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7/31/2019 Project Report on GIS
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Project ReportOn
Application of GIS and Remote
Sensing
In Mineral Resources
Completed under the guidance of Dr.Jasmeet Kaur
(Professor of S.G.T.B Khalsa College)
Submitted by:
Hakim Asif Haider
Life Science
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ACKNOWLEMENT
No significant achievement can be a solo performance
especially when starting a project from ground up. This
project on Application of GIS and Remote Sensing in
Mineral Resources has by no means been an
exception. Apart from the efforts of me, the success of
this project depends largely on the encouragement and
guidelines of many others. I take this opportunity to
express my gratitude to the people who have been
instrumental in the successful completion of thisproject.
I would like to show my greatest appreciation to Dr.
Jasmeet Kaur , Professor of S.G.T.B Khalsa College. I
cant say thank you enough for Your tremendoussupport and help. I feel motivated and encouraged every
time I attend her class. Without her encouragement and
guidance this project would not have materialized.
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The guidance and support received from my parents . I
am grateful for their constant support and help.
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Abstract
The search for metals and materials needed to sustain
our culture has been carried out since primitive man has
searched for flint to craft hand tools. Today, the
materials needed to drive our economic andtechnological growth are just as crucial. Most of the
easily accessible metal ores were discovered decades
ago; and thus the search has turned to more subtle
deposits and more remote locations.
Since the inception of rudimentary aerial photography
at the turn of the twentieth century, remote sensing has
been used as a tool in the search for economic mineral
deposits. As the level of technology has improved, the
value of remotely sensed data has increased. The page
will highlight the history and implementations of
remote sensing on mineral exploration today.
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TABLE OF CONTENTS
1) Acknowledgement......................................................................................22) Abstract.......................................................................................................43) Introduction..............................................................................................6-13
A Pegmatite hosted gem mine in California. Pegmatites can often belocated within granite bodies due to differential vegetation growth
and erosion patterns. This is prominent in aerial photos
A fault trace near Moab Utah that would be difficult to detect on theground is easily seen in an aerial photograph.
The Ray Rock Gold Prospect, North West territories, Canada. ALarge Scale Linear Feature related to an ophiolite sequence
developed during Precambrian tectonism.
4) GIS Approach In Mineral Targeting with Narayanpet Kimberlite SpatialDatasheet.........................................................................................................15
a) Kimberlite Emplacement Model: Theoretical facts.........................16b)Narayanpet Kimberlite Field...........................................................20
i) Geology........................................................................................21ii)Tectonic Elements........................................................................22iii)Geophysical Surveys.....................................................................22
c) Project Work/ Methodology..............................................................23 i) Data Capture................................................................................24ii)
Derivation of Theme Based Evidence Maps...............................30
iii)Spatial analysis using Bayesian Probability Principle coupled withIndex............................................................................................33
d)Conclusion.........................................................................................405) Mineral Prediction Using Remote Sensing and GIS in Rajasthan.......42
a) Abstract ............................................................................................42b)Introduction......................................................................................44c) Objective...........................................................................................45
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d)Materials and Methodology...........................................................46e) Study Area .....................................................................................47f) Result And Discussion....................................................................48 g) Conclusion......................................................................................52
6) Summary.............................................................................................537) Bibliography.....................................................................................54-59
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Introduction
The value of remote sensing data to mineral
exploration has evolved and increased as technology
has improved. In the early days of aerial photography,
aerial photos were used when available to evaluate
topography and plan prospecting and sampling forays.
After World War II, the analysis of aerial photo data
became much more sophisticated and actualgeological data began to be extracted. The use of
stereoscopic pairs enabled geologist to interpret subtle
structural features. Nonetheless, the primary use of
remotely gathered data was comparative. If a
particular type of deposit was being mined in a
district, aerial photos would be used to locate similar
features elsewhere within the district.
These trends of comparative photography
continued until well into the satellite age when
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satellite imagery became commercially available. The
availability of multi-spectral, radar, and IR imaging,
in variety of combinations allowed geologists to
evaluate regions in much more detail than ever. In
addition, the multiple flyovers allowed a prospect to
be viewed in different light during different seasons.
This greatly reduced the cost of regional explorationby precluding the need for repeated trips to a locale to
reassess. Another advantage was the ability to gather
data through cloud and surface cover with radar
imagery. This allowed data to be collected from the
tropics and arid regions that had previously been
inhospitable to large regional field exploration. The
computer age further enhanced the usefulness of data
by allowing imagery to be digitally enhanced to
highlight specific features. Now spectral studies can
be done which allow the identification of specific
minerals from space.
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A Pegmatite hosted gem mine in California. Pegmatites can often be located
within granite bodies due to differential vegetation growth and erosion patterns.
This is prominent in aerial photos.
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The most elementary operation of remote sensing in
mineral exploration is using aerial photographs to
identify topographic surface features which may imply
the subsurface geology. Such telling surface features as
differential erosion, outcropping rock, drainage
patterns, and folds/faults can be identified. These
features can be compared to other potential targets inthe region when looking for similar deposits. Faults
fractures and contacts often provide a conduit or
depositional environment for hydrothermal or magmatic
fluids in regions of known mineralization, and thus
make excellent targets for further investigation.
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A fault trace near Moab Utah that would be difficult to detect on the ground is
easily seen in an aerial photograph.
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An Extension of simple air photo comparison is
utilising satellite imagery to locate structural features on
a regional level. This can allow geologists to narrow a
large field of targets to a more manageable group. The
same basic parameters exist, the search for favourable
structural trends, only on a regional level. Field teams
can than prioritise their energy in a more effective and
efficient manner.
The Ray Rock Gold Prospect, North West territories, Canada. A Large Scale Linear
Feature related to an ophiolite sequence developed during Precambrian tectonism.
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As Satellite image collection and data management
improved a new kind of remote sensing application
began being used by exploration geologists.
Multispectral imaging and thematic mapping allowed
surface mapping to be performed remotely in ways only
dreamed about during the era of early photo
interpretation. Different scanning spectrums enabled
researchers to begin cataloguing various reflection and
adsorption properties of soils, rock and vegetation.
These spectra could be utilised to interpret actualsurface lithologies from remote images. With a field
crew providing ground truth data, large areas could be
geologically mapped in a short time at a fraction of the
cost of traditional geologic mapping.
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The Pogo district granitics, as seen from LandSat 5. These granites are
clearly seen in this enhanced false color image. The Pogo district deposits
are among the richest mineral locals in the world.
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Diamond, as a lustrous precious stone, has human
fascination since historical times. In India, well known
epics the Ramayana and the Mahabharata have
mention of ornaments of diamonds. India continued to
have dominant position in diamond mining and trade till
beginning of the 18th century. But, mining of the
diamond on large scale started with real rush in the
last quarter of the 19th century in South Africa. The
rush in gradual process founded the basis for the
modern scientific methods of exploration. Kimberlite
was identified as the primary source.
Index minerals were identified as exploration tools.
Thermodynamics of diamonds and hostrock-mineral-
entities and association helped in re-establishing the
relation between the host rock and diamonds.
Mineralogical assemblage, geophysical signatures and
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geochemical attributes of the primary rock become the
implements for integrated approach in prospecting.
Airborne Surveys and remote sensed data were inducted
in the search process. By now, there are sufficient
numbers of discoveries world-over to establish the
pattern of distribution in tectonic set-up and localisation
in plate-tectonics model. The broad outlines used in
exploration for primary source of diamonds can be
summarised in the following lines:
Kimberlite Emplacement Model: Theoretical facts
World over, majority of the kimberlites are emplaced
in ancient cratonic blocks (Clifford, 1966) or where
Archaean basement is underlain by deep lithospheric
keels (Haggerty, 1986).
Productive or diamond bearing kimberlites are
emplaced in areas where surface heat flow is generally
less than 40 Mw/m2 .This condition is suitably available
in majority of the cratonic blocks.
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Ascent of Kimberlite melt is from upper mantle but
below the graphite-diamond stability surface (i.e.,
where pressure >45 to 60 Kb 150-200 km depth and
temperature from 900 to 1300 C).
The melt intrudes through pre-existing deep-seated
faults / fractures, which have their roots in upper
mantle.
The deep-seated faults and deep-seated fractures are
well depicted on the imageries in the form of
lineaments traversing hundreds of kilometres.
After reaching the lower level of the upper crust, the
melt finds a number of shallow faults / fractures in its
way. The melt intrudes into these weak zones of varied
orientation in the form of cones and dykes.
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The kimberlites are usually localised in zones of high
magmatic permeability and repetitive basic and ultra
basic magmatic activity (Kaminski et al., 1995).
The kimberlites occur in clusters and such clusters
make a field (of 1 to 50 or more bodies Janse, 1984,
Mitchell, 1986) and a set of fields a province.
Mantle-xenoliths and xenocrysts, along with
diamonds, are inducted into the accelerated ascent of
melt from the upper mantle environment and diamond
stability zone.
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The above stated facts could serve as conceptual model
or exploration model in search of the locales of the
primary sources for diamonds. A detailed account on
conceptual facts in diamond exploration and
identification of prioritised zones in India was dealt in
the compilation work of Satyanarayana, 2000.
With the advent of GIS technology with tessellation
and vector encoding, the integrated approach in search
of the primary sources for diamond also get a boost in
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identification of statistically derived favourable zones
from the overlaid predictive thematic layers.
The present work constitutes an analysis for mineral
targeting in 2953 sq km area falling in the Narayanpet
Kimberlite Field, southern India, where integration of
evidence maps is attempted on 'Index Overlay Model'
coupled with 'Bayesian Probability Principle' in a
vector GIS.
Narayanpet Kimberlite Field
The Geological Survey of India since 1984-85 has been
discovering Kimberlite bodies in Maddur-Narayanpet
area (Nayak et al., 1987: Sarma, 1990; Rao, 1995),
Mahboob nagar district of Andhra Pradesh. Till date
more than 30 Kimberlite bodies have been located, now
this area which is designated as Narayanpet
Kimberlite Field (NKF) (Satyanarayana et al., 1997)
is a promising zone for kimberlites after the well-known
Wajrakarur Kimberlite Field (WKF) in Dharwar
Craton. The NKF measures about 60 km x 40 km in
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western part of Mahboob nagar district, Andhra Pradesh
and eastern part of the adjoining Gulberga district,
Karnataka. The kimberlites of both the fields have
broad similarities in mineralogical, petrological
characters and major element chemistry, but there are
subtle differences in REE distribution, indicator
minerals and mantle nodules (Rao et al., 2001). Thekimberlites of both NKF and WKF are in similar
tectonic set-up, and of contemporary ages (Anil Kumar
et al., 1993).
Geology
The area comprises broadly Archaean gneisses,
migmatites and granites with enclaves of schistose
rocks, Proterozoic granitic intrusive and sediments of
Bhma Group and Cretaceous Deccan Basalts.
Emplacement of kimberlites of the NKF is noticed in
the gneisses and granites (Rao et al., 1998). For
prognostication purpose, the geology is simplified into
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three litho domains i.e., gneissic domain, granitic
domain and basalt domain.
Tectonic Elements
A number of faults, fractures and lineaments of varied
dimensions, in different orientation related to different
tectonic events are recorded in the area. Some of these
are occupied by basic (including ultrabasic) intrusive,
pegmatites, quartzo-feldspathic veins, younger acidic
intrusive etc. These intrusive are suggestive of zones of
magmatic permeability and repetitive basic and
Ultrabasic magmatic activity in the area. The various
tectonic elements of this area are grouped into E-W,
NE-SW, N-S, NW-SE sets on the basis of their
orientation.
Geophysical Surveys
Multidisciplinary studies involving detailed satellite
imagery, aerial photo interpretation and ground
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geophysical surveys were initiated in the NKF to locate
new Kimberlite occurrences. Regional gravity and
magnetic surveys carried out (Reddy et al., 2001) and
interpretation of satellite and aerial photo data distincty
brought out the co-axial relations existing between
Geophysical anomaly linears and major lineaments /
faults (Rao, 2001). The computer based dataset for the
present work is derived from the analogue maps
generated during the actual surveys and field activities
in the NKF.
Present Work
Methodology
The present GIS Project involves the following stages:
1. Data capture from analogue maps
2. Derivation of theme based evidence maps
3. Spatial analysis using Bayesian Probability
Principle coupled with Index
Overlay Model for mineral targeting
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Data Capture: Approach in search of new targets in an
area is mostly based on the philosophy of known to
unknown. In this respect the first step was to bring out
all the appropriate data together into GIS database. The
foresaid maps were digitised into GIS software
Arc/Info version 8.2. The software captures data into
workspace-coverage storage model.
Two types of datasets i.e., evidence set and over
plotting set, are delineated among the captured spatial
dataset. The evidence spatial dataset comprising those
discrete geographic objects evidencing the causativerole in the emplacement process or defining the
signatures of probable locales of Kimberlite and allied
rocks in NKF. And thus they are of predictor nature in
Prognostication process. The spatial database from theevidence set are:
i. Lithology
ii. Structurefaults/fractures and lineaments
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iii. Gravity contours interpreted too high and low axes
represented as lines
iv. Ground magnetic contours interpreted too high and
low anomaliesrepresented as lines
The other set represents the existing Kimberlite
prospects in the NKF: The database of 33 discrete
known bodies of Kimberlite and allied rocks.
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The dataset was captured, populated with attribute
values, assigned Polyconic projection with map-centre
of the mosaic of four 1:50,000 topobases as origin of
co-ordinate axes, edge-matched and mosaiced, thereby,
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all the themes were brought to a common reference
system. This facilitated overlaying and over plotting of
spatial databases for GIS analysis. Study of over plotted
digital maps is helpful in revealing the pattern of
occurrence, field controls on known bodies and
theoretical understanding of emplacement model for
known bodies.
Visual analysis in understanding the pattern of
distribution of kimberlites in NKF aided in
understanding the controls on Kimberlite emplacement.
Small outcrops of geographic object like Kimberlite -represented by points on the digital map may have three
fundamental distribution patterns i.e., a) Complete
spatial randomness, b) Clustered pattern and c) Regular
pattern. The kimberlites of WKF and NKF represent a
clustered pattern. Each cluster has limited aerial spread
And the spread of kimberlites of NKF is in the form of
an ellipse. On the whole, the ellipse precisely defines
the Kimberlite points. The major axis of this elliptical
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distribution suggests a probable E-W control on the
Kimberlite emplacement. But the ellipse has a
considerably broad secondary axis perpendicular to the
major axis, which suggests role of additional controls
besides the E-W control.
A)An ellipse define overall distribution of kimberlites of NKF B) Lithologicaldomains and distribution of kimberlites of NKF
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Over-plotting of the Kimberlite occurrences on the
generalised Lithological domains (comprising gneisses,
diapiric granites and Deccan Basalt) brings out a
modified distribution pattern influenced by the litho-
domains. Though the number of known Kimberlite
occurrences is almost equal in gneiss and granite (17
and 16 respectively), the controls on distribution pattern
in these two Lithological domains is attributed to
different sets of faults. The major axis of orientation of
the Kimberlite distribution is E-W in gneissic domain,
whereas it is NNW-SSE in granitic domain.
Most of the theoretical concepts of Kimberlite
emplacement hold good for the NKF. The emplacement
model for kimberlites emphasises - clustering in a form
of nest of crustal faults and fractures in proximity of a
mega lineament. The mega lineament in NKF is traced
from the Cuddapah Basin to the east that extends
hundreds of kilometres in E-W disposition. It is thus
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Obvious that the linear geographic objects like faults,
lineaments and gravity axes have controls on the pattern
of distribution of kimberlites. The directional sets of
feature-elements have different degree of controls, i.e.,
from significant to least significant.
Derivation of theme based evidence maps: GIS
provides unlimited opportunities to make observations
on over-plotted thematic maps, delineation of predictive
features and understanding their role in identification of
prognostication zones. Following the understanding of
the cause and effect relationships, the second phase in
an analysis process, is to manipulate the data so as to
derive theme base evidence maps. Table 1 gives the
details of the need for manipulation, the analysis toolapplied in spatial data manipulation and its objective as
a part of prognostication over the Narayanpet dataset:
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Table 1: Manipulation, a chain of operations to make
the data usable in analysis process
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A chain of operations through near, buffer and statistics
reporting tools are utilised to establish the precisely
defined proximity between the predictive features of
different themes and locales of any economic
consequence (target). 'Near tool' - provides opportunity
to define the closest nearness in quantitative terms,
between the predictive line features (for present studyfaults & lineaments and interpreted gravity linears) and
the target point objects (kimberlites).
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Spatial analysis using Bayesian Probability Principle
coupled with Index Overlay Model for mineral
targeting:
Index overlay model (Bonham-Carter, 1996 and
Westen, 1997) is selected for analysis with the
Narayanpet evidence dataset. The procedure of the
model is that each class of a predictive map is given
different score as well as each predictive thematic map
itself receives different weight. Before integration into a
multi thematic map, each class or subset of a thematic
map was weighted, ranking on a suitability scale (09,maximum 10 weighted classes). The spatial association
between known occurrence and the predictive datasets
are used to workout weights, which were applied to
predictive areas with similar characteristics to the
known occurrence.
On account of standardising the weights for classes or
sub-sets of a theme or for inter-thematic maps, factor (f)
of posterior probability upon prior probability on
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the notion of Bayesian Probability Principle were
undertaken. Prior probability is the ratio of known
kimberlites (33) upon area (NKF). The expression for
prior probability is the unbiased distribution of
kimberlites per sq km area. Meaning thereby, the factor
of prior probability is an expression of predictability at
a point in the area when none of the thematic controlsof predictive maps are taken into account. Whereas,
posterior probability is the ratio between circumscribed
Kimberlite bodies upon summed up area of the
predictive zone (inside of the buffered zone). Thus, the
posterior probability is a biased probability for a
specific case. With the bias of known controls on the
emplacement of kimberlites, there is always an
improved degree of predictability for a point within the
predictive zone. Assuming a common weight base (= 2)
for the outside the predictive zone, the weight (integer)
for inside is subscribed (Table-2) by multiplying weight
base and probability factor (f). Thematic integration of
subsets was carried out followed by integration into a
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multi thematic map i.e., the subset-evidences together
make a factor map. Addition of weights and
normalisation was carried out in a newly created field in
attribute table. Finally combining by union algorithms
the three factor maps, namely-
1. Fault and lineament factor map,
2. Gravity factor map and
3. Magnetic factor map result into the favourability map
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* Prior probability of whole area or Dharwar Craton.
# Relatively higher score is prescribed than the ratio.
This is for highest upper crustal perforation along the
set of faults (NW-SE) and the zone around has
maximum number of Kimberlite localised.
The favourability map generated on the combine of the
Bayesian Probability Principle and Index Overlay
Model, is a decision support map. The merit of the
model could be realised when this map at decision
making stage was over-plotted with Kimberlite
occurrence map in ARCMAP of ARCGIS Version 8.2,
for each selection of a set of polygons qualifying a
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normalised weighted value or range of value one can
have selection by location of the Kimberlite bodies
falling within concerned polygons. A degree of
confidence can be expressed in terms of known
kimberlites falling within area acquired by the
polygons. So, at a decision making stage, for each
unique normalised weight classes the decision makerhave both figures - statistically summed up area and
improved confidence level (Table-3, column-e).
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For example, polygons selected for high range of
weights (8 to 9) make patches, having a total area of 80
sq km, which contain 3 known Kimberlite bodies in it.
A higher ratio 3 nos/80 sq km (target area) upon 33
nos/2953 sq km (project area) propose an improved
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degree of confidence in the zone which is higher by
3.37 times from prior probability.
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Conclusion
A chain of operations on near, buffer and statistics
reporting tools are utilised to establish the precisely
defined proximity between the predictive features of
different themes and zones of any economic
consequence.
Using the proximity figures and sub-thematic weights
resulted from proximity and probability driven analysis
approach in working out a model for the Narayanpet
Kimberlite Field could be applied on the whole to the
Dharwar Craton for locating new nest of faults and
fractures along mega-lineaments with any yield
expectancy zones around them for kimberlites or related
rocks. Most appreciating aspect of the model is the
factor degree of confidence in a class or
predictability of a class of the final favourability map,
which prompts a prospector or a decision making
authority to arrive at a right kind of decision. The
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model is for close observation in light of new discovery
in the NKF in recent years. In light of the fact that
kimberlites of the NKF are in similar tectonic set-up
with age-wise contemporaneous to WKF (~1100 Ma,
Radiometric dating of the bodies), the model has a
scope to improve and widen its application throughout
the Dharwar Craton, which depicts heat flow regime of< 40 Mw/ m2 (Heat flow map of India, after Ravi
Shanker, 1988) that is favourable for diamond stability.
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Mineral Prediction using remote sensing and
GIS in Rajasthan
ABSTRACT
The Indian state of Rajasthan is rich in mineral deposits
including asbestos, mica, barite and talc. Integration of
available background information (viz. geology,
tectonics, mineralogy and geomorphology) in GIS
environment leads to development of MineralExploration Geographic Information System. This study
examines the use of remote sensing in geological
studies in south east Rajasthan. Landsat Enhanced
Thematic Mapper plus (ETM+) and TM were used to
(i) classify the various geological units found in study
area
(ii) Discriminate the Lithology and structure of this
area
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(iii) Delineate the associated zones for mineral
identification.
A wide variety of digital image processing techniques
were applied such as the principal components analysis.
The colour composite of principal components (1, 2,
and 3), enabled us to determine the different types of
sedimentary rocks in the study area. The selective PCA
of ETM+ using bands 1, 3, 4, and 5 was used in
mapping iron and iron oxide bearing minerals. GIS can
describe and analyse interactions, to make predictions
with models and to provide support for decision-makers. Steps of mineral potential mapping includes
identify mineralisation recognition criteria, data
preparation and structuring, producing factor maps and
combining factor maps in the appropriate inference
networks. In this research, conventional models for
combining factor maps have been investigated and GIS
model selected in mineral deposit exploration in
detailed stage. An integration model using of
appropriate models has also been proposed.
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INTRODUCTION
Aravalli Mountain range hosts several economically
viable mineral deposits. The mineral deposits found in
this area include lead-zinc deposits, copper deposit,
tungsten deposit and several other industrial mineral
deposits. A careful study indicates a close association of
most of the mineral deposits to the neighbouring
fault/thrusts. Remote sensing (RS) and Geographical
Information System (GIS) tools, mineral composite
characteristics (ferrous minerals (FM), iron oxide (OI),and clay minerals (CM)) of south east Rajasthan were
integrated and mapped. The resulting mineral
composition index maps were summarised in nine
classes by using natural breaks classification method
in GIS. Using field data for which their geographic
coordinates had been determined by global positioning
system (GPS), developed MC maps were verified and
found dependable for environmental studies.
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OBJECTIVE
The objectives were:
To understand the geologic and geomorphic setting
of study area.
To understand image lineament and their
importance in mineralisation zone.
To understand the mineralisation pattern using
remotely sensed data and GIS modelling.
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Materials and Methodology
Materials:-
Spatial Data and Non- spatial data: Topographic
sheet (scale 1:50,000 and 1: 2,50,000) and remote
sensing data - IRS LISS III, SRTM data, thematic
Mapper (TM), enhanced thematic Mapper (ETM+),
geological maps and district resources maps.
Software:ArcGIS 9.2, ERDAS IMAGINE, ILWIS,
AutoCAD Map.
Methodology:-
The methodology is based on mineral composition
and normalised difference vegetative (NDVI) indices
(Tucker 1079; Sabins 1987; Jensen 1996; Campbell1996). Mosaicing is the first step for Landsat ETM+
data and topographical data. The mosaic image was
subset the area of interest by using vector AOI that
was created on the map of S-E Rajasthan. Geological,
geomorphologic, lineament, ferrous mineral, iron
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oxide, clay minerals and mineral composition map or
thematic layers was prepared. Then, weightages map
of all layers for overlay analysis were prepared.
Study Area:-
The study area is located in the S-E part of Rajasthan.
The main minerals present in the study area are Pb,
Zn, Cu and gold. The study area (figure 1) extends
between Latitude - 240 40 - 250 37 N and
Longitude - 740 50 - 750 40 E. The covering area is
7549.45 km.
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RESULT AND DISCUSSION
Geology: Rajasthan is endowed with a continuous
geological sequence of rocks from the oldest Archaean,
metamorphites, represented by Bhilwara Super Group
to sub-recent alluvium and windblown sand. The
sedimentaries include the rocks of Aravalli Super
group, Delhi Super group, Upper Precambrian
Vindhyan Super group and of Cambrian to Jurassic,
Cretaceous and Tertiary ages. The south-eastern
extremity of the state is occupied by a pile of basaltic
flows of Deccan traps of cretaceous age (figure 2 (a)).
Geomorphology: Geomorphology is the science of the
evolution and the development of the landforms. It is
concerned mainly with the study of the landforms and
their genesis on the earths surface. The landforms are
carved and/or developed on earth surface as a
consequence of weathering and erosion and/or
deposition.
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Different types of landforms are available in the study
area. These include alluvial plain, pediplain,
denudational hill, dissected hill, plateau, residual hill,
structural hill, valley fill and ridges (fig.2 (b)).
Figure2(a) geological formation, (b) Geomorphological
map of the study area
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GIS modelling for minerals: The study of minerals is
called mineralogy. It includes mineral identification and
description, the classification of mineral groups and the
study of where minerals occur. Minerals have an
internal, crystalline structure. Molecules that make up
the minerals are arranged in a set pattern. This
crystalline structure is sometimes reflected as a crystalif the mineral is given room to grow. However, even
organic substances can grow crystals (figure 4, 5).
Figure 4. (a) Clay minerals (b) ferrous minerals of the
study area
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Figure 5. (a) Iron oxide (b) mineral composition map
of the study area
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CONCLUSION
In this study, mineral composition raster maps of the
study area were studied using remote sensing and GIS.
The overall results indicated that a considerable amount
of area has moderate iron oxide but poor ferrous and
clay mineral contents. Negative correlations between
iron (FM, IO) and clay (CM, clay %) variables
suggested that the dominant clay minerals of the study
area have low iron content (figure 6).
The known mineral occurrences in SE Rajasthan are
controlled by the Jahazpur Thrust and Great Boundary
Thrust. The lineament length, lineament density and
lineament intersection density, all these indicate NE-
SW as dominant lineament trend in SE Rajasthan.
Barite, steatite and tin are other important minerals
present in SE Rajasthan. The techniques employed in
the present study may be extended to prepare the
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mineral potential map in other potential areas for
mineral exploration programme.
SUMMARY:-
As population increases and more nations industrialize
the demand for natural resources continues to increase.
Growing pressure for environmental sustainability and
the spread of population centres has driven the search
for economically viable mineral deposits into more
remote and desolate regions. Increased competitiveness
and price consciousness from investors has produced
intense pressure to maximize the return on exploration
capital expenses. These are among the many factors
contributing to the increased use ofremote sensing in
geologic mineral exploration. As technology
improves so will geologists ability to gather even more
detailed data information from remotely sensed data.
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