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IRON ORE MINERAL DEPOSITS EXPLORATION BY GROUND
MAGNETICS IN KINDANI AREA, MERU COUNTY, KENYA
BENSON MWIRIGI CYPRIAN [B.ED (Sc)]
I56/CE/24507/2012
A thesis submitted in partial fulfillment of the requirement for the award of the degree
of Master of Science in the School of Pure and Applied Sciences of Kenyatta University
November, 2016
iii
DECLARATION
This thesis is my original work and has not been presented for a degree in any other
University or any other award
Signature ………………………………… Date …………………………
Benson Mwirigi Cyprian
Department of Physics
Kenyatta University
We confirm that the work reported in this thesis was carried out by the candidate under
our supervision and approval as university supervisors
Signature …………………………………… Date …………………………
Dr. Willis Ambusso
Physics Department
Kenyatta University
Signature …………………………………… Date …………………………
Dr. John Githiri
Physics Department
Jomo Kenyatta University of Science and Technology
iv
DEDICATION
This thesis is dedicated to my mum Charity Karimi Ciauru and my dad Cyprian Ciauru
Anampiu who have made immense sacrifices to ensure I had all I needed to pursue
education to the highest possible echelons. I am eternally grateful.
v
ACKNOWLEDGEMENTS
I am grateful to God Almighty for the divine grace and enablement to pursue and
complete this work.
I sincerely appreciate my University supervisors Dr. Willis Ambusso (KU) and Dr.
John Githiri (JKUAT). You have patiently but firmly molded me in my academic
journey in the few months I have been under your supervision. I am grateful.
I am thankful to my friend Mr. Bonie Gitonga who helped me to get to the study area
and gave me important contacts. I am also grateful to my friend Mr. Nyamu Raria who
selflessly guided me in the Kindani area and rode me around each day on his bike
during my field survey. I also thank Mr. Kenneth Munene, for hosting me countless
times in his residence, and for encouraging me during my many trips to the University.
I also acknowledge my co-researcher Margaret Kebwaro with whom I started the
research at Kindani but who could not complete with me because her gravimeter broke
down. I know one day you will complete your work. I appreciate your encouragement
in the work. Special thanks to the Department of Infrastructure at Industrial area for
helping me do the chemical analysis.
May God bless you all, together with the many who I haven’t mentioned here who have
contributed to the success of this work.
vi
TABLE OF CONTENTS
Declaration…………………………………………………………………….……….. ii
Dedication……………………………………………………………………….…..… iii
Acknowledgements…………………………………………………………….…….... iv
Table of Contents……………..……………………………………………..…………. v
List of figures …………………..………………………………………………..….… vii
List of tables ……………………………………………………………………...…… ix
List of abbreviations………………………………………………...…………..………x
Abstract……………………..………………………………………………….…….… xi
CHAPTER 1: INTRODUCTION ................................................................................. 1
1.1 Background information ......................................................................................... 1
1.2 Geological setting ................................................................................................... 3
1.3 Statement of the research problem .......................................................................... 5
1.4 Objectives of the research project ........................................................................... 5
1.4.1 General Objective ................................................................................................ 5
1.4.2 Specific objectives ............................................................................................... 5
1.5 Rationale of the study ............................................................................................. 6
CHAPTER 2: LITERATURE REVIEW ...................................................................... 7
2.1 Introduction ............................................................................................................ 7
2.2 Iron ore forming processes ...................................................................................... 8
2.3 Ground magnetics survey theory ............................................................................. 9
2.4 The geomagnetic field .......................................................................................... 10
2.5 Elements of the geomagnetic field ........................................................................ 11
CHAPTER 3: MATERIALS AND METHODS ......................................................... 14
3.1 Introduction .......................................................................................................... 14
3.2 Survey equipment ..................................................................................................14
3.2.1 The Global Positioning System (GPS) ................................................................14
3.2.2 Flux-gate magnetometer .....................................................................................15
3.3 Methodology .........................................................................................................18
3.3.1 Data acquisation .................................................................................................18
3.3.2 Data processing ..................................................................................................19
vii
3.3.3 Data analysis...................................................................................................... 21
3.3.4 Reduction to the pole ........................................................................................ 21
3.3.5 Euler deconvolution .......................................................................................... 22
3.3.6 Forward modeling ............................................................................................. 23
3.3.7 Chemical analysis by energy dispersive spectroscopy (EDS) ............................. 24
CHAPTER 4 RESULTS AND DISCUSSIONS ..........................................................26
4.1 Introduction ...........................................................................................................26
4.2 Elevation of the Kindani Study area ...................................................................... 27
4.3Qualitative analysis of magnetic data ......................................................................28
4.4 Quantitative analysis ..............................................................................................31
4.4.1 Removal of regional trend ...................................................................................32
4.4.2 Euler deconvolution solutions and discussions ................................................... 35
4.4.3 Forward modeling results ................................................................................... 40
4.5 Chemical analysis results ...................................................................................... 45
4.5.1 Comparison of Kindani with Kimachia values ................................................... 48
CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS ................................ 50
5.1 Conclusions .......................................................................................................... 50
5.2 Recommendations ................................................................................................ 51
REFERENCES ........................................................................................................... 53
APPENDIX I: RAW DATA & DIURNAL CORRECTIONS TABLE ........................ 56
APPENDIX II: GEOMAGNETIC CORRECTIONS ...................................................59
APPENDIX III: BASE STATIONS (BS) DATA AND DIURNAL CURVES ............ 62
APPENDIX IV: PROFILE DATA ............................................................................. 66
APPENDIX V: ELEVATION DATA......................................................................... 73
viii
LIST OF FIGURES
Figure 1.1 Location of the study area ………...………………………………….…… 2
Figure 1.2 Geological map of the study area………………………………….……….4
Figure 2.1 Vector diagram on relationship between magnetization components……10
Figure 2.2 Elements of the geomagnetic field…………………………………...……12
Figure 3.1 Structural diagram of a flux-gate magnetometer…………………………..15
Figure 3.2 Working principle of a flux-gate magnetometer………………….……… 17
Figure 3.3 A diurnal curve graph…………………………….………….……….…… 20
Figure 3.4 Working principle of EDS……………………….……….……….………. 25
Figure 3.5 Example of a EDS spectrum ……………….……………………..……… 25
Figure 4.1 Distribution of the magnetic stations…………………………….……..… 26
Figure 4.2 Contour map of elevation of study area…………………………;………. 27
Figure 4.3 A 3-D topographic map of the study area……………….……..………… 28
Figure 4.4 A residual anomaly map of the Kindani area ………………….………….29
Figure 4.5 A 3-D Magnetic intensity surface map of the study area….…..………… 31
Figure 4.6 Profile cross-sections……………………………………….…..….……… 32
Figure 4.7 (a) Profile AA’ magnetic intensity trend……………….…………………. 33
Figure 4.7 (b) Profile BB’ magnetic intensity trend……………….………….……… 34
Figure 4.7 (c) Profile CC’ magnetic intensity trend………………….……….……… 34
Figure 4.7 (d) Profile DD’ magnetic intensity trend ……………….………….…….. 35
Figure 4.8 (a) Euler solutions along profile AA’ ………………………..…………… 36
Figure 4.8 (b) Euler solutions along profile BB’ ………………….……….………… 37
Figure 4.8 (c) Euler solutions along profile CC’ …………….……………….……… 38
Figure 4.8 (d) Euler solutions along profile DD’ …………………………….……… 39
Figure 4.9 (a) 2-D Modeling results along profile AA’ ……………………..………. 40
Figure 4.9 (b) 2-D Modeling results along profile BB’ ……………….……………. 41
Figure 4.9 (c) 2-D Modeling results along profile CC’ ……………...……………... 42
ix
Figure 4.9 (d) 2-D Modeling results along profile DD’ ……………………….……. 43
Figure 4.10 Rock samples from the Kindani study area …….………………..….… 45
Figure 4.11 Areas where the rock samples were collected …………………….…... 46
x
LIST OF TABLES
Table 3.1 Structural indices for different geological structures……………………. 23
Table 4.1 The I.G.R.F values for Kindani area ………………………..….……….. 36
Table 4.2 Summary of the 2-D modeling results ……………………….….….…… 44
Table 4.3 Chemical analysis results of Kindani area samples ………...….….……. 47
Table 4.4 Chemical analysis results of selected samples from Kimachia area.....… 49
xi
LIST OF ABBREVIATIONS
GDP - Gross Domestic Product
IGRF - International Geomagnetic Reference Field
BIF - Banded Iron Formations
nT - nano Tesla
2-D - Two dimension
3-D - 3 Dimension
GPS - Global Positioning System
RTP - Reduction To Pole
EDS/EDX - Energy Dispersive Spectroscopy
RMI - Residual Magnetic Intensity
SI -International System of Units
xii
ABSTRACT
Recent geophysical surveys have reported presence of iron ore deposits within Meru
County. It has been speculated that there could be more deposits within the region.
Ground magnetic surveying was used to detect magnetic rocks within host formations
in Kindani area of Maua. A fluxgate magnetometer was used to measure the vertical
component of the Earth’s magnetic field in some 98 stations, covering an area of about
25 km2. Diurnal and geomagnetic corrections were then done on the data. A contour
map that delineates anomalies in the study area was generated using Surfer 10 software.
The map shows varied anomalies spread out within the region. The anomalies mostly
trend on NW-SE and SW-NE directions. Four cross sectional profiles were drawn
across various anomalies and the digitized data used to draw 2-D line graphs. The data
obtained was used in 2D modeling using Euler software which gives estimated depths
to magnetic structures at between 0m-1500m. Mag2dc modeling gives bodies of
susceptibility between -1.724 SI to 1.7624 SI. The depth to top of magnetic structures
ranges from 0 m to 136 m, which indicates shallow structures. A chemical analysis of
some rock samples indicates quantity of Fe2O3 at an average of 25%. From the study,
there is confirmation of iron ore deposits in the region, which confirms presence of
extended iron deposits within Meru County. There is need to survey the entire Kindani
plains, using different geophysical methods to delineate more deposits for possible
exploitation
1
CHAPTER 1: INTRODUCTION
1.1 Background information
An initial reconnaissance geological survey by Rix (1967) acknowledges presence of a few
minerals but notes however, that the minerals were all present in very small quantities and
were unlikely to prove of any economic value. An initial geological reconnaissance survey of
the Meru region by Mason (1955) indicates that no mineral deposits of importance were
found in the area and therefore no intensive prospecting was recommended.
However, recent surveys by Abuga (2013) and Kassim (2014) confirm presence of
significant deposits of iron ore in the Kimachia area in Meru County. Chemical analysis
results revealed over 90% iron content in some rock samples. They speculated that these
deposits could be part of more deposits within the region. Rock samples collected in the
Kindani area show greater potential for iron due to their high magnetic properties. This
therefore necessitates more research. This study sought to confirm presence of more iron
deposits in Kindani area which could confirm presence of an iron rich belt as suggested by
Abuga et al. (2013).
The intended survey was undertaken by Ground magnetics. Magnetic surveys are used to
investigate subsurface geology on the basis of anomalies in the Earth’s magnetic field (Keary
et al., 1984). The anomalies are caused by local bodies which cause magnetic highs and lows
compared to the values of the earth’s geomagnetic field predicted at a particular place by the
International Geomagnetic Reference Field (IGRF).
The Kindani area is about 7 kilometers from Maua town and North East of Meru town. The
survey area covered the small markets of Kilili, Ndila, Junction and Kindani. The surveyed
area is shown in figure 1.1
2
Fig 1.1: Location of Kindani study area. Simplified from Kenya Government maps (1983).
Map of Irereni, Sheet 109/3.
3
1.2 Geological setting
The area under study is bound by the longitudes 38000’ E and 38
005’ E and latitudes 0
005’
and 0015’. Kindani area lies within the outskirts of Nyambene ranges. The Nyambene
volcanic range is stretches in a north-east to south west direction from the foothills of Mt.
Kenya and rises to an elevation of 7000 feet. Rocks in the Nyambene volcanic series are
young Tertiary, Pleistocene and recent extrusive rocks and subordinate sediments. The
Pleistocene-recent lava is mostly olivine basalts. The basement system metamorphic rocks
comprise gneisses, plagioclase amphibolites, crystalline limestone and quartizes (Rix 1967).
A geological map of the area is shown in figure 1.2
Earlier tests for valuable minerals were negative, but magnetite was found to be present in
some concentrates examined (Rix, 1967) albeit in small quantities. In the recent times
however, studies in the adjacent areas by Kenyatta university students reveals that the area
could have considerable amounts of iron ore (Abuga et al., 2013; Kassim, 2014). Samples
collected in Kindani area were found were found to be highly magnetic and with a high
specific density which indicates a high prospect for iron ore.
4
Figure 1.2: Geological map showing study area and adjacent regions (Rix, 1967)
5
1.3 Statement of the research problem
Recent geophysical studies have revealed presence of iron ore deposits in the Kimachia
region of Meru County. There has been need therefore, to establish if the deposits cover a
wider region. Preliminary rock samples collected from Kindani indicates that the area indeed
has potential for iron ore deposits. This study was therefore undertaken to investigate for
more deposits in the area. In addition, Kenya has an increasing demand for iron for industrial,
construction and infrastructural development. A lot of foreign exchange is lost every year in
importing large amounts of iron and iron products into the country. To facilitate the much
needed development in the country, local and cost effective iron will be needed. The results
from the study will provide a basis for making recommendations for possible exploration for
iron ore in the area.
1.4 Objectives of the research project
1.4.1 General Objective
The main objective of this study is to carry out a ground magnetic survey in the Kindani area
to locate areas of possible iron ore deposits and the extent of coverage in the region.
1.4.2 Specific objectives
To carry out magnetic measurements and reduce the magnetic data of the Kindani study area
To image depths to any magnetic sources using Euler deconvolution technique
To generate forward models of the magnetic data that quantify size and extent of any
detected magnetic bodies that may be iron ore
To determine mineral compositions of rock samples from the study area using chemical
analysis
6
1.5 Rationale of the study
Ground magnetic surveys are regularly used for the direct detection of mineralizations such
as skarns, massive sulphides, iron ore deposits, kimberlites and others. Ground magnetics is
frequently preferred because relative to other geophysical methods, acquisition of magnetic
data is rapid and cost effective, especially when surveying small areas such as Kindani. Due
to recent enhancements in instrumentation and data filtering, processing and display
methods, magnetic data is rapidly processed and interpreted. Maps of magnetic anomalies
often reveal magnetic signatures that often reveal regions of mineralization. This research
therefore provided information about iron mineralization in Kindani and also provided
information useful in updating the geology of the area.
7
CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
The magnetic method is one of the oldest methods of geophysical exploration (Gunn and
Dentith, 1997). Esperson (1997) stated that magnetic surveys were used in Sweden since mid
17th Century. Recent improvements in instrumentation and navigation make it possible to
map crustal sections at various scales. In addition, magnetic method is usually the primary
tool in search for minerals. It is used for mapping basement structures, defining lithologic
contacts, locating intrasedimentary faults among other uses. The method has increasingly
been used in different realms of exploration such as search for minerals, oil and gas,
geothermal resources, ground water and natural hazards assessment (Nabighian et al., 2005).
The magnetic method mainly utilizes the difference in magnetic susceptibilities of minerals
and the host rocks. In iron exploration, for example, the magnetic signature of the iron
formation is usually one or two orders of magnitude greater than that of the rock where the
deposit is found.
Magnetic surveying has been used in the Albuquerque basin to map aquifers. Magnetic maps
in the area showed intrabasin faults and buried igneous rocks (Grauch et al., 2001).
In Kenya, magnetic surveying has widely been used as a reconnaissance survey method in
geothermal exploration. Adero et al. (2014) used the magnetic method to determine the
subsurface structure of the Homa-Hills geothermal prospect area. They successfully used the
method to delineate geothermal heat sources in the study area. Many other studies have been
carried out in different geothermal prospects in the country and especially within the Rift
valley.
8
In Northwestern Tasmania, magnetic surveying played a key role in delineating the massive
Savage River magnetite deposit (Eadie, 1970). The deposit is thought to have a magmatic
origin (Coleman, 1975). It is associated with a magnetic anomaly of more than 10, 000 nT.
Kerr et al., (1994) describes the magnetic signatures of BIF hosted iron ore deposits from the
Pilbara region. Structural and stratigraphic control of the mineralization is important here and
the mapping of the appropriate stratigraphic horizons and identification of structures such as
folds and faults are important in interpretation of magnetic data (Gunn and Dentith, 1997).
The ore appears as zones of reduced magnetic intensity within the magnetic BIF zones.
Kassim (2014) successfully used the ground magnetics geophysical method to delineate
regions of small scale iron ore deposits in Kimachia area. The study, carried out on the
eastern parts of Nyambene ranges confirms of small scale iron deposits. The deposits are
suspected to be part of a larger iron rich zone (Abuga et al., 2013). Rix (1967), from the
geological survey of the Kinna area confirms presence of small quantities of garnets and
magnetite in the region. These occurred at MelkaLorni and Kalusikaumu. He however noted
that they were unlikely to prove of value. The work was however for general mapping
purposes and therefore no extensive geophysical studies were carried out.
2.2 Iron ore forming processes
Iron ore, is formed in the continental crust by a variety of ways. This may involve
sedimentation, igneous activity or by physical weathering. Bedded sedimentary iron ore
deposits are thought to occur as a result of mineral precipitation from solutions present
during the Precambrian period (2.6 to 1.8 billion years ago). Sedimentation may lead to
formation of Banded Iron Formations (BIF) or they may occur as ironstones. Iron content in
BIFs may range from 25% to 40%. Ironstones are formed due to intense weathering of
continental crust. They contain about 20-40% iron (Bett and Maranga, 2012).
9
They may occur as pellets of limonite, hematite or chamosite. Iron ore formation by igneous
activity is mostly by magmatic segregation. The deposits may occur as magnetite, hematite
or ilmenite. When Iron bearing minerals decay physically or chemically, they may lead to
concentrations of iron oxides (Bett and Maranga, 2012).
In Kenya, Iron ore occurs majorly in Taita, Kitui, Tharaka-Nithi and Siaya-Samia Hills. Bett
and Maranga (2012) classify the ores found in Kenya into 4 classes: the magmatic,
sedimentary, detrital and residual. The magmatic occur within the basement system rocks and
occur majorly in Western, Eastern and North-Eastern parts of the country. The sedimentary
occur as Archeans chists in Voi and as Precambrian banded iron stones in Nyanza. The
detrital occur as black sands at the coast and the residuals occur as lateric iron stones mainly
in western Kenya.
2.3 Ground magnetics survey theory
The aim of magnetic surveying in general is to investigate subsurface geology on the basis of
anomalies caused in the earth’s magnetic field (Keary et al., 1984). It’s used for detailed
mapping in order to understand the subsurface geology of an area (Kayode et al., 2010).
Exploration for iron based on its magnetic effect represents the earliest use of geophysics in
mineral exploration (Gunn and Dentith, 1997). It’s used to locate places with unusual
magnetizations mainly due to locally buried bodies. Rocks usually contain magnetic minerals
of different quantities and different magnetic susceptibilities. A material placed in a
magnetic field may acquire magnetic properties which it may later lose (induced magnetism)
or it may acquire magnetism that then becomes permanent (remanent magnetism). It is this
induced or remanent magnetism that eventually causes anomalies in the measured magnetic
field at a location. The extent of the magnetization depends on the magnetic susceptibility of
the rock. The induced magnetization Ji intensity is proportional to the magnetizing force (H).
10
kHJ i
(2.1)
Where k is the magnetic susceptibility of the material.
A rock containing magnetic minerals may have both induced (Ji) and remanent (Jr)
magnetism. The resultant magnetic vector (J) has a magnitude that affects the amplitude of
the magnetic anomaly and its orientation affects the shape of the anomaly. A vector diagram
of the 3 vectors is as shown below.
Ji
Jr J
Fig 2.1 Vector diagram on the relationship between Induced (Ji) remanent (Jr) and total (J)
magnetization components (Keary et al., 1984)
Ground magnetics are usually carried out over small areas. In carrying out a ground magnetic
survey, the 3 components of the magnetic field may be measured. These are the vertical,
horizontal or the total component. The vertical component and the total components are the
most widely used in past studies to delineate faults, depth to magnetic basements and other
geological structures (Folami, 1992).
2.4 The geomagnetic field
The earth’s main magnetic field is believed to be produced by the fluid outer core of the
earth. The major constituent of the fluid core is believed to be chiefly iron, with small
percentages of nickel and some non-metallic light elements such as silica, sulfur or oxygen.
The liquid cools on the outside, becomes denser and therefore sinks towards the inside of the
outer core. It is then replaced by warmer less dense fluid. As a result of these movements,
11
convection currents are produced by the liquid metallic matter which moves through a weak
cosmic magnetic field and which subsequently generates induction currents (Nettleton,
1976). It is these induction currents that then generate the earth’s magnetic field (Telford et
al., 1976). The rest of the geomagnetic field is caused by electric currents in the ionized
layers of the upper atmosphere. These appear in the form of diurnal variations, lunar
variations and magnetic storms. Diurnal changes are the changes on the field on a daily basis
with amplitudes of between 20 – 80 nT (Keary et al., 1984). These changes necessitate
diurnal corrections of magnetic data when carrying out magnetic surveys. Magnetic storms
may cause disturbances of up to 1000 nT. Magnetic storms occur when charged solar
particles enter the earth’s ionosphere. If a magnetic storm is detected, in the process of a
magnetic survey, the survey has to be discontinued because the values obtained in such a
study would be grossly misleading. The total geomagnetic field ranges from 25000 nT in
magnitude to about 65000 nT. The intensity of the geomagnetic field decreases from the
poles towards the equator. Changes in the geomagnetic field occur continually, both over
short periods as well as over long periods. The changes in variation of a year or more are due
to changes in the conductive core (secular variation).
2.5 Elements of the geomagnetic field
The earth’s magnetic field is a vector. The magnetic elements are used to describe the field
which has different components. If a magnet is freely suspended, its north pole points to the
magnetic north of the earth. The magnetic north lies at an angle to the geographic north of the
earth. The geomagnetic elements are as illustrated in figure 2.2 below.
12
Fig 2.2 Elements of the geomagnetic field (After Lowrie, 1997)
Where:
Z is the vertical component of the magnetic field.
H is the horizontal component which is the magnetic meridian (magnetic north).
D is the angle of declination (angle between the magnetic meridian and the geographic
meridian).
I is the angle of inclination (angle by which the total magnetic vector F dips from the
horizontal).
F is the total magnetic field (sum of the horizontal and vertical components of the
geomagnetic field).
X is the direction of the geographic north and
Y is the East direction
13
These geomagnetic elements are related to each other by these equations:
DIFX coscos (2.2)
DIFY sincos (2.3)
IFZ sin (2.4)
2222 ZYXF (2.5)
X
YD arctan (2.6)
22arctan
YX
ZI (2.7)
14
CHAPTER 3: MATERIALS AND METHODS
3.1 Introduction
In this study a Fluxgate magnetometer was used to measure the vertical component of the
magnetic field in each station. Its working principle is discussed in this chapter. Data
collection, reduction and the process of analysis is also discussed. Energy Dispersive
Spectroscopy is the method used for chemical analysis of the rock samples. A brief
description of the process has also been discussed.
3.2 Survey equipment
3.2.1 The Global Positioning System (GPS)
This is a satellite based navigation system made of 24 satellites that orbit the earth. The
system was invented by the US Department of Defense for military use but was later allowed
for civilian use. The satellites circle the earth about every 12 hours in a precise orbit and
continuously transmit signal information to the earth. They orbit the earth at about 12,000
miles, travelling at about 7,000 miles every hour. Each satellite weighs about 2,000 pounds
with a span of about 17 feet.
GPS receivers on the earth receive signals from the satellites and then use trilateration
method to calculate the user’s exact position. To calculate the latitude and longitude (2-D)
position of a user, the GPS receiver must connect to at least 3 satellites. The satellite orbit the
earth in such a way that at least four of them can be located from any point on the surface of
the earth. To determine a 3 dimensional location (latitude, longitude and elevation) the
receiver must locate at least four satellites.
The advantage of using the GPS system for positioning is that it works well in different
weather conditions, in any part of the world. It is available for free as long as one has a GPS
15
receiver. The signal easily penetrates clouds, plastic and glass. However, it may not penetrate
thick solid objects such as mountains or concrete. The system therefore is commonly used for
field positioning as it may not work well inside buildings, in caves or in water.
Some mobile phones today are GPS enabled and they have applications that enable GPS
positions to be read. In this study, a handheld Garmin Etrex GPS machine was used.
3.2.2 Flux-gate magnetometer
The flux-gate magnetometer is made up of two parallel cores of high susceptibility, such that
they can be magnetized by the geomagnetic field.
Fig 3.1 Structural diagram of a flux-gate magnetometer (After Reynolds, 2011)
The primary coils are wound such that when a primary current is passed through them (Fig
3.2A) they get magnetized in opposite directions. A secondary coil, wound on the primary
coils detect the changes in the magnetic flux in the primary coils. As a result of the changing
flux, a voltage is then induced in the secondary coils. In the absence of an external field, the
coils will saturate every half cycle (Fig 3.2B). The voltages induced in the secondary have
S N
16
opposite polarities because the coils are wound in opposite directions. This yields zero net
voltage (Fig 3.2C). In the presence of a magnetic field however, the component of field
parallel to the cores causes one core to saturate before the other. The voltages now therefore
do not cancel out and we get a net voltage (Fig 3.2 D, E). The output voltage is calibrated in
terms of magnetic field since its proportional to the magnetic field strength. The flux-gate
magnetometer has an accuracy of about 1 nT (Keary et al., 1984; Lowrie, 1997)
17
Fig 3.2 Working principle of a flux-gate magnetometer (After Reynolds, 2011).
18
3.3 Methodology
3.3.1 Data acquisition
Magnetic surveying aims at locating rocks or minerals with anomalous magnetizations which
reveal themselves as anomalies in the intensity of the earth’s magnetic field (Abdelraham and
Kassa, 2005; Adagunodo et al., 2012). The ground magnetics survey that was carried out in
Kindani region aimed at locating possible sources of iron ore in the region. Magnetic
surveying involves 3 major steps:-
i) Measuring the terrestrial magnetic field at some predetermined points.
ii) Correcting the magnetic data for known changes.
iii) Comparing the resultant value of the field with the expected value at each measurement
station (Lowrie, 1997).
The expected of the field is the value given by the International Geomagnetic Reference
Field (I.G.R.F) the difference between the observed values and the I.G.R.F values gave the
magnetic anomaly which was then appropriately processed and interpreted.
A fluxgate magnetometer was used to measure the vertical magnetic intensity at each station.
A total of 98 magnetic stations were established in the area. A base station was also
established and severally occupied during the day so that its values would be used for diurnal
corrections. A total of 4 readings were taken in each station and the average obtained. This
practice enhances accuracy of the data (Maunde et al., 2013). In addition other readings that
were taken include elevation at each station, time of taking magnetic readings and the
position (Easting & Northing) of each of the stations.
19
During data acquisition, all magnetic objects including mobile phones, metallic belts were
kept away. All stations were situated at a safe distance of about 50m from concrete buildings
and power lines to enhance integrity of the magnetic data.
For a small target area such as Kindani, ground magnetics was preferred because it gives a
detailed pattern of the study area over the region since the measurements were taken close to
the anomaly. The survey stations were distributed along pre-determined positions in the
region. Exceptions were made where extreme terrains were encountered. Stations were
spaced about 500m apart. They were located at least 50m away from tarmac roads and from
buildings to reduce the chance of interference from unwanted magnetic fields.
3.3.2 Data processing
The first step in data processing was applying corrections to the collected magnetic data. The
following corrections are usually carried out on magnetic data:-
i) Diurnal corrections
This is the correction that is necessitated by the changes in the intensity of the geomagnetic
field in the course of the survey. These changes occur due to interferences from the
ionosphere. This correction was applied by taking readings every hour or two at the base
station within the survey area, and the drifts removed at the end of each day. If the readings
were found with very huge differences, they were interpreted to have been caused by
magnetic storms.
To make this correction a diurnal line graph (Fig 3.3) was drawn using magnetic data
collected at the base station. Readings were referenced to the first value taken at the base
station.
Corrected Value = Observed value ± diurnal corr.(∆d)
20
Fig 3.3 A diurnal curve graph
In the figure 3.3 above for example, between 9.30 am and about 12.00 noon the reading at
the base station dips. Readings taken at other stations at this time therefore must be lower
than the observed values. The diurnal correction is therefore subtracted from the observed
values. Readings taken between 12 noon and 1am should be higher than the observed values
and therefore the diurnal difference is added. The diurnal graph curves of the work
undertaken in Kindani area are shown in appendix III.
ii) Geomagnetic corrections
This is the correction which removes the effects of a reference geomagnetic field from the
survey data (Keary et al., 1984). This correction was done by subtracting values given by the
IGRF from the magnetic data obtained after doing diurnal corrections. The IGRF values for
the stations and the geomagnetic corrections done are shown in appendix II. These values
10220
10240
10260
10280
10300
10320
10340
10360
9 10 11 12 13 14 15 16 17
MA
GN
ETIC
INTE
NSI
TY
TIME (HRS)
Series1
∆ d
21
were obtained from public domain software mathematical models. The inputs are latitude,
longitude elevation and the date of the observation (Maunde et al., 2013).
iii) Terrain and elevation corrections
These are correction of altitude. However this correction was unnecessary because the
vertical gradient is 0.03nTm-1
at the poles and -0.015nTm-1
at the equator (Keary et al.,
1984). This difference is insignificant and therefore elevation and terrain correction were not
applied to this data.
3.3.3 Data analysis
There are several methods of presenting magnetic data (Kayode et al., 2010; Obot and
Wolfe, 1981). Presentation involved drawing magnetic contour maps and using traverses to
draw magnetic profiles. Depth estimates were imaged using Euler Deconvolution software
developed by Cooper (2004).
3.3.4 Reduction to the pole
This is a data processing technique that recalculates the total magnetic intensity data as if the
inducing field had a 900 inclination. The acquired anomaly is therefore one that would be
measured at the north magnetic pole, where induced magnetization and ambient field are
directed downwards (Blakely, 1995; Githiri et al., 2011). The major effect of this
transformation, and which makes interpretation much easier, is that dipolar anomalies are
transformed to mono polar anomalies. These single pole anomalies are usually centered over
the causative magnetic subsurface bodies.
Reduction to the pole is usually unreliable at low magnetic latitudes where northly striking
magnetic features have little magnetic expression. Some bodies have no detectable magnetic
anomaly at zero inclination (Blakely, 1995; Githiri et al., 2011). Another disadvantage of
22
using RTP to transform magnetic data collected very close to the magnetic equator is that a
large correction would need to be made for the amplitude of anomalies. Validity of reduction
to the pole is therefore advised only for declinations greater than 100.
In the Kindani survey area declination was -19.70 and therefore reduction to the pole was
considered a reliable method to transform the magnetic data. The software Euler was used in
reducing to the pole of the profile data. It was used to delineate areas of magnetic sources and
their approximate depths. The software also calculates and displays variations in the
horizontal and vertical gradients of the magnetic data along the profiles.
Euler deconvolution is advantageous in that it provides a fast method to image approximate
depths to anomalous magnetic bodies. The identified locations and depths to the causative
bodies are independent of magnetization directions or distortion of field caused by remanent
magnetization (Githiri et al., 2011). The kind of magnetic features can also be inferred from
the optimum structural index selected.
3.3.5 Euler deconvolution
Euler deconvolution is a technique which uses potential field derivatives to image subsurface
depth of a magnetic or gravity source (Hsu, 2002, Githiri et. al., 2011). Euler deconvolution
is expressed as:-
TBN
zTzz
yTyy
xT
000x-x (3.1)
Applying the Euler’s expression to profile or line oriented data (2D source), x-coordinate is a
measure of the distance along the profile and y-coordinate is set to zero along the entire
profile (Adero B et al., 2014). Equation 3.1 is then written as:-
TBN
zTzz
xT
00x-x (3.2)
23
where:
x0, z0 is the coordinate position of the top of the body
Z is the depth measured as position down
X is the horizontal distance
T is the value of residual field.
B is the value of the regional field
N is the structural index which is a measure of fall-off rate of the magnetic field. It depends
on the geometry of the source (El Dawi et al., 2004; Adero et al., 2014).
The structural indices for different possible geological structures (Reid et al. 2011) are as
follows.
Table 3.1 Structural indices for different geological structures
Structural Index Geological Structure
0 Contact
0.5 Thick step
1 Sill/Dyke
2 Vertical pipe
3 Sphere
3.3.6 Forward modeling
Forward modeling involves determination of parameters like geometry, depth, and density
contrast. This is a trial and error method that is used to obtain the best fit to the observed
anomalies. It involves constructing an initial model for the source body using prior
geological knowledge of the area. Adjustments are then made to the model until an
24
acceptable fit is obtained. This was done using mag2dc computer software program
developed by Cooper (2004). Description of the method of the program mag2dc can be found
in the work of Talwani and Heirtzler (1964).
3.3.7 Chemical analysis by energy dispersive spectroscopy (EDS)
Energy dispersive spectroscopy is an analytical technique that can be used to determine
elemental composition of a sample or for chemical characterization. In this technique, a beam
of high energy particles such as electrons or protons is directed to a sample. High energy x-
rays may also be used.
An atom is made of a dense nucleus which is surrounded by electrons orbiting in energy
levels (energy shells). The shells are usually labeled K, L, and M beginning with the
innermost shell. An atom will usually have unexcited electrons. When a beam of high energy
x-rays hit the electrons, it excites them. The electrons in the inner shells may get dislodged
from their shells as a result of the excitation. These then leave electron holes in these inner
shells. When this happens, the atom becomes unstable. To regain stability, electrons from the
outer shells must move to refill the inner shells. However, electrons in outer shells have
greater energy than those from the inner shells. An electron that moves to the inner shell
must therefore lose some energy. This energy is lost in form of x-rays. These x-rays emitted
from the sample atoms are characteristic in energy and wavelength both to the element of the
parent atom and also to the particular shell that released them. An EDS detector can therefore
be used to separate these characteristic x-rays of different elements into an energy spectrum.
An EDS computer software system is then used to analyze the energy spectrum in order to
determine the abundance of each specific element.
25
Fig 3.4 Working principle of Energy Dispersive Spectroscopy (Simplified from Heath, 2015)
An EDS spectrum is usually portrayed as a plot of x-ray counts vs. energy in KeV as shown
in figure 3.5. The energy peaks correspond to the various elements in the sample and can be
analyzed from the energy spectrum.
Fig 3.5 Example of EDS spectrum, (Goldstein et. al., 2003)
26
CHAPTER 4: RESULTS AND DISCUSSIONS
4.1 Introduction
A total of 98 magnetic stations (locations where magnetic readings were taken) covering an
area of about 25km2 were established. The surveyed area was bound by Northings 19000-
24000 and Eastings 389000- 394500. The distribution of the stations is as shown in figure
4.1 below.
Fig 4.1 Distribution of the magnetic stations
27
4.2 Elevation of the Kindani Study area
Figure 4.2 below is a contour map showing the topography of the area. The elevation data
collected from the field (shown in appendix V) was used to plot the contour map. The blue
and black colour shows the areas with the lowest elevations while the hotter colors of red and
yellow indicate the regions that rise the highest within the region. The area generally slopes
from the North West towards the South East. However, apart from the small hill at the North
Western part of the map, the area slopes gently towards the East. The highest point rises to
about 1140m while the lowest point slopes to about 760m above the sea level.
Fig 4.2 A contour map of the elevation of the study area
Meters
28
The topography of an area is important because sometimes minerals occur in hilly areas of a
place, or at the low lands. The elevation map would therefore help to indicate whether there
is a relationship between the Kindani terrain and the deposits. A 3-D topographical map of
the area is shown in figure 4.3 below.
Fig 4.3 3-D topographical map of the study area
4.3 Qualitative interpretation of magnetic data from Kindani area
The residual magnetic intensity data obtained after doing diurnal and geomagnetic
corrections was used to draw a contour map (Fig 4.4). The data used is shown in appendix II.
29
Fig 4.4 A residual anomaly map of the Kindani area
Qualitative interpretation of a magnetic map begins with a visual inspection of the shape,
trend of the major anomalies, and examination of the characteristic features of each
individual anomaly. Such features may include the relative locations and amplitudes of the
positive and negative parts of the anomaly and the aerial extent of the contours and sharpness
of the anomaly, as distinguished by the spacing of the contours (Nettleton 1976; Selim and
Aboud, 2013).
nT
30
Figure 4.4 above shows a color range of magnetic residual anomaly values, with red as the
highest and blue-purple being the colors with the least values. The highest anomaly rises to
about 1600 nT while the lowest values are at about -3000nT.
The anomalies in the region show 3 major orientations: SE-NW, SW-NE and the E-W
orientations. The longest positive anomaly, marked A is elongated on a SE-NW direction. It
has values that rise to about 1600 nT on the lower end and to about 200 nT on the upper end.
The high amplitudes suggest near surface magnetized bodies. Such high values are usually
characteristic of highly magnetized ores such as those containing high magnetite content.
Anomaly B is a magnetic high circular anomaly appearing at the center of the study area. It
also rises to a high of about 1600 nT.
Anomalies marked E and F are negative anomalies with anomaly F getting to a low of up to -
3000nT. The anomalies marked E have a SW-NE orientation. The anomaly F has a SE-NW
orientation which is a common trend with the other major anomaly in the neighborhood,
anomaly A. Since these negative anomalies occur near the positive anomalies, they could be
the negative poles on the same bodies since magnetism is a dipolar quantity.
The anomalies D, G and H represent more subdued highs. These lower amplitude anomalies
suggest deeper buried bodies which may be of volcanic origin. Area C and the lower parts of
H are more magnetically quiet areas and suggest absence of highly magnetized bodies. These
areas seem to have homogenous non-magnetic material.
Figure 4.5 is a 3-D map of the magnetic intensity variations of the study area.
31
Fig 4.5 A 3-D Magnetic intensity surface map of the study area.
4.4 Quantitative analysis
Quantitative analysis in this study involved interpretation of profile data and forward
modeling. Four profiles AA’, BB’, CC’ and DD’ were chosen, cutting across major
anomalies observed in the study area. Each profile was chosen to cut through both magnetic
highs and adjacent lows because magnetism is a dipolar quantity. Anomalous objects are
therefore expected to show both positive and negative poles in observed data. The profiles
are illustrated in figure 4.6 below.
EASTINGS NORTHINGS
nT
32
Fig 4.6 Profile cross-sections
4.4.1 Removal of regional trend
Before profile data can be used in further analysis, it is important to remove the regional
trend from the data. Magnetic anomaly fields are often characterized by a smoothly varying
field due to magnetic response of large scale background structures such as basement rocks.
The local anomalies are superimposed on this regional field. In this study, the interest was
the near surface anomalies that have short wavelength and high frequency. It is therefore
necessary to remove the longer wavelength, low frequency anomaly. Since the data being
considered here is 2-dimensional profile data, trend removal was reliably done graphically.
nT
33
Linear trend analysis was done and the regional field subtracted from the corrected magnetic
data using the equations below for profiles AA’, BB’, CC’ and DD’ respectively.
472.85 -0.089x - = Y (4.1)
695.99 -0.0022x = Y (4.2)
1105.09 -0.009x =Y (4.3)
1507.85 -0.134x = Y (4.4)
The profile trends are illustrated in figure 4.7 (a) – 4.7 (d). The red graphs represent the
graphs with regional trends while the blue graphs show the graphs without the regional
trends. The profile data is shown in appendix IV.
Fig 4.7 (a) Profile AA’ magnetic intensity trend
-2000
-1500
-1000
-500
0
500
1000
1500
2000
-1000 0 1000 2000 3000 4000 5000 6000
Mag
net
ic In
ten
sity
(nT)
Distance (m)
Without trend
With trend
34
Fig 4.7 (b) Profile BB’ magnetic intensity trend
Fig 4.7[c] Profile CC’ magnetic intensity trend
-2500
-2000
-1500
-1000
-500
0
500
1000
1500
2000
-1000 0 1000 2000 3000 4000 5000 6000
nT
Distance (m)
Without trend
With trend
-3000
-2000
-1000
0
1000
2000
3000
0 2000 4000 6000 8000
nT
Distance (m)
Without trend
With trend
35
Fig 4.7 (d) Profile DD’ magnetic intensity trend
4.4.2 Euler deconvolution solutions and discussions
Euler 1.0 software was used to map the depth to subsurface magnetic structures in the survey
area. A structural index of 1 was adapted for this work as being the one best representing the
structural formations of the area. Estimating the depth to the anomaly also involved reduction
to the pole, calculation of the vertical and horizontal gradients of magnetic field data,
choosing window sizes and the structural index (Adero et al., 2014). A window size of 11
was chosen, with a X-separation of 255.71 m and Y separation of 127.86m.The I.G.R.F
values used for this area are shown below.
-3000
-2500
-2000
-1500
-1000
-500
0
500
1000
0 1000 2000 3000 4000 5000
nT
Distance (m)
Without trend
With trend
36
Table 4.1 the I.G.R.F values for Kindani area
Component Field Value
Declination 0.67 degrees
Inclination -19.828 degrees
Vertical Intensity (Bz) 11455 nT
Total Intensity 31770 nT
Fig 4.8 (a) Euler solutions along profile AA’
From the profile AA’ (Fig. 4.8 a), Euler solutions suggest shallow magnetic structures at just
below the surface to a maximum depth of less than a km below the surface. The RTP curve
rises to its highest at a distance of 4000 m along the profile and is lowest at 1500m. The high
37
suggests a source of high magnetic susceptibility relative to host rocks while the low may
suggest rocks of lower susceptibility.
From profile BB’ (Fig. 4.8 b) solutions clusters occur near surface and between distances
750m to about 1750m along the profile. They also occur at 2250 m and between 4600 -
5000m along the profile. There is an abrupt change in vertical and horizontal gradients at
2250m. This also corresponds to an abrupt change in the RTP data curve outline. This point
also corresponds to one of the near surface Euler solution clusters. A discontinuity at a
distance of 4500m suggests presence of faulted structure.
Fig 4.8 (b) Euler solutions along profile BB’
38
On profile CC’ (Fig. 4.8 c), Euler cluster solutions occur less than 100m below the surface at
250m, 1000m and at 2500m along the profile. The deepest solution clusters occur at 530m.
These indicate presence of shallow magnetic structures which could be iron ore bodies. The
solutions at 250m and 1000m along the profile coincide with areas of abrupt changes in
horizontal and vertical gradients. These may represent abrupt lateral change in magnetization
relative to host rocks. Solutions at 4250m coincide with a point of inflection on the RTP
curve which may indicate the top of a magnetic body. There is also a rapid fall of both
vertical and horizontal magnetic gradients at 2500m. This indicates a rapid change in
magnetism relative to host rocks.
Fig 4.8 (c) Euler solutions along profile CC’
39
Fig 4.8 (d) Euler solutions along profile DD’
The Euler deconvolution solutions for profile DD’ (Fig. 4.8 d) indicates presence of magnetic
sources at a shallow depth of about 200m below the surface. The deepest sources occur at a
depth of 900m. These solutions occur at 800km, 2750m and at 3750m. These indicate
relatively shallowly buried magnetic bodies. The RTP curve dips deepest at 900m along the
profile and rises highest at about 2800m which indicates low and high magnetic
susceptibility bodies respectively.
40
4.4.3 Forward modeling results
Forward modeling was done using mag2dc software. The results obtained from Euler
deconvolution were used as start parameters in modeling. The program is based on Talwani
algorithm and allows manipulation of parameters like magnetic susceptibility, shape, and
depth until a best fit of calculated values to the observed values is obtained. The system uses
the method of least squares that gives a misfit value based on the differences between the
calculated and the observed values. In this study the misfit values were below 500 points for
all models. The results of the modeling are shown in figure 4.9 (a)-4.9 (d). The bodies are
labeled i, ii, or iii respectively from left to right in all the four profiles.
Fig 4.9 (a) 2-D Modeling results along profile AA’
The profile AA’ is about 4700 m in length and trends NW-SE of the RMI contour map. It
cuts through an elongated positive magnetic anomaly that has a NW-SE trend and also cuts
through a magnetic low on the southern part of the map. The models on the profile indicate
LEGEND: -------- Observed Calculated
41
two causative bodies with magnetic susceptibilities -1.204SI and 0.7284 SI respectively.
Body (i) is at a shallow depth of about 13m while body (ii) is at a modeled depth of 59m.
Body (i) is extensive covering a length of 3499 m while body (ii) has an approximate width
of 1274m. These bodies are speculated to be ferromagnetic subsurface bodies. The negative
susceptibility of body (i) could be as a result of reverse magnetization.
Fig 4.9 (b) 2-D Modeling results along profile BB’
Profile BB’ runs NW-SE at a bearing of 1460
to the North. It cuts through several magnetic
highs and lows in the Kindani study area. The profile runs about 5000m in length. Three
magnetic bodies were modeled along the profile. Body (i) occurs at the start of the profile
and has a body width of about 2244m. Its depth is estimated at 59m below the surface. Its
modeled susceptibility is -0.834SI. Body (ii) is an intrusive speculated to be an ore body of
LEGEND: -------- Observed Calculated
42
depth 112m below the surface. Its body width is modeled as 1300 m and its susceptibility is
1.7624 SI. Body (iii) has a magnetic susceptibility of -0.235SI. Its modeled body width is
1070m at a relatively shallow depth of 58 m.
Fig 4.9 (c) 2-D Modeling results along profile CC’
Profile CC’ has a SW-NE trend on the RMI map. It cuts through successive magnetic highs
and a low near its tail end. The profile runs a length of about 5500m, on a bearing of about
0520. Three causative anomalous bodies are modeled along this profile. These are speculated
to be iron ore bodies which are the sources of the highly magnetic surface rocks found in the
area. The 3 bodies (i), (ii) and (iii) have magnetic susceptibilities -1.204, -0.834 and 0.6305
LEGEND: -------- Observed Calculated
43
respectively. The three bodies are all relatively shallow at about 136m, 12m and 53 m
respectively. Their body widths are modeled as 533 m, 1926 m and 743 m respectively in
length. This indicates possible presence of extensive ferromagnetic ore bodies in the study
area.
Fig 4.9 (d) 2-D Modeling results along profile DD’
Profile DD’ (Fig. 4.9 d) cuts almost horizontally at the southern part of the map on a bearing
of about 860. It stretches about 4200m in length and cuts across the magnetic anomalies on
the southern part of the map. Two causative subsurface bodies are modeled on this profile.
Both bodies are near surface intrusives at 58m and about 0.1 m respectively, below the
surface. Body (i) stretches about 2037 m in length while body (ii) has a body width of about
1573m. Body (i) has a high magnetic susceptibility of 0.7284SI while body (ii) has a
LEGEND: -------- Observed Calculated
44
negative susceptibility of -0.235 SI. Both bodies are postulated to be magnetized iron bearing
ores. A summary of anomalous bodies’ properties is shown in the table 4.2 below.
Table 4.2 Summary of the 2-D modeling results
PROFILE BODY DEPTH TO TOP
OF BODY (M)
BODY
WIDTH
(M)
MODELED
SUSCEPTIBILITY
(SI)
AA’ i 13 3499 -1.204
ii 59 1274 0.7284
BB’ i 59 2244 -0.834
ii 112 1300 1.7624
iii 58 1070 -0.235
CC’ i 136 533 -1.204
ii 12 1926 -0.834
iii 53 743 0.6305
DD’ i 58 2037 0.7284
ii 0.1 1573 -0.235
45
4.5 Chemical analysis results
Four rock samples, from the Kindani survey area were presented for Chemical analysis. The
analysis was done by Energy Dispersive Spectroscopy (EDS/EDX). The samples were
sampled from 3 different regions of the study area. The samples are shown in the photos
below.
Fig 4.10 Rock samples from the Kindani study area
The map 4.10 indicates areas where the four samples S1, S2, S3 and S4 were collected.
Sample 4 was obtained by pitting while the rest are surface rocks. Looking at this map, and
reading it together with the elevation map, there doesn’t seem to be an influence of terraine
on the deposits.
21
S1
S2 S3
S4
4 cm
46
Fig 4.11 Areas where the rock samples were collected from
47
The results of the chemical analysis are as shown in table 4.3 below.
Table 4.3 Chemical analysis results of Kindani area samples
Element Sample 1 Sample2 Sample 3 Sample 4
Point picked/pit
Easting/Northing
389705
22873
390928
22805
390928
22805
389776
21617
Silicon as SiO2 % m/m 27.48 27.87 27.68 30.11
Iron as Fe2O3 % m/m 24.95 25.79 24.20 25.72
Aluminum as Al2O3 % m/m 22.74 25.35 23.83 22.21
Calcium as CaO % m/m 10.40 9.83 9.93 10.38
Potassium as K2O % m/m 8.78 5.02 8.83 8.77
Phosphorous as P2O5 % m/m 2.91 3.31 2.83 -
Titanium as TiO2 % m/m 2.03 2.13 1.96 2.09
The rocks found in Kindani are mostly ferromagnesian basaltic rocks of igneous origin.
Ferromagnesian silicates are minerals rich in iron and or/magnesium and typically low in
silica. Olivine, pyroxene, biotite and amphibole are common ferromagnesian constituents.
The mineral olivine is the most common in the Kindani rocks. Olivine , according to the
Bowen’s reaction series is one of the minerals that crystallizes first during cooling of
basaltic magma (Fredrick and Edward, 2000). This is then followed by crystallizing of
calcium rich plagioclase (CaAl2Si2O8). The chemical analysis results above (Table 4.3)
support presence of these compounds in the Kindani rocks.
The average crustal mineral composition of Si02 is 66%, Al2O3 at 15.2% and FeO at 4.5%
(Taylor and McLennan, 1985). In comparison, the silica composition of rocks in Kindani is
low at about 28%. In turn the values of Iron, aluminum and calcium are high. Titanium,
48
Phosphorous and Potassium are at about 2%, 3% and 8% respectively on average. Other
minerals like sodium are at negligible quantities.
The high quantity of iron suggests presence of the different ores of iron such as magnetite
(Fe3O4) and hematite (Fe2O3). The presence of the some titanium also suggests presence of
compounds like Ilmenite (FeTiO3) and ulvospinel (Fe2TiO4). Ilmenite usually is iron-black or
gray with a brownish tint (Heinz et al., 2005). Ilmenite is usually found in both igneous
rocks, such as those in Kindani and in metamorphic rocks. It usually occurs within the
pyroxenitic portions. Many igneous rocks contain grains of intergrown magnetite and
ilmenite usually formed by the oxidation of ulvospinel.
These elements also suggest presence of other different compounds. The high quantity of
aluminium suggests presence of the compound orthoclase (KAlSi3O8) and bauxite (AlOH3).
Bauxite usually occurs together with the iron oxides goethite and haematite, kaolinite and
anatase (TiO2). These lateritic bauxites were most likely formed by laterization of various
silicate rocks such as granite, gneiss, basalt, syenite and shale, most of which are present in
the Kindani area. This suggests that there has been significant weathering of the rocks in
Kindani area.
4.5.1 Comparison of Kindani with Kimachia values
Abuga et al. (2013) did a geophysical study in Kimachia area of Meru County. The chemical
analysis of some samples from the study area gave the results shown below. Since the two
regions studied are within the same county, and less than 100 km apart, it is important to do a
comparison of findings between the two areas.
49
Table 4.4 Chemical analysis results of selected samples from Kimachia area
Sample No 2445 2446 2449 2450
Iron as Fe2O3 92 86 11.8 22.5
Aluminum as
Al2O3
0.5 2.9 - -
Calcium as CaO 0.12 0.03 7.4 0.15
Potassium as K2O 0.01 Nd 2.4 5.8
Titanium as TiO2 0.44 0.4 1.2 0.76
MgO 0.01 0.03 2.7 6.8
Na2O 0.02 0.01 4.24 0.1
A comparison of the values from the two regions shows that samples from Kindani have an
average of 27-30% silica while the samples from Kimachia have silica values that range from
undetectable quantities in sample 2445 to high of about 57% in sample 2449. Kindani
samples have high values of potassium and Aluminium which have negligible values in the
Kimachia values. These quantities in Kindani suggest the compounds orthoclase (KAlSi3O8),
magnetite (Fe3O4), Ilmenite (FeTiO3) and possibly Calcium plagioclase (CaAl2Si2O8) as the
possible main compounds as earlier discussed. Magnetite seems to be the main compound in
the Kimachia samples. The two regions seem to have different rock formations. It is therefore
possible that iron mineralization occurred differently in the two areas even though they are
geologically close. It is evident that as Abuga et al., (2013) had speculated, the iron deposits
spread beyond the Kimachia area and cover other regions of the county. It is possible that
these deposits form a belt with the more extensive Tharaka deposits.
50
CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
A ground magnetic survey was effectively carried out covering an area of about 25km2. The
magnetic data was reduced for diurnal and geomagnetic corrections. Contour maps and 3-D
surface maps were used to qualitatively interpret the data. The contour map reveals an area
with extensive anomalies covering the entire study area, while the 3-D surface map gives a 3-
dimensional view of the magnetic variations within the study area. The major trend of the
anomalies is NW-SE and SW-NE trends. Four cross-sectional profiles were chosen cutting
across major anomalies in the study area. Euler deconvolution and 2-D forward modeling
was used to analyze the data quantitatively. Chemical analysis was also done by Energy
Dispersive Spectroscopy (EDS) on a few rock samples to quantify amount of Iron in the
samples. The following conclusions can be made from this work.
Euler deconvolution results reveal mostly near surface bodies interpreted as possible iron ore
deposits. The anomalous bodies are shallow, with the greatest depths of solutions noted at
about 1500m on profile DD’. These bodies are the sources of the highly ferromagnetic
surface rocks seen in Kindani area.
The high susceptibility values of up to 2.000SI modeled using Mag2dc software indicate high
magnetization of rocks in this area. Magnetization values are usually determined by the
amount of iron bearing minerals in a rock.
The Chemical analysis results confirm high quantities of iron (as Fe2O3) with up to 25% by
mass. These rocks were only a small sample and there is a likelihood of higher quantities in
other rocks within the area.
The 2-D models done using mag2dc software agree with the Euler solutions of mostly
shallow bodies, the lowest being less than 1m deep. The models show extensive bodies, the
51
longest being about 3500m. It is possible that these bodies are not a continuous ore, but
rather, several ore bodies lying along the profile. It might not have been easy to pick out
these discontinuities because a station spacing of about 500m was used. Perhaps this would
have been more obvious if smaller station spacing was used.
Ground magnetic surveying was therefore effectively used to delineate areas of possible iron
ore deposits within the Kindani area. Different analytic techniques have been used to
interpret observed data while employing geological constrains available for the area. The
Chemical analysis results were also important in confirming presence of iron content in the
area rocks. From their appearance and iron content, these samples don’t appear to be BIFs.
The iron ore in this region is most likely of magmatic origin.
5.2 Recommendations
Results of this study confirm presence of more iron ore deposits within Meru region than was
previously known. It is therefore necessary for the ministry of mining or some exploration
companies to do further exploration within the region, to quantify extent of coverage of these
deposits for possible exploitation. It is possible that the Kindani deposits extend within the
Meru national park, and therefore an aeromagnetic study might be necessary as ground
magnetics would be dangerous because of the wild animals.
It is also necessary to carry out more geophysical studies using a different method since
geophysical methods are usually non-unique. Electro-magnetics is recommended in this case
to delineate the deposits. This method is highly suitable since iron ions allow passage of
electric currents and therefore areas with these deposits would show enhanced conductivities
relative to their host rocks. Gravity methods may also be used since iron compounds usually
have high specific densities.
52
There is need to revise the geology of the area since geological reports available indicate that
it isn’t possible to find significant mineral deposits within the Meru region. This study,
coupled with other recent studies, suggest that these iron deposits could be significantly
extensive.
New surveys in the area should include accurate measurements of susceptibilities of rocks in
Kindani area as well as dip angles of these deposits. The instruments to measure these
quantities were not available at the time of this study.
53
REFERENCES
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56
APPENDIX I: RAW DATA & DIURNAL CORRECTIONS TABLE
STATION
SERIAL
EASTING NORTHING TIME OBSERVED
FIELD
DIURNAL
CORR.
RESIDUAL
ANOMALY
BS 389714 21367 1049 10295 0 10295
A1 392001 21119 1130 10318 -10 10308
A2 392494 21339 1147 10781 -14 10767
A3 393006 20985 1203 10883 -20 10863
A4 393535 20750 1224 10507 -23 10484
A5 393928 20818 1243 11139 -28 11111
BS 389714 21367 1409 10246 0 10246
A6 390999 21031 1420 10783 -45 10738
A7 390501 21003 1429 11308 -43 11265
A8 390000 21054 1437 10545 -42 10503
A9 389500 21198 1447 9967 -39 9928
A10 389000 21314 1457 10709 -36 10673
BS 389714 21367 1518 10262 0 10262
BS 389714 21367 915 10330 0 10330
B1 391500 24008 1008 11361 -8 11353
B2 391900 23733 1021 10761 -9 10752
B3 392500 23499 1037 10899 -10 10889
B4 393185 23499 1056 10046 -13 10033
B5 392900 23059 1113 9187 -16 9171
B6 393006 22679 1136 11154 -20 11134
B7 393226 22500 1144 11192 -21 11171
BS 389714 21367 1214 10307 0 10307
BS 389714 21367 1300 10272 0 10272
B8 391070 24138 1325 10797 -51 10746
B11 390600 23443 1420 10840 -44 10796
B12 391000 23013 1431 11852 -34 11818
B13 391500 23003 1449 10900 -28 10872
B14 392000 22807 1457 10844 -24 10820
B15 392444 22500 1505 11626 -23 11603
B16 393000 22004 1518 10544 -21 10523
B17 393400 21718 1529 10182 -15 10167
BS 389714 21367 1607 10323 0 10323
BS 389714 21367 927 10330 0 10330
C1 390500 23822 956 10728 -12 10716
C2 390504 24006 1005 10890 -18 10872
C3 390001 23702 1020 10768 -26 10742
C4 389298 23506 1038 11064 -32 11032
C5 390232 23500 1050 10762 -38 10724
BS 389714 21367 1102 10287 0 10287
C6 390000 23031 1126 10755 -38 10717
C7 389591 23044 1144 10720 -34 10686
C8 390500 22852 1204 10800 -29 10771
57
C9 391000 22928 1215 12028 -26 12002
C10 391500 22654 1223 10612 -25 10587
C11 391775 22500 1230 10501 -24 10477
C12 392000 22333 1237 11031 -23 11008
C13 392450 22066 1243 9469 -21 9448
C14 392888 22002 1300 10757 -18 10739
BS 389714 21367 1328 10318 0 10318
C15 390000 22027 1415 12046 -10 12036
C16 390500 22322 1428 11169 -9 11160
C17 390540 22177 1437 10536 -9 10527
C18 391000 22111 1448 10716 -8 10708
C19 391500 22013 1457 13119 -8 13111
C20 392041 21817 1505 10343 -7 10336
C21 392500 21714 1540 10517 -6 10511
BS 389714 21367 1617 10326 0 10326
BS 389714 21367 927 10308 0 10308
D1 390000 21215 945 10810 -16 10794
D2 390500 21445 954 12060 -20 12040
D3 391022 21456 1002 10013 -24 9989
D4 392000 21387 1016 9189 -38 9151
BS 389714 21367 1052 10244 0 10244
D5 389503 19003 1157 9625 -4 9621
D6 389606 19500 1206 10373 4 10377
D7 389655 20000 1215 9037 12 9049
D8 389481 20486 1225 10547 22 10569
D9 389580 21000 1236 10224 32 10256
BS 389714 21367 1241 10347 0 10347
BS 389714 21367 1330 10254 0 10254
D10 394044 18969 1422 10657 -32 10625
D11 394207 19553 1440 10764 -22 10742
D12 394118 19680 1448 10584 -20 10564
D13 394007 19951 1456 10041 -16 10025
D14 393684 20740 1512 10832 -8 10824
D15 393706 21000 1529 11315 0 11315
D16 393526 21158 1537 10512 2 10514
BS 389714 21367 1608 10326 0 10326
BS 389714 21367 904 10261 0 10261
E1 394025 20124 1003 10721 3 10724
E2 394144 20589 1013 9152 3 9155
E3 394185 21016 1028 11054 5 11059
E4 394124 21589 1044 10417 6 10423
E5 393955 22000 1054 10936 6 10942
E6 393759 21000 1123 11044 7 11051
BS 389714 21367 1149 10269 0 10269
58
E7 393221 21000 1230 10966 2 10968
E8 393062 20500 1240 10625 0 10625
E9 392876 20300 1255 11146 -2 11144
E10 392920 20500 1301 10592 -2 10590
E11 392614 21000 1318 10362 -4 10358
E12 392638 21500 1343 10026 -7 10019
E13 392601 21896 1354 10923 -9 10914
BS 389714 21367 1430 10249 0 10249
E14 391516 20000 1522 10149 21 10170
E15 391500 19833 1526 9526 23 9549
E16 391440 19500 1547 10144 35 10179
E17 391993 19034 1556 10545 42 10587
E18 392000 19904 1608 11251 48 11299
BS 389714 21367 1642 10331 0 10331
BS 389714 21367 922 10334 0 10334
F1 392448 20480 948 10617 1 10618
F2 392052 20008 959 10736 2 10738
F3 392471 20124 1009 10951 3 10954
F4 391775 20433 1025 11133 3 11136
F5 391784 20500 1032 10138 4 10142
F6 391445 20442 1047 10662 5 10667
F7 391390 22203 1058 10249 5 10254
F8 391323 20058 1108 10050 6 10056
BS 389714 21367 1122 10341 0 10341
F9 390849 20000 1149 10388 2 10390
F10 390770 20496 1209 12993 -2 12991
F11 390554 19501 1251 9828 -12 9816
F12 390733 19131 1302 11039 -14 11025
BS 389714 21367 1340 10311 0 10311
BS 389714 21367 933 10310 0 10310
G1 390513 20018 952 10376 -8 10368
G2 390117 20096 1006 10334 -12 10322
G3 390446 20530 1019 10397 -16 10381
G4 390498 19676 1038 9522 -22 9500
G5 390008 19509 1050 8434 -26 8408
G6 389749 19123 1111 11220 -34 11186
BS 389714 21367 1136 10269 0 10269
59
APPENDIX II: GEOMAGNETIC CORRECTIONS
STATION
SERIAL
EASTING NORTHING CORRECTED
VALUE
IGRF ANOM.
A1 392001 21119 10308 11454 -1146
A2 392494 21339 10767 11451 -684
A3 393006 20985 10863 11454 -591
A4 393535 20750 10484 11455 -971
A5 393928 20818 11111 11455 -344
A6 390999 21031 10738 11457 -719
A7 390501 21003 11265 11458 -193
A8 390000 21054 10503 11459 -956
A9 389500 21198 9928 11457 -1529
A10 389000 21314 10673 11456 -783
B1 391500 24008 11353 11421 -68
B2 391900 23733 10752 11423 -671
B3 392500 23499 10889 11425 -536
B4 393185 23499 10033 11424 -1391
B5 392900 23059 9171 11429 -2258
B6 393006 22679 11134 11435 -301
B7 393226 22500 11171 11435 -264
B8 391070 24138 10746 11421 -675
B11 390600 23443 10796 11429 -633
B12 391000 23013 11818 11434 384
B13 391500 23003 10872 11433 -561
B14 392000 22807 10820 11435 -615
B15 392444 22500 11603 11436 167
B16 393000 22004 10523 11442 -919
B17 393400 21718 10167 11445 -1278
C1 390500 23822 10716 11425 -709
C2 390504 24006 10872 11423 -551
C3 390001 23702 10742 11427 -685
C4 389298 23506 11032 11430 -398
C5 390232 23500 10724 11428 -704
C6 390000 23031 10717 11435 -718
C7 389591 23044 10686 11436 -750
C8 390500 22852 10771 11435 -664
C9 391000 22928 12002 11435 567
C10 391500 22654 10587 11436 -849
C11 391775 22500 10477 11437 -960
C12 392000 22333 11008 11439 -431
C13 392450 22066 9448 11441 -1993
C14 392888 22002 10739 11442 -703
C15 390000 22027 12036 11446 590
60
C16 390500 22322 11160 11442 -282
C17 390540 22177 10527 11443 -916
C18 391000 22111 10708 11444 -736
C19 391500 22013 13111 11444 1667
C20 392041 21817 10336 11446 -1110
C21 392500 21714 10511 11446 -935
D1 390000 21215 10794 11455 -661
D2 390500 21445 12040 11452 588
D3 391022 21456 9989 11451 -1462
D4 392000 21387 9151 11451 -2300
D5 389503 19003 9621 11481 -1860
D6 389606 19500 10377 11476 -1099
D7 389655 20000 9049 11470 -2421
D8 389481 20486 10569 11465 -896
D9 389580 21000 10256 11458 -1202
D10 394044 18969 10625 11474 -849
D11 394207 19553 10742 11467 -725
D12 394118 19680 10564 11466 -902
D13 394007 19951 10025 11464 -1439
D14 393684 20740 10824 11454 -630
D15 393706 21000 11315 11452 -137
D16 393526 21158 10514 11451 -937
E1 394025 20124 10724 11461 -737
E2 394144 20589 9155 11456 -2301
E3 394185 21016 11059 11451 -392
E4 394124 21589 10423 11444 -1021
E5 393955 22000 10942 11440 -498
E6 393759 21000 11051 11451 -400
E7 393221 21000 10968 11452 -484
E8 393062 20500 10625 11459 -834
E9 392876 20300 11144 11460 -316
E10 392920 20500 10590 11459 -869
E11 392614 21000 10358 11453 -1095
E12 392638 21500 10019 11448 -1429
E13 392601 21896 10914 11443 -529
E14 391516 20000 10170 11466 -1296
E15 391500 19833 9549 11469 -1920
E16 391440 19500 10179 11473 -1294
E17 391993 19034 10587 11477 -890
E18 392000 19904 11299 11467 -168
F1 392448 20480 10618 11461 -843
F2 392052 20008 10738 11465 -727
F3 392471 20124 10954 11463 -509
F4 391775 20433 11136 11461 -325
61
F5 391784 20500 10142 11461 -1319
F6 391445 20442 10667 11461 -794
F7 391390 22203 10254 11441 -1187
F8 391323 20058 10056 11466 -1410
F9 390849 20000 10390 11467 -1077
F10 390770 20496 12991 11462 1529
F11 390554 19501 9816 11474 -1658
F12 390733 19131 11025 11478 -453
G1 390513 20018 10368 11468 -1100
G2 390117 20096 10322 11467 -1145
G3 390446 20530 10381 11461 -1080
G4 390498 19676 9500 11471 -1971
G5 390008 19509 8408 11475 -3067
G6 389749 19123 11186 11479 -293
62
APPENDIX III: BASE STATIONS (BS) DATA USED FOR DIURNAL
CORRECTIONS AND DIURNAL CURVES
Day/Date Time
(Hours
Intensity
(nT)
Day 1
06/12/2014
1049 10295
1409 10246
1518 10262
Day 2
08/12/2014
915 10330
1214 10307
1300 10272
1607 10323
Day
310/12/2014
927 10330
1102 10287
1328 10318
1617 10326
Day 4
12/12/2014
927 10308
1052 10244
1241 10347
1330 10254
1608 10326
Day 5
13/12/2014
904 10261
1149 10269
1430 10249
1642 10331
Day 6
15/12/2014
922 10334
1122 10341
1340 10311
Day 7
16/12/2014
933 10310
1136 10269
10240
10260
10280
10300
10 11 12 13 14 15 16
Mag
net
ic In
ten
sity
(nT)
Time (Hrs)
Diurnal Curve Day 1
Intensity (nT)
63
10260
10280
10300
10320
10340
9 11 13 15 17
Inte
nsi
ty (n
T)
Time (Hrs)
Diurnal Curve Day 2
Intensity (nT)
10280
10300
10320
10340
9 11 13 15 17
Inte
nsi
ty (n
T)
Time (Hrs)
Diurnal Curve Day 3
Intensity (nT)
64
10220
10240
10260
10280
10300
10320
10340
10360
9 10 11 12 13 14 15 16 17
Inte
nsi
ty (n
T)
Time (Hrs)
Diurnal Curve Day 4
Intensity (nT)
10240
10260
10280
10300
10320
10340
9 11 13 15 17
Inte
nsi
ty (n
T)
Time (Hrs)
Diurnal Curve Day 5
Intensity (nT)
65
10300
10320
10340
10360
9 10 11 12 13 14
Inte
nsi
ty (n
T)
Time (Hrs)
Diurnal Curve Day 6
Intensity (nT)
10260
10280
10300
10320
9 10 11 12
Inte
nsi
ty (n
T)
Time (Hrs)
Diurnal Curve Day 7
Intensity (nT)
66
APPENDIX IV: PROFILE DATA
Profile Data for AA’
X (m) No trend W/trend
0.00 0.00 -1507.85
2.94 -0.24 -1507.69
5.41 -0.31 -1507.43
55.59 -2.33 -1502.72
108.25 -2.48 -1495.80
160.90 -1.05 -1487.30
213.56 0.99 -1478.19
266.21 1.59 -1470.53
318.87 -3.50 -1468.54
371.53 -22.90 -1480.88
424.18 -72.68 -1523.59
476.84 -171.21 -1615.06
529.49 -317.06 -1753.84
582.15 -491.18 -1920.89
634.80 -678.08 -2100.72
687.46 -867.02 -2282.59
740.11 -1045.61 -2454.11
792.77 -1192.93 -2594.37
845.43 -1277.08 -2671.45
898.08 -1277.31 -2664.61
950.74 -1213.45 -2593.68
1003.39 -1119.19 -2492.36
1056.05 -1017.12 -2383.21
1108.70 -919.43 -2278.46
1114.31 -910.11 -2268.39
1161.36 -830.81 -2182.77
1214.01 -755.99 -2100.89
1266.67 -696.61 -2034.44
1319.33 -649.95 -1980.71
1371.98 -567.43 -1891.12
1424.64 -453.24 -1769.86
1477.29 -354.32 -1663.87
1529.95 -273.99 -1576.48
1582.60 -212.06 -1507.47
1635.26 -167.51 -1455.86
1687.91 -139.01 -1420.29
1740.57 -125.15 -1399.36
1793.23 -124.66 -1391.81
1845.88 -136.43 -1396.51
1898.54 -159.33 -1412.34
1951.19 -192.06 -1438.01
2003.85 -233.03 -1471.90
2056.50 -280.28 -1512.09
2109.16 -331.51 -1556.24
2161.81 -383.78 -1601.45
2214.47 -432.63 -1643.23
2223.20 -438.61 -1648.04
2267.13 -468.92 -1672.45
2319.78 -477.19 -1673.66
2372.44 -434.81 -1624.20
2425.09 -336.91 -1519.24
2477.75 -205.86 -1381.12
2530.40 -61.93 -1230.13
2583.06 84.26 -1076.86
2635.71 227.26 -926.80
2688.37 362.97 -784.03
2741.03 486.11 -653.81
2793.68 588.18 -544.68
2846.34 657.90 -467.89
2898.99 689.44 -429.28
2951.65 690.91 -420.74
3004.30 677.38 -427.21
3056.96 659.94 -437.57
3109.61 643.81 -446.63
3162.27 630.54 -452.84
3214.93 620.04 -456.27
3267.58 611.65 -457.59
3320.24 604.59 -457.59
3332.10 603.11 -457.48
3372.89 597.92 -457.19
3425.55 591.09 -456.95
3478.20 583.46 -457.51
3530.86 574.39 -459.51
3583.51 563.29 -463.55
3636.17 549.60 -470.16
3688.83 532.89 -479.81
3741.48 512.84 -492.79
3794.14 489.34 -509.23
3846.79 462.46 -529.03
3899.45 432.48 -551.95
3952.10 399.78 -577.58
4004.76 364.84 -605.46
67
4057.41 328.10 -635.12
4110.07 289.99 -666.17
4162.73 250.82 -698.27
4215.38 210.83 -731.19
4268.04 170.12 -764.83
4320.69 128.69 -799.20
4373.35 86.40 -834.41
4426.00 42.99 -870.76
4441.00 30.20 -881.53
4476.51 -0.02 -906.99
Profile Data for BB’
X (m) No trend W/trend
0.00 0.00 -695.99
2.61 0.50 -695.49
40.72 8.38 -687.52
65.17 15.77 -680.07
127.74 34.87 -660.84
138.52 38.14 -657.55
190.30 59.62 -635.96
236.33 80.93 -614.54
252.86 91.53 -603.90
315.42 145.26 -550.03
334.13 167.93 -527.32
377.98 221.75 -473.41
431.93 296.86 -398.18
440.54 308.86 -386.17
503.10 402.80 -292.08
529.74 445.54 -249.28
565.67 503.48 -191.27
627.54 612.11 -82.51
628.23 613.29 -81.32
690.79 728.91 34.44
725.34 799.40 105.00
753.35 854.37 160.04
815.91 991.71 297.51
823.15 1008.18 313.99
878.47 1121.05 426.98
920.95 1217.76 523.79
941.03 1220.92 526.99
1003.60 1023.55 329.76
1018.75 979.50 285.75
1066.16 828.51 134.85
1116.56 680.47 -13.07
1128.72 644.29 -49.23
1191.28 466.47 -226.91
1214.36 405.23 -288.10
1253.84 300.37 -392.87
1312.16 156.93 -536.19
1316.40 146.75 -546.36
1378.97 6.59 -686.37
1409.97 -56.73 -749.63
1441.53 -116.95 -809.79
1504.09 -223.69 -916.38
1507.77 -229.42 -922.11
1566.65 -303.51 -996.07
1605.57 -345.28 -1037.75
1629.21 -354.94 -1047.37
1691.77 -348.20 -1040.48
1703.38 -337.11 -1029.37
1754.33 -173.37 -865.51
1801.18 202.38 -489.67
1816.90 321.84 -370.17
1879.46 956.66 264.79
1898.98 1170.67 478.84
1942.02 1506.11 814.38
1996.79 1721.87 1030.25
2004.58 1736.14 1044.54
2067.14 1584.52 893.06
2094.59 1452.49 761.09
2129.70 1301.42 610.09
2192.26 987.64 296.45
2192.39 986.96 295.78
2254.83 667.90 -23.15
68
2290.20 489.34 -201.63
2317.39 356.15 -334.77
2379.95 54.51 -636.27
2388.00 16.65 -674.11
2442.51 -234.17 -924.81
2485.80 -427.25 -1117.79
2505.07 -510.37 -1200.87
2567.63 -776.43 -1466.80
2583.61 -842.76 -1533.09
2630.19 -1030.92 -1721.15
2681.41 -1241.45 -1931.57
2692.76 -1283.81 -1973.91
2755.32 -1526.71 -2216.66
2779.21 -1509.38 -2199.28
2817.88 -1366.65 -2056.46
2877.02 -1129.50 -1819.19
2880.44 -1115.26 -1804.94
2943.00 -872.15 -1561.69
2974.82 -773.73 -1463.20
3005.56 -684.31 -1373.72
3068.12 -561.75 -1251.02
3072.62 -556.57 -1245.83
3130.69 -485.37 -1174.50
3170.42 -458.10 -1147.14
3193.25 -441.96 -1130.96
3255.81 -414.56 -1103.42
3268.23 -410.52 -1099.35
3318.37 -394.82 -1083.54
3366.03 -380.51 -1069.13
3380.93 -376.34 -1064.93
3443.49 -355.01 -1043.45
3463.83 -346.51 -1034.91
3506.05 -328.61 -1016.92
3561.64 -300.51 -988.70
3568.62 -296.84 -985.01
3631.18 -260.08 -948.11
3659.44 -241.83 -929.81
3693.74 -218.43 -906.33
3756.30 -169.94 -857.70
3757.24 -169.09 -856.85
3818.86 -110.91 -798.54
3855.05 -70.56 -758.11
3881.42 -41.08 -728.57
3943.98 36.18 -651.17
3952.85 47.29 -640.05
4006.55 117.23 -569.98
4050.65 171.38 -515.73
4069.11 195.35 -491.73
4131.67 258.34 -428.60
4148.46 269.27 -417.63
4194.23 293.47 -393.33
4246.26 302.25 -384.44
4256.79 301.69 -384.98
4319.35 290.52 -396.01
4344.06 284.30 -402.17
4381.91 269.93 -416.46
4441.87 247.07 -439.19
4444.48 245.88 -440.38
4507.04 218.46 -467.65
4539.67 204.79 -481.26
4569.60 191.23 -494.75
4632.16 164.44 -521.41
4637.47 162.25 -523.59
4694.72 137.84 -547.87
4735.28 121.64 -563.98
4757.28 112.72 -572.85
4819.84 88.93 -596.50
4833.08 84.19 -601.22
4882.41 66.60 -618.70
4930.88 50.72 -634.47
4944.97 46.21 -638.95
5007.53 27.54 -657.48
5028.69 21.71 -663.27
5070.09 10.75 -674.13
5114.22 0.02 -684.77
69
Profile CC’ Data
X (m) No trend W/trend
0.00 0.00 -1105.09
63.96 -10.29 -1114.81
73.55 -10.07 -1114.51
130.65 -26.19 -1130.12
159.34 -27.52 -1131.19
197.34 -35.87 -1139.21
245.13 -36.54 -1139.45
264.03 -38.60 -1141.34
330.71 -33.98 -1136.13
330.92 -33.94 -1136.08
397.40 -21.56 -1123.11
416.72 -14.83 -1116.21
464.09 2.52 -1098.44
502.51 23.91 -1076.71
530.78 41.17 -1059.19
588.30 89.87 -1009.98
597.47 98.43 -1001.34
664.16 178.37 -920.81
674.09 192.84 -906.25
730.84 283.32 -815.26
759.88 336.28 -762.04
797.53 410.12 -687.87
845.68 509.84 -587.72
864.22 550.15 -547.25
930.91 692.79 -404.00
931.47 693.93 -402.87
997.60 828.68 -267.52
1017.26 863.38 -232.65
1064.29 946.64 -148.97
1103.05 998.41 -96.86
1130.97 1036.50 -58.52
1188.84 1081.35 -13.15
1197.66 1088.43 -5.99
1264.35 1093.44 -0.39
1274.64 1086.43 -7.30
1331.04 1039.30 -53.93
1360.43 991.94 -101.03
1397.73 921.31 -171.33
1446.22 801.96 -290.25
1464.42 752.22 -339.83
1531.10 551.00 -540.45
1532.01 548.10 -543.34
1597.79 339.88 -750.98
1617.80 275.26 -815.42
1664.48 142.91 -947.35
1703.60 44.53 -1045.38
1731.17 -1.57 -1091.24
1789.39 -24.68 -1113.83
1797.86 -23.99 -1113.06
1864.55 66.81 -1021.67
1875.18 91.17 -997.21
1931.23 211.16 -876.73
1960.97 291.10 -796.53
1997.92 387.18 -700.11
2046.76 536.61 -550.24
2064.61 590.35 -496.35
2131.30 823.01 -263.09
2132.56 827.82 -258.27
2197.99 1085.27 -0.24
2218.35 1175.16 89.83
2264.68 1388.10 303.18
2304.14 1591.16 506.60
2331.36 1740.81 656.49
2389.93 2085.08 1001.28
2398.05 2139.55 1055.82
2464.74 2431.01 1347.88
2475.72 2424.56 1341.53
2531.43 2166.81 1084.27
2561.52 2014.99 932.72
2598.12 1767.77 685.82
2647.31 1509.68 428.18
2664.81 1417.94 336.59
2731.49 1135.86 55.10
2733.10 1130.21 49.47
2798.18 916.64 -163.52
2818.89 863.54 -216.44
2864.87 760.53 -319.03
2904.68 697.05 -382.16
2931.56 663.45 -415.52
2990.48 628.11 -450.34
2998.25 625.53 -452.85
70
3064.94 633.03 -444.75
3076.27 629.85 -447.84
3131.63 601.80 -475.39
3162.06 602.12 -474.80
3198.31 610.97 -465.63
3247.85 647.12 -429.03
3265.00 661.24 -414.76
3331.69 743.53 -331.87
3333.64 746.78 -328.61
3398.38 834.42 -240.39
3419.43 867.82 -206.80
3465.07 911.93 -162.29
3505.23 937.65 -136.22
3531.76 938.01 -135.61
3591.02 895.29 -177.81
3598.44 887.85 -185.18
3665.13 780.85 -291.59
3676.81 756.84 -315.49
3731.82 641.19 -430.65
3762.60 567.85 -503.72
3798.51 479.24 -592.01
3848.39 343.88 -726.93
3865.20 296.25 -774.40
3931.89 88.65 -981.41
3934.19 81.06 -988.98
3998.57 -146.16 -1215.63
4019.98 -228.53 -1297.80
4065.26 -412.37 -1481.24
4105.77 -592.27 -1660.78
4131.95 -711.73 -1780.01
4191.56 -1004.16 -2071.91
4198.64 -1029.60 -2097.29
4265.33 -1031.86 -2098.94
4277.35 -1016.82 -2083.80
4332.02 -884.77 -1951.26
4363.15 -828.50 -1894.71
4398.70 -756.98 -1822.88
4448.94 -674.98 -1740.44
4465.39 -647.45 -1712.75
4532.08 -553.29 -1618.00
4534.73 -550.12 -1614.81
4598.77 -470.68 -1534.80
4620.52 -448.17 -1512.10
4665.46 -401.10 -1464.62
4706.31 -364.91 -1428.07
4732.15 -342.17 -1405.10
4792.11 -297.04 -1359.43
4798.83 -292.07 -1354.41
4865.52 -248.56 -1310.31
4877.90 -241.55 -1303.18
4932.21 -210.99 -1272.14
4963.69 -195.58 -1256.45
4998.90 -178.56 -1239.12
5049.48 -157.09 -1217.19
5065.59 -150.36 -1210.32
5132.28 -125.62 -1184.99
5135.27 -124.63 -1183.96
5198.96 -103.57 -1162.34
5221.07 -97.12 -1155.70
5265.65 -84.22 -1142.40
5306.86 -73.64 -1131.45
5332.34 -67.17 -1124.75
5392.65 -53.52 -1110.56
5399.03 -52.09 -1109.08
5465.72 -38.58 -1094.97
5478.44 -36.27 -1092.55
5532.41 -26.53 -1082.33
5564.23 -21.41 -1076.93
5599.09 -15.84 -1071.05
5650.03 -8.57 -1063.32
5665.78 -6.34 -1060.95
5714.50 -0.02 -1054.20
71
Profile DD’ data
X (m) No trend W/trend
0.00 0.00 -1507.85
2.94 -0.24 -1507.69
5.41 -0.31 -1507.43
55.59 -2.33 -1502.72
108.25 -2.48 -1495.80
160.90 -1.05 -1487.30
213.56 0.99 -1478.19
266.21 1.59 -1470.53
318.87 -3.50 -1468.54
371.53 -22.90 -1480.88
424.18 -72.68 -1523.59
476.84 -171.21 -1615.06
529.49 -317.06 -1753.84
582.15 -491.18 -1920.89
634.80 -678.08 -2100.72
687.46 -867.02 -2282.59
740.11 -1045.61 -2454.11
792.77 -1192.93 -2594.37
845.43 -1277.08 -2671.45
898.08 -1277.31 -2664.61
950.74 -1213.45 -2593.68
1003.39 -1119.19 -2492.36
1056.05 -1017.12 -2383.21
1108.70 -919.43 -2278.46
1114.31 -910.11 -2268.39
1161.36 -830.81 -2182.77
1214.01 -755.99 -2100.89
1266.67 -696.61 -2034.44
1319.33 -649.95 -1980.71
1371.98 -567.43 -1891.12
1424.64 -453.24 -1769.86
1477.29 -354.32 -1663.87
1529.95 -273.99 -1576.48
1582.60 -212.06 -1507.47
1635.26 -167.51 -1455.86
1687.91 -139.01 -1420.29
1740.57 -125.15 -1399.36
1793.23 -124.66 -1391.81
1845.88 -136.43 -1396.51
1898.54 -159.33 -1412.34
1951.19 -192.06 -1438.01
2003.85 -233.03 -1471.90
2056.50 -280.28 -1512.09
2109.16 -331.51 -1556.24
2161.81 -383.78 -1601.45
2214.47 -432.63 -1643.23
2223.20 -438.61 -1648.04
2267.13 -468.92 -1672.45
2319.78 -477.19 -1673.66
2372.44 -434.81 -1624.20
2425.09 -336.91 -1519.24
2477.75 -205.86 -1381.12
2530.40 -61.93 -1230.13
2583.06 84.26 -1076.86
2635.71 227.26 -926.80
2688.37 362.97 -784.03
2741.03 486.11 -653.81
2793.68 588.18 -544.68
2846.34 657.90 -467.89
2898.99 689.44 -429.28
2951.65 690.91 -420.74
3004.30 677.38 -427.21
3056.96 659.94 -437.57
3109.61 643.81 -446.63
3162.27 630.54 -452.84
3214.93 620.04 -456.27
3267.58 611.65 -457.59
3320.24 604.59 -457.59
3332.10 603.11 -457.48
3372.89 597.92 -457.19
3425.55 591.09 -456.95
3478.20 583.46 -457.51
3530.86 574.39 -459.51
3583.51 563.29 -463.55
3636.17 549.60 -470.16
3688.83 532.89 -479.81
3741.48 512.84 -492.79
3794.14 489.34 -509.23
3846.79 462.46 -529.03
3899.45 432.48 -551.95
3952.10 399.78 -577.58
4004.76 364.84 -605.46
4057.41 328.10 -635.12
4110.07 289.99 -666.17
72
4162.73 250.82 -698.27
4215.38 210.83 -731.19
4268.04 170.12 -764.83
4320.69 128.69 -799.20
4373.35 86.40 -834.41
4426.00 42.99 -870.76
4441.00 30.20 -881.53
4476.51 -0.02 -906.99
73
APPENDIX V: ELEVATION DATA
S/SERIAL EASTING NORTH ING ELEV
BS 389714 21367 985
A1 392001 21119 870
A2 392494 21339 850
A3 393006 20985 854
A4 393535 20750 845
A5 393928 20818 802
A6 390999 21031 876
A7 390501 21003 910
A8 390000 21054 929
A9 389500 21198 966
A10 389000 21314 992
BS 389714 21367 985
BS 389714 21367 985
B1 391500 24008 870
B2 391900 23733 865
B3 392500 23499 866
B4 393185 23499 870
B5 392900 23059 865
B6 393006 22679 865
B7 393226 22500 858
B8 391070 24138 909
B9 390482 25038 943
B10 389985 25488 940
B11 390600 23443 946
B12 391000 23013 890
B13 391500 23003 897
B14 392000 22807 882
B15 392444 22500 840
B16 393000 22004 848
B17 393400 21718 820
C1 390500 23822 920
C2 390504 24006 935
C3 390001 23702 953
C4 389298 23506 1022
C5 390232 23500 920
BS 389714 21367 985
C6 390000 23031 1126
C7 389591 23044 1025
C8 390500 22852 910
C9 391000 22928 890
C10 391500 22654 897
C11 391775 22500 887
C12 392000 22333 881
C13 392450 22066 867
C14 392888 22002 835
C15 390000 22027 946
C16 390500 22322 910
C17 390540 22177 935
C18 391000 22111 905
C19 391500 22013 891
C20 392041 21817 869
C21 392500 21714 830
D1 390000 21215 940
D2 390500 21445 941
D3 391022 21456 922
D4 392000 21387 869
BS 389714 21367 985
D5 389503 19003 900
D6 389606 19500 931
D7 389655 20000 936
D8 389481 20486 950
D9 389580 21000 981
D10 394044 18969 775
D11 394207 19553 799
D12 394118 19680 805
D13 394007 19951 816
D14 393684 20740 838
D15 393706 21000 868
D16 393526 21158 820
E1 394025 20124 819
E2 394144 20589 821
E3 394185 21016 816
E4 394124 21589 830
E5 393955 22000 816
E6 393759 21000 833
BS 389714 21367 985
E7 393221 21000 840
E8 393062 20500 863
E9 392876 20300 862
E10 392920 20500 869
E11 392614 21000 865
E12 392638 21500 801
E13 392601 21896 832
E14 391516 20000 884
74
E15 391500 19833 886
E16 391440 19500 879
E17 391993 19034 857
E18 392000 19904 884
F1 392448 20480 877
F2 392052 20008 869
F3 392471 20124 843
F4 391775 20433 865
F5 391784 20500 865
F6 391445 20442 864
F7 391390 22203 878
F8 391323 20058 883
F9 390849 20000 889
F10 390770 20496 885
F11 390554 19501 902
F12 390733 19131 885
G1 390513 20018 902
G2 390117 20096 908
G3 390446 20530 912
G4 390498 19676 906
G5 390008 19509 918
G6 389749 19123 916