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IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India, 2002
6 data were found to be low due to unknown reasons, however,
it is assumed that the shift between the actual and the satellite-
measured temperatures is uniform.
7. APPLICATION OF GIS
One of the most significant capability of GIS has been the
integration / analysis of spatial datasets derived from different
sources to describe the spatial associations and to predict the
spatial phenomena (Bonham-Carter, 1994). This capability of
GIS has been explored by the geologists in different parts of the
world for mineral potential mapping (Bonham Carter et al.,
1988 and 1989; Shulman, 1992; Bhattacharya et al, 1993;
Mukhopadhyay et al, 2002). There are many methods for
combining the spatial datasets. In the present study, weights of
evidence method has been used for integrating the remote
sensing, geological and geophysical datasets for locating the
favorable zones of mineralization in the study area because of
the objectivity in weights determination and popularity in use.
It involved the following steps — (i) selection of evidential
themes based on the conceptual deposit model and the
availability of data, (ii) conversion of multi-class evidential
themes into binary form, (iii) calculation of weights and (iv)
preparation of mineral favourability map estimated as posterior
probability. All these steps have been performed in Arcview
GIS software.
The geological map (after Basu, 1971; Patel, 1987 and AMSE,
1979), digital elevation model, remote sensing-derived spectral
anomalies and geophysical maps have been used as evidential
themes in the binary form. The known mineral deposit map
(Patel, 1987) has been used as a vector layer of training points
for calculating the weights (W* and W^, which in turn have
been used to prepare the posterior probability map (Figure-3).
In this map, the higher posterior probability indicates the higher
favourability for mineralization. The comparison of this map
with the proved reserves in different blocks (Patel, 1987)
indicates that the favorable zones appearing in red and orange
colours account for about 70% of the total reserves proved in
the study area. However, there are few red and orange colour
zones where significant ore reserves do not exist, though there
are evidences of mineralization. In addition, the GIS analysis
also shows the presence of a favorable zone in the eastern
outskirts of Bhilwara town on Bhilwara-Suwana road where a
new zone of ferruginous quartzite has been found based on the
analysis of satellite data. It is worthwhile to mention here that
the present GIS analysis is highly constrained with the limited
availability of multi-source datasets and therefore, the
availability of high-resolution geophysical and geochemical
Posterior Probability
0-0
5 0 — 0.001
0.001 — 0.003
.03 — 0.00
muy 0-03 0.008
0.008 — 0.143
0.143 — 0.201
0.201 — 0.503
data sets would have highly improved the results.
8. CONCLUSIONS
Satellite data helped to locate the ferruginous quartzite, the host
rock for base metal mineralization in this area, away from the
Figure-3 Mineral favorability map prepared based on GIS
analysis. Black circles show high thermal inertia zones along
the mineralized belt.
known NE-SW trending Pur-Banera mineralized belt. The
preliminary chemical analysis of the rock sample collected at
this location shows high values of lead and zinc. The GIS
analysis, though constrained with the limited availability of
multi-source spatial datasets, predicted the mineral potential
zones to a fairly good extent. The ATI mapping carried out
using the ground-based temperature measurements and
satellite-borne thermal infrared data could prove to be a good
complementary tool in mineral potential mapping. The study
demonstrates that the remote sensing and GIS could be used as
effective complementary tools in mineral exploration.
9. REFERENCES
AMSE, 1979. Detailed report on ground evaluation of the
airborne geophysical anomalies of the project “Operation Hard
Rock,” Rajasthan. Unpublished Report of AMSE, GSI.
Bakliwal P.C., Ramaswamy, S.M., Padia, K.N. and Majumdar,
T.J., 1985. Evaluation of airborne panchromatic, CIR, and
multispectral data for base metal exploration in Pur-Banera and
Rajpur-Dariba mineralised belts, Bhilwara and Udaipur
districts, Rajasthan. Proc. Seminar Remote Sensing in Geology
and Mineral Targeting, Hyderabad, India, pp. 5-1 to 5-13.
Basu K.K., 1971. Base metal mineralisation along the Pur-
Banera belt, Bhilwara district, Rajasthan. Geol. Surv. Ind.
Miscellaneous Publication, 16, Basemetals (Part-I), pp. 153-
159.
Bhattacharya, A., Kamaraju, M.V.V., Mehta, D.S., Shrimal,
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mineral exploration : a case study from Rajpura-Dariba-Lunera
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360-366.
Bonham-Carter, G.F., Agterberg, F.P. and Wright, D.F., 1988.
Integration of geological datasets for gold exploration in Nova
Scotia. Photogrammetric Engg. and Rem. Sensing, 54(11), pp.
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Bonham-Carter, G.F., Agterberg, F.P. and Wright, D.F, 1989.
Weights of evidence modeling: a new approach to mapping
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Geological Survey of Canada paper 89-9, pp. 171-183.
Bonham-Carter, G.F., 1994. Geographic Information Systems
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Gupta, B.C., 1934. The geology of Central Mewar. Mem. Geol.
Surv. Ind., 65, Pt. 2, pp. 107-169.
Mehta N.S., Mehta D.S., Rajawat R.S., Shrimal, A.K. and
Bahuguna, I.M., 1994. Report on Geological potential of ERS-1
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