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Multi-criteria analysis in GIS environment for natural resource development - a case study on gold exploration
Discussion and
Conclusion
The final map is a posterior probability map, showing the
suitability of target area delineation for gold deposits. From the weights,
shown above it has been seen that the presence of lineaments down weights the
probability of gold mineralization, whereas, the presence of favorable
geochemical signature and lineament-proximity are strong positive factor. The
rock-type is a moderately favorable factor. This technique of multi-crietria
analysis in integrating several datasets of varied nature and modelling
uncertainties has worked out excellent in mineral resource development. The
posterior probability map has identified an unexplored gold potential zone in
additions to the known gold potential zones. Decision tree approach of spatial
data integration provides a way of identifying target areas for mineral
exploration and land resource evaluation and allocation. The inference network
is a powerful device for representing expert knowledge, fuzzy-logic and Bayesian
logic, allowing for the incorporation of uncertainties into the model. It has an
important advantage over expert systems that are limited to deterministic rules.
GIS with its flexibility of experimentation and with the inference net model and
ability to extract topological attributes from maps, works as a unique tool for
land resource evaluation and allocation.
Reference
- Bonham-Carter, G.F., F.P. Agterberg and D.F. Wright. 1988.
Integration of geological datasets for gold exploration in Nova Scotia.
Photogrammetric Engineering and Remote Sensing, 54, 1585-1592.
- Bonham-Carter, G.F. R.K.T. Reddy. 1990. Preliminary results using a
forward-chaining inference net with a GIS to map base-metal potential:
Application to Snow Lake Greenstone Belt, Manitoba, Canada. In Proceedings
International Workshop on Statistical Prediction of Mineral Resources, Wuhan,
China, Oct. 20-25, 1990.
- Bonham-Carter, G.F., R.K.T. Reddy, and A.G. Galley. 1995.
Knowledge-driven modeling of volcanogenic massive sulphide potential with a
geographic Information System. In Mineral Deposit Modeling. Geological
Association of Canada, Special Paper 40, pp. 735-749.
- Campbell, A.N., V.F. Hollister, R.O. Duda, and P.E. Hart. 1982.
Recognition of a hidden mineral deposit by an artificial intelligence program.
Science 217, 927-929.
- Carver, S. J., 1991. Integrating multi-criteria evaluation with
Geographic information systems, Int. Jour. Remote Sensing, 5, 3, 321-339.
- Duda, R.O., P.E. Hart, N.J. Nilsson, R. Reboh, J. Slocum, an d G.I.
Sutherland. 1977. Development of a computer-based consultant for mineral
exploration. Stanford Research Institute International, SRI International,
Artificial Intelligence Center, Final Report for SRI Projects 5821 and 6415,
Menlo Park, California, 193p.
- FAO, 1976. A framework for land evaluation. Soil Bulletin 32. Rome:
food and Agricultural organization of the United States.
- McCammon, R.B. 1990. Prospector III - towards a map-based expert
system for regional mineral assessment. In Statistical Applications in Earth
Sciences. Geological Survey of Canada, Paper 89-9, 395-404.
- Sahoo, N. R., and H. S. Pandalai, 1999. Integration of sparse
geological information in gold targeting using logistics regression analysis in
the Hutti-Maski schist belt, Raichur, Karnataka, India - A Case study, Natural
Resources Research, 8, 3, 233-250.
- Sahoo, N. R. and H. S. Pandalai, 2000. Secondary geochemical
dispersion in the Precambrian auriferous Hutti-Maski schist belt, Raichur,
Karnataka, India. Part I : Anomalies of As,Sb, Hg and Bi in soil and
groundwater, Jour. Geochem. Explor., (under Publication)
- Sahoo, N. R., H. S. Pandalai and A. Subramaniam, 2000. Secondary
geochemical dispersion in the Precambrian Hutti-Maski schist belt, Raichur,
Karnataka, India. Part II : Application of factorial design in the analysis of
secondary dispersion of As, jour. Geochem. Explor., (under publicatioon).
- Voogd, H., 1983. Multi-criteria evaluations for urban and regional
planning, London Princeton Univ.
- Zadeh, L. A., 1965, Fuzzy sets, Information and Control, 8, 338-353.
- Wackernagel, H, 1995. Multivariate geostatistics, an introduction
with applications, Springer-Verlag, Berlin-Heidelberg, New York, pp. 144-151.
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