4%

0.1%

0.1%

0.2%

0.3%

VF-74-103

TEXTURAL GROUP:

SEDIMENT NAME:

Sand

Poorly Sorted Very Coarse Sand

SAMPLE IDENTITY:

Triangular Diagram

Silt Clay

Sand

90%

50%

10%

1:2 2:1 Silt:Clay Ratio

Sand %

Sand

Clayey Sand Muddy Sand

Sandy Clay Sandy Silt Sandy Mud

Clay Mud Silt

Silty Sand

NOTE Gravel is also present in this sample

VF-74-103

TEXTURAL GROUP: IGNORING GRAVEL

FRACTION

SAMPLE IDENTITY: Gravel:

Sand:

Mud:

0.0%

98.4%

1.6%

Coarse Gravel:

Medium Gravel:

Fine Gravel:

Coarse Sand:

Medium Sand:

Fine Sand:

Very Fine Sand:

Very Coarse Silt:

Very Coarse Gravel:

Very Coarse Sand:

Coarse Silt:

Clay:

Fine Silt:

Very Fine Gravel:

Medium Silt:

Very Fine Silt:

0.0%

0.0%

0.0%

30.4%

17.0%

5.8%

0.4%

5.8%

0.0%

39.3%

0.0%

0.4%

0.1%

0.1%

0.2%

0.3%

Frequency Distribution Histogram

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

-2.0 0.0 2.0 4.0 6.0 8.0 10.0

Cla

ss w

eig

ht

(%)

Particle diameter (f)

Cumulative Frequency Curve

0

10

20

30

40

50

60

70

80

90

100

-1.0 1.0 3.0 5.0 7.0 9.0

Cu

mu

lati

ve m

ass r

eta

ined

(%

)

Particle diameter (f)

Frequency Distribution Curve

0

5

10

15

20

25

30

35

40

45

50

1.0 10.0 100.0 1000.0

Cla

ss w

eig

ht

(%)

Particle diameter (mm)

Cumulative Frequency Curve

0

10

20

30

40

50

60

70

80

90

100

1.0 10.0 100.0 1000.0

Cu

mu

lati

ve m

ass r

eta

ined

(%

)

Particle diameter (mm)

Documents

GRADISTAT-1 (Danau)