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Remote sensing and GIS
for locating favourable
zones of lead-zinc-copper
mineralization in Rajpura-
Dariba area, Rajasthan, India
S. K. Srivastav a , A. Bhattacharya a , M. V. V.
Kamaraju a , G. Sreenivasa Reddy a , A. K. Shrimal
b , D. S. Mehta b , F. K. List c & H. Burger c
a National Remote Sensing Agency , Hyderabad,
500 037, India
b Department of Mines and Geology , Udaipur, 313
001, India
c Freie University of Berlin , Germany
Published online: 25 Nov 2010.
To cite this article: S. K. Srivastav , A. Bhattacharya , M. V. V. Kamaraju , G.
Sreenivasa Reddy , A. K. Shrimal , D. S. Mehta , F. K. List & H. Burger (2000)
Remote sensing and GIS for locating favourable zones of lead-zinc-copper
mineralization in Rajpura-Dariba area, Rajasthan, India, International Journal of
Remote Sensing, 21:17, 3253-3267, DOI: 10.1080/014311600750019877
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int. j. remote sensing, 2000, vol. 21, no. 17, 3253–3267
Remote sensing and GIS for locating favourable zones of
lead–zinc–copper mineralization in Rajpura-Dariba area,
Rajasthan, India
S. K. SRIVASTAV, A. BHATTACHARYA, M. V. V. KAMARAJU,
G. SREENIVASA REDDY,
National Remote Sensing Agency, Hyderabad—500 037, India
A. K. SHRIMAL, D. S. MEHTA,
Department of Mines and Geology, Udaipur—313 001, India
F. K. LIST and H. BURGER
Freie University of Berlin, Germany
Abstract. This paper reports the results of a pilot study carried out in Rajpura-
Dariba area, Rajasthan, for locating favourable zones of lead–zinc–copper
(Pb–Zn–Cu) mineralization using remote sensing, Geographical Information
System (GIS) and geostatistical modelling techniques. Remotely sensed data, both
aerial and satellite, were used to update the existing geological map. ATLAS GIS
software and multivariate geostatistical techniques were used to analyse and
integrate diŒerent types of geological and geophysical datasets. The Favourability
Index (FI) maps prepared during this study show the occurrence of three favour-
able zones for Pb–Zn–Cu mineralization. They are: (i) around and north of
Rawan ka Khera; (ii) isolated spots between Ruppura and Bhupalsagar; and
(iii) north of Dhani. Selective geochemical sampling and resistivity proŽ ling
carried out in these favourable zones indicated the presence of geochemical
/
anomalies (anomalous concentrations of Zn and Cu) and low moderate resistivity
zones, respectively. Recent drilling carried out by the Department of Mines and
Geology (DMG), Rajasthan, at about 2.5 km north of Rawan ka Khera (one of
the predicted favourable zones) indicated evidence of Cu mineralization at a depth
of about 70 m.
1. Introduction
The biggest challenge before geoscientists today is to discover new mineral
deposits, as most of the shallow deposits have either been located or are being
exhausted (Rao 1996). The conventional methods of mineral exploration involve a
high degree of risk, in addition to being time-consuming and cost-prohibitive. In
order to overcome these limitations, innovative techniques for resource surveillance
and data integration have to be used. Remote sensing and Geographical Information
System (GIS) techniques have emerged as powerful tools in recent times for
assessing/predicting mineral resources or isolating the favourable areas from
Paper presented at ICORG ’97 International Conference on Remote Sensing and
/
GIS GPS Hyderabad, India, 18–21 June 1997.
International Journal of Remote Sensing
ISSN 0143-1161 print/ISSN 1366-5901 online © 2000 Taylor & Francis Ltd
http://www.tandf.co.uk/journals

3254
S. K. Srivastav et al.
unfavourable ones. Remote sensing data are mainly used to: (i) update the geological
(lithological and structural) maps of the area; and (ii) map the hydrothermally
altered minerals. GIS, on the other hand, is used to integrate and analyse diŒerent
types of spatial datasets, such as geological, geophysical and geochemical maps.
Advanced GIS packages bridge the gap between the traditional manual overlay
approach and mathematical methods using multivariate statistics and image analysis
(Bonham-Carter et al. 1988). The spatial information from multiple sources can be
integrated using several methods; however, ‘weights of evidence (or probabilistic)’
and ‘Dempster-Shafer (or evidential)’ methods are more commonly used (Lee et al.
1987, Bonham-Carter et al. 1988, Kim and Swain 1989, Moon 1990, Shulman et al.
1992, Bhattacharya et al. 1993).
The well known Rajpura-Dariba multimetal deposit in Rajasthan witnessed
probably the earliest (# 3000 years BP; Department of Mines and Geology, personal
communication, Rajasthan) lead–zinc (Pb–Zn) mining in the world, as evident from
the records of old workings. The main deposit was located by the Geological Survey
of India (GSI) in 1963 (GSI 1977). Subsequent surface and subsurface exploration
by the GSI between 1963 and 1970 proved the existence of a mineralized belt nearly
17 km long between Dariba and Bethumbi through Rajpura and Sindesar Kalan
(Choudhary and Mathur 1973). The average Pb–Zn content of the ore is about
6.7% with subsidiary Cu, Ag, Sb, As and Hg (GSI 1977). The establishment of a
well-deŽ ned ore deposit near Rajpura-Dariba encouraged many geologists to carry
out detailed mineral exploration studies in surrounding areas for sulphide minerals.
With this background, the present study aimed to map the favourable locations
of lead–zinc–copper (Pb–Zn–Cu) mineralization in the Rajpura-Dariba area of
Rajasthan based on integration of geological and geophysical datasets using remote
sensing, GIS and multivariate geostatistical modelling techniques. The work reported
here pertains to part of the Indo-FRG collaborative project on mineral exploration.
2. Study area
2.1. L ocation
The study area, covering parts of the Rajsamand and Chittaurgarh districts of
Rajasthan, is located between latitudes 24ß50 and 25ß05 N, and longitudes 74ß05
¾
¾
¾
and 74ß15 E. It falls on the Survey of India (SOI) toposheets no. 45 K/4 and L/1.
¾
The area forms part of the Mewar-Bhilwara plateau and, with the exception of two
linear ridges—Dariba–Rajpura–Bethumbi in the west and Dhani-Rawan Ka
Khera–Bhupalsagar–Matuniya in the east—the entire area is represented by an
undulating pediplain with soil cover of varying thickness. Mining of lead–zinc min-
erals is in progress near Dariba. Udaipur, an important nearby city, is at a distance
of about 84 km from Dariba by road. The area enjoys a semi-arid climate with
extremely hot summers and cold winters. The mean daily maximum and minimum
temperatures are about 39ßC and 8ßC, respectively. The mean annual rainfall is
about 650 mm and is mainly restricted to monsoon months of July to September.
The location map of the study area is shown in Ž gure 1.
2.2. Geology
The belt of Rajpura-Dariba polymetallic sulphide mineralization runs for about
17 km from Dariba in the south to Bethumbi in the north through Rajpura, Sindesar
Khurd and Sindesar Kalan. A number of preliminary accounts of the geology and
the nature of mineralization at Rajpura-Dariba are available. Many geologists/

L ocating lead–zinc–copper mineralization
3255
Figure 1. Location map of the study area.
geophysicists of GSI and other organizations have studied the area and have gathered
information on geological and geophysical aspects of the entire region, and the
Rajpura-Dariba area in particular (Gupta 1934, Gupta et al. 1980, Raja Rao 1967,
1970, Raja Rao et al. 1972, Choudhary and Mathur 1973, Sharma et al. 1978,
Agarwal et al. 1980, Deb and Bhattacharya 1980, Roy et al. 1981, Samaddar and
Bohra 1986, Patel 1987, Roy 1988). According to the workers of GSI, the sulphide
bearing metasedimentary rocks in the Rajpura-Dariba area together constitute the
‘Rajpura-Dariba Group’, which are underlain by high grade metamorphites of the
‘Mangalwar Complex’. The rocks of the ‘Rajpura-Dariba Group’ and ‘Mangalwar
Complex’ together constitute the Bhilwara Super Group (pre-Aravallis). However,
according to Roy et al. (1981) and Roy (1988), the rocks of the Rajpura-Dariba area
form part of the Aravalli Supergroup. The chief litho-units constituting the ‘Rajpura-
Dariba Group’ are sulphide bearing calc-silicates having dolomite/dolomitic marble,
graphitic mica schists, calc-biotite schists and quartzites. The litho-units belonging

3256
S. K. Srivastav et al.
to the Mangalwar complex include garnetiferous mica schists, migmatitic mica
schists/gneisses and quartzites.
The general strike of the rock formations varies from N–S to NE–SW and dips
are steep ( 65ß) towards the east. The metasediments exhibit polyphase deformation
>
and regional metamorphism corresponding to amphibolite facies (GSI 1977). The
rock formations of the study area are reported to constitute part of a north-easterly
plunging regional syncline, the closure of which is supposedly exposed near Bhinder
in the south (Raja Rao et al. 1972). The Dariba-Rajpura-Bethumbi belt constitutes
the western limb and the Bhupalsagar-Jashma belt constitutes the eastern limb of
the regional syncline. There are, however, diŒerent views on the structure and status
of diŒerent parts of the belt (Choudhary and Mathur 1973, Sharma et al. 1978,
Samaddar and Bohra 1986, Agarwal et al. 1980, Ram Gopal and Agarwal 1993).
3. Mineralization
Rajpura-Dariba ores demonstrate a classic multimetal association of Zn–Pb–
Cu–Ag–Cd–As–P–F–S with minor Au, Mo, In and Hg. The major ore minerals are
sphalerite and galena which are associated with chalcopyrite, pyrite and pyrrhotite
(Mukherjee 1988). The average Zn–Pb content of the ores is about 6.7% with 5.5%
Zn and 1.2% Pb (GSI 1977). The ore bodies occur as broad layers of mainly
Pb–Zn–Cu sulphides conformable to bedding of associated graphite mica schists
and calc-silicate rocks (Deb and Bhattacharya 1980 from Mukherjee 1988). Restricted
occurrence of the Rajpura-Dariba ores to a typical sedimentary sequence, the pres-
ence of well-deŽ ned primary sedimentary features within it, the stratiform and Ž nely
laminated nature of the deposits prompted Poddar (1974) to ascribe a syn(dia)genetic
origin of the ores. Initially, the concentration of ores took place during diagenesis.
Further mobilization of ores occurred during the polyphase deformation and regional
metamorphism (Deb and Bhattacharya 1980).
3.1. Controls of mineralization
The base metal sulphide deposit of the Dariba-Rajpura-Bethumbi belt is marked
by its conŽ nement to the following controls of mineralization (Choudhary and
Mathur 1973):
1. A diagnostic in situ gossan and ferruginous chert breccia is formed in between
Dariba and Rajpura. The gossan zone has a dip of 65–80ß towards the east
and strikes N–S. The depth of oxidation is as deep as 400 m below the surface.
2. The sulphide ore bodies are invariably associated with graphitic mica schists
and calc-silicate rocks bearing dolomite/dolomitic marble belonging to the
Rajpura-Dariba Group.
3. A N–S strike fault dipping 65–80ß due east is indicated by the sheared graphitic
mica schists along the eastern margin of the gossan. It extends from near
Dariba in the south to Bethumbi in the north, and is parallel to the regional
structural trend (N–S to NE–SW) of the major litho-units. The outcrops of
diagnostic gossan and old workings along the graphitic schists-dolomite-chert
horizon and the fault zone indicate that the mineralization is controlled by
lithology and the workable lodes have been formed due to subsequent limited
remobilization along the fault zone.

L ocating lead–zinc–copper mineralization
3.2. Exploration strategy
3257
In addition to the detailed account of geology and mineralization discussed
above, geophysical surveys conducted by the GSI have also revealed diŒerent types
of geophysical anomalies related to sulphide mineralization (Sharma and Singh 1976,
Sharma et al. 1978, Agarwal et al. 1980). It has been shown that the Turam and
Magnetic methods are an eŒective combination in locating the potential zones of
sulphide mineralization in this area where graphitic mica schists form the main host
rock. The Turam responses associated with moderate magnetic anomalies are indicat-
ive of sulphide mineralization at depth in view of the known occurrences of pyrrhotite
with Pb–Zn–Cu sulphides invariably in this area.
Keeping in view the geological controls of mineralization and the results obtained
through geophysical surveys over the mineralized areas, the exploration strategy for
locating new zones of sulphide mineralization was essentially based on integrated
analysis of geological, geophysical and geochemical parameters. In this context, the
present study aimed to extract the geological information from remotely sensed data,
and to analyse and integrate it with the available geophysical data in a GIS environ-
ment using suitable geostatistical modelling tools with the objective of locating the
favourable zones of Pb–Zn–Cu mineralization in the surroundings of the known
mineralized belt of Dariba-Rajpura-Bethumbi.
4. Used data and data processing
The following datasets were used in this study:
1. Satellite data (IRS-1A LISS-II, Landsat TM and SPOT PLA/MLA) and
panchromatic aerial photographs to update the existing geological map at
1 50 000 scale
5
2. Airborne electromagnetic data acquired during ‘Hard Rock Operation’ by GSI
3. Total Ž eld airborne magnetic data acquired during ‘Hard Rock Operation’
by GSI
4. Ground geological, geophysical and geochemical data
5. Survey of India (SOI) toposheets at 1 50000 scale.
5
The satellite data, after geocoding with reference to SOI toposheets, were digitally
enhanced using linear contrast stretching, principal component analysis, Intensity-
Hue-Saturation (IHS) transformation and ratioing techniques. PC-based ERDAS
and EASI/PACE image processing software were used for this purpose. Then, the
enhanced satellite images and aerial photographs at 1 50 000 scale were visually
5
interpreted in conjunction with Ž eld checks to update the existing geological map
of GSI, which was used as a base. The major contribution of remotely sensed data
was in mapping the graphitic mica schists, which form the host rocks for sulphide
mineralization, at two hitherto unknown locations: (i) west of Bari village; and (ii) an
extension of these rocks from near Bhupalsagar town to Ruppura village. In addition,
the contacts between diŒerent formations were revised at several places in addition
to identiŽ cation and mapping of several faults/fractures/lineaments. It is worth
mentioning here that it was di cult to map the faults/lineaments running parallel
to the regional structural trend of the rock formations. In contrast, those oblique to
the regional trend could be mapped very well on the satellite imagery. Aerial
photographs facilitated identiŽ cation and mapping of minor strike-slip faults across
the quartzite ridges. The geological map of the study area (synthetic of information

3258
S. K. Srivastav et al.
derived from remotely sensed data and existing geological map) is shown in Ž gure 2.
The lithostratigraphy is given in table 1.
In order to carry out the GIS-based data analysis/integration, lithological, struc-
tural, aeroelectromagnetic and aeromagnetic datasets were used as inputs. The
lithological and structural details were taken from the geological map, shown in
Ž gure 2, in separate layers. The lithological layer consisted of 10 variables repres-
enting diŒerent litho-units including gossan, as shown in tables 1 and 2. The structural
layer consisted of three variables: (i) faults; (ii) fractures/lineaments, and (iii) trend
lines (table 2). The airborne electromagnetic data acquired by the GSI indicating
channel anomalies is shown in Ž gure 3. Contouring of original data was done in
order to use them in the GIS. These data formed the important geophysical inputs
as they indicate the conductivity of the rock formations and associated sulphide ore
bodies. The total Ž eld aeromagnetic contour map was divided into six classes of
magnetic highs and lows (Ž gure 4). These are: (i) magnetic high ( 47 500 gamma);
>
(ii) magnetic high (47 300–47 500 gamma); (iii) magnetic high ( 47 300 gamma); (iv)
<
magnetic low ( 47 200 gamma); (v) magnetic low (47 000–47 200 gamma); and
>
(vi) magnetic low ( 47 000 gamma). Further, magnetic high/low axes and discontinu-
<
ities were interpreted and used as inputs (Ž gure 5). These datasets were analysed
and integrated using GIS and weighted modelling techniques, discussed in the
next section.
5. GIS analysis
ATLAS GIS software, a PC-based software from Strategic Mapping Inc., USA,
was used to integrate the geological (lithological and structural) and geophysical
datasets. For this purpose, as discussed earlier, these datasets were digitized in the
layered form, wherein each individual layer consists of several variables (table 2). In
the case of line features, such as faults, fractures, magnetic axes, etc., buŒer zones of
250 m were created. The width of the buŒer zone was selected based on Ž eld
conditions. Subsequently, the weighted modelling technique was used to prepare
‘Favourability Index’ (FI) maps showing the potential areas for Pb–Zn–Cu mineraliz-
ation. The procedures suggested by Bhattacharya et al. (1993) were adopted for
calculation of weightages (using a binary approach), Overall Associativity (OA) and
Favourability Index (FI). The weightage (W ) of each variable was computed by
using two methods: (i) based on the binary approach and conditional probability
i
Figure 2. Geological map of the study area (modiŽ ed after GSI map). 1, Migmatitic mica
/
schists gneisses; 2, garnetiferous biotite-muscovite schists; 3, quartzites; 4a and b, calc-
/
silicate rocks bearing (a) dolomite dolomitic marble with minor quartz-chlorite-biotite
schist and amphibolite bands and (b) quartz-chlorite-biotite schist with minor bands
of dolomite and amphibolite; 5, calcareous biotite schists with minor
/
dolomite amphibolite bands; 6, kyanite-staurolite-garnet bearing graphitic mica schists
with intercalatory quartzite bands; 7, Sindesar Kalan Jashma Bhupalsagar quartzites;
/
/
/
8, ferruginous chert breccia; 9, gossan (limonitised graphitic mica schist siliciŽ ed dolo-
/
mite); —Ö—Ö—Ö— quartz vein; F—————F fault; ————— fracture lineament;
/
............ trend line; bedding; foliation schistosity; ————— lithological boundary (con-
Ž rmed); ---------- lithological boundary (inferred); ————— road; —|—|—|—|—
E
/ /
river stream; , tank water body; A, settlement. Settlement names:
##
railway line;
A, Kuraj; B, Bethumbi; C, Pachmata; D, Sindesar Kalan; E, Sindesar Khurd; F,
Rajpura; G, Dariba; H, Dhani; I, Jashma; J, Lunera; K, Bari; L, Dhaneriya; M,
Bhupalsagar.

L ocating lead–zinc–copper mineralization
3259

3260
S. K. Srivastav et al.
Table 1. Lithostratigraphy of the Rajpura-Dariba area (based on GSI map).
/
Quartz pegmatite veins
/
Gossan (limonitised graphitic mica schists siliciŽ ed dolomite)
Ferruginous chert breccia
/
/
Sindesar Kalan Jashma Bhupalsagar quartzites
Kyanite-staurolite-garnet bearing graphitic mica schists with
intercalatory quartzites
Rajpura-Dariba Group
/
Calcareous biotite schists with minor dolomite amphibolite
bands
Calc-silicate rocks bearing (a) dolomite dolomitic marble with
/
minor quartz-chlorite-biotite schists and amphibolite bands
and (b) quartz-chlorite-biotite schists with minor bands of
dolomite and amphibolite
Unconformity
Quartzites
Garnetiferous biotite-muscovite schists
Mangalwar Complex
/
Migmatitic mica schists gneisses
method; and (ii) based on the areal extent of each variable in the control (or known
mineralization) area. In the Ž rst method, W is calculated using the following equation:
i
W
P{(V E)/E}
(1)
5
m
i
i
where V is the ith variable and E is the event, i.e. mineralization. In the second
i
method, W is calculated as the ratio of the areal extent of variable ‘i’ within the
control area and the total areal extent of the control area. The weightages of diŒerent
i
variables calculated using the above two methods are given in table 2. The weightages
calculated by the two methods are diŒerent in absolute sense, however, their relative
importance is maintained. This is because of the basic diŒerence in data format and
the method of weightage calculation. Since the binary approach is basically grid-
based, the mere presence of a particular variable contributes towards the weightage,
which is not true in case of the second method in which the weightage is calculated
based on the areal extent of the variable within the known mineralized area.
After calculating the weightages of diŒerent variables, overlay analysis was
performed in a GIS environment by taking two layers at a time, and FI maps were
prepared. The FI values of the resultant units, obtained after overlay analysis, were
calculated using the following equations:
OA
FI
(2)
5
maximum OA of the area
where,
N
OA
(W V )
Ö
(3)
5
ž
i
i
i=1
Further, the FI map was also prepared by changing the importance of lithological,
structural, electromagnetic and magnetic data, and recalculating the weightage of
each variable derived from the binary approach.
Essentially, the FI map indicates the percentage of the statistical similarity
between an unknown area and the control area where the mineralization is present.
It is also important to note that the FI map does not indicate whether a deposit is

L ocating lead–zinc–copper mineralization
3261
Table 2. List of variables and their corresponding weightages.
Weightage
Variable no.
Variable name
Method I* Method II**
Geological
V
Garnetiferous biotite-muscovite schists
Calc-silicates
/
Migmatitic mica schists gneisses
Quartzites
Gossan
Graphitic mica schists with intercalatory quartzites
Calcareous biotite schists with dolomite bands
/ /
Sindesar Kalan Jashma Bhupalsagar quartzites
0.0
0.6
0.0
0.0
1.0
1.0
0.0
0.1
0.0
0.0
0.8
0.1
0.0
0.0
0.4
0.0
0.0
1.0
0.6
0.0
0.0
0.0
0.0
0.2
0.04
0.0
1
V
2
V
3
V
4
V
5
V
6
V
7
V
8
/
V
9
Quartz pergmatite veins
V
10
V
11
V
12
V
13
Ferruginous chert breccia
Fault
/
Fracture Lineament
Trend line
Airborne electromagnetics
/
>
V
V
V
V
Channel 1 (in phase quadrature ratio5 3.0)
0.2
0.7
0.5
0.0
0.1
0.4
0.1
0.0
14
15
16
17
/
Channel 2 (in phase quadrature ratio5 1.6–3.0)
/
Channel 3 (in phase quadrature ratio5 1.5–0.75)
/
<
Channel 4 (in phase quadrature ratio5 0.75)
Airborne magnetics
>
Magnetic high ( 47 500 gamma)
Magnetic high (47 500–47 300 gamma)
V
V
V
V
V
V
V
V
V
0.0
0.5
0.2
0.6
0.3
0.0
0.3
0.4
0.2
0.0
0.0
0.1
0.1
0.1
0.0
0.1
0.2
0.1
18
19
20
21
22
23
24
25
26
<
Magnetic high ( 47 300 gamma)
>
Magnetic low ( 47 200 gamma)
>
Magnetic low ( 47 200–47 000 gamma)
<
Magnetic low ( 47 000 gamma)
Magnetic high axis
Magnetic low axis
Magnetic discontinuity
*Based on binary approach.
** Based on areal extent.
present. It only indicates that a particular area is more favourable than another
(Shulman et al. 1992). The work reported here is an improved model compared with
the previous one suggested by Bhattacharya et al. (1993), mainly because: (i) aero-
electromagnetic data were used as additional inputs; and (ii) FI zones were more
correctly deŽ ned spatially and follow the geometry of geological controls.
6. Results and discussion
The FI maps show that although the favourable zones in diŒerent models are
more or less matching, their FI values are diŒerent in each model. The study of
diŒerent FI maps indicates that the favourable locales for Pb–Zn–Cu mineralization
can be grouped into two zones. The Ž rst zone falls in the Dariba-Rajpura-Bethumbi
belt coinciding with the already lead–zinc mineralized belt, a part of which is under
exploration by the Hindustan Zinc Limited (HZL) and a part under detailed
exploration by the GSI. The second zone, a new zone, falls along the Dhani-Rawan

3262
S. K. Srivastav et al.
Figure 3. Map showing airborne electromagnetic anomalies (Source: GSI).
ka Khera-Bhupalsagar belt in the eastern part of the study area. The FI maps
pertaining to diŒerent models were integrated and a map showing the favourable
zones of Pb–Zn–Cu mineralization was prepared (Ž gure 6), considering a proper FI
value as a cut-oΠbased on FI values over known mineralized areas. Thus, the
favourable mineralized locales obtained along the Dhani-Rawan ka Khera-
Bhupalsagar belt are: (i) around and north of Rawan ka Khera village (along Jashma
ridge); (ii) isolated spots between Ruppura village and Bhupalsagar town; and
(iii) north of Dhani village (along Jashma ridge). In the Ž rst zone, at about 2.5 km
north of Rawan ka Khera village, a fault was noticed causing displacement in the
graphitic schists-quartzites ridge. Subsequently, selective resistivity proŽ ling was
carried out around the Ž rst favourable zone, which showed low to moderate resistivity
(or high to moderate conductivity) zones falling exactly over the favourable zones
identiŽ ed during this study. Further, a few stream/soil samples were collected over

L ocating lead–zinc–copper mineralization
3263
Figure 4. Airborne magnetic highs and lows interpreted from total Ž eld aeromagnetic map
of GSI (Source: GSI).
the potential and surrounding areas, and analysed for Pb, Zn and Cu contents. The
results indicate that the concentrations of Zn and Cu are anomalous near
Bhupalsagar and at about 2.5 km north of Rawan ka Khera, respectively.
Recent drilling carried out by the Department of Mines and Geology (DMG),
Rajasthan, in one of the favourable sites identiŽ ed through the present study at
about 2.5 km north of Rawan ka Khera along the graphitic schists-quartzites ridge,
has indicated evidence of Cu mineralization at a depth of about 70 m (DMG,
personal communication, Rajasthan). In view of the fact that in the Dariba-Bethumbi
mineralized belt, Cu mineralization (poor grade) occurs at shallow depth and is
followed by Pb–Zn mineralization at a deeper level, it is quite likely that even in
this area with a similar geological environment Pb–Zn mineralization may be enco-
untered at depth (DMG, personal communication, Rajasthan). Since this is the Ž rst
borehole drilled by the DMG, Rajasthan, in this area, which itself has met with
success, it has been suggested that a few more exploratory boreholes are drilled in
other favourable locations identiŽ ed during the present study after carrying out the
necessary ground-based geophysical and geochemical surveys.

3264
S. K. Srivastav et al.
/
Figure 5. Airborne magnetic high low axes and discontinuities interpreted from total Ž eld
aeromagnetic map (Source: GSI).
This study has highlighted the use of remote sensing, GIS and geostatistical
techniques for narrowing down the search areas for mineral prospecting.
Figure 6. Map showing the favourable zones for Pb–Zn–Cu mineralization obtained through
+
GIS-based analysis for planning detailed ground-based exploration. Favourable zone;
[
]
D
/
R1, resistivity proŽ le showing low moderate resistivity zone; S1, sample
L/M
location having anomalous concentration of Zn Cu; ————— road; —|—|—|—
R1
/
E
/ /
river stream; , tank water body; A, settlement. Settlement names:
###
railway line;
A, Kuraj; B, Bethumbi; C, Pachmata; D, Sindesar Kalan; E, Sindesar Khurd; F,
Rajpura; G, Dariba; H, Dhani; I, Jashma; J, Lunera; K, Bari; L, Dhaneriya; M,
Bhupalsagar; N, Rawan ka Khera; O, Ruppura.

L ocating lead–zinc–copper mineralization
3265

3266
S. K. Srivastav et al.
Acknowledgments
We are grateful to the Director, NRSA and Director, DMG, Rajasthan, for their
help and support for this work. We are grateful to Dr P. R. Reddy, Head, Geology
Division of NRSA, and S/Shri S. N. Kak, S. K. Das, V. J. Katti and A. S Chawla
of AMD, Hyderabad for fruitful discussions. Thanks are due to GTZ, Germany for
various support.
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