Artificial Neural Networks for Mineral-Potential Mapping: A Case Study from Aravalli Province, Western India
Tóm tắt
This paper describes a GIS-based application of a radial basis functional link net (RBFLN) to map the potential of SEDEX-type base metal deposits in a study area in the Aravalli metallogenic province (western India). Available public domain geodata of the study area were processed to generate evidential maps, which subsequently were encoded and combined to derive a set of input feature vectors. A subset of feature vectors with known targets (i.e., either known mineralized or known barren locations) was extracted and divided into (a) a training data set and (b) a validation data set. A series of RBFLNs were trained to determine the network architecture and estimate parameters that mapped the maximum number of validation vectors correctly to their respective targets. The trained RBFLN that gave the best performance for the validation data set was used for processing all feature vectors. The output for each feature vector is a predictive value between 1 and 0, indicating the extent to which a feature vector belongs to either the mineralized or the barren class. These values were mapped to generate a predictive classification map, which was reclassified into a favorability map showing zones with high, moderate and low favorability for SEDEX-type base metal deposits in the study area. The method demarcates successfully high favorability zones, which occupy 6% of the study area and contain 94% of the known base metal deposits.
Tài liệu tham khảo
Agterberg, F.P., 1974, Automated contouring of geological maps to detect target areas for mineral exploration: Math. Geology, v.6, no.4, p. 373–395.
Agterberg, F.P., Bonham-Carter, G.F., and Wright, D.F., 1990, Statistical pattern integration for mineral exploration, in Gaál, G., and Merriam, D.F., Computer Applications in Resource Estimation Prediction and Assessment for Metals and Petroleum: Pergamon Press, Oxford, p. 1–21.
Brown, W.M., Gedeon, T.D., Groves, D.I., and Barnes, R.G., 2000, Artificial neural networks: a new method for mineral prospectivity mapping: Australian Jour. Earth Sciences, v.47, no.4, p. 757–770.
Deb, M., 2000, VMS deposits: geological characteristics, genetic models and a review of their metallogenesis in the Aravalli range, NW India. in Deb, M., ed. Crustal Evolution and Metallogeny in the Northwestern Indian Shield: Narosa Publishing House, New Delhi, p. 217–239.
Deb, M., and Thorpe, R.I., 2001, Geochronological constraints in the Precambrian geology of northwestern India and their met-allogenic implications: Proc. Intern. Workshop on Sediment-hosted Lead-Zinc Deposits in the Northwestern Indian Shield, New Delhi and Udaipur, India, p. 137–152.
Goodfellow, W.D., 2001, Attributes of modern and ancient sediment-hosted, sea-floor hydrothermal deposits: Proc. Intern. Workshop on Sediment-hosted Lead-Zinc Deposits in the Northwestern Indian Shield, New Delhi and Udaipur, India, p. 1–35.
GSI, 1981, Total intensity aeromagnetic map and map showing the magnetic zones of the Aravalli region, southern Rajasthan and northwestern Gujarat: Geol. Survey India, Hyderabad, India, 4 sheets, scale 1:250,000.
Gupta, S.N., Arora, Y.K., Mathur, R.K., Iqballuddin, Prasad, B., Sahai, T.N., and Sharma, S.B., 1995a, Lithostratigraphic map of Aravalli region, 2nd edition, scale 1:250,000, Geological Survey of India, Calcutta, India, 4 sheets.
Gupta, S.N., Arora, Y.K., Mathur, R.K., Iqballuddin, Prasad, B., Sahai, T.N., and Sharma, S.B., 1995b, Structural map of the Precambrian of Aravalli region (2nd edn.): Geological Survey of India, Calcutta, India, 4 sheets, scale 1:250,000.
Gupta, S.N., Arora, Y.K., Mathur, R.K., Iqballuddin, Prasad, B., Sahai, T.N., and Sharma, S.B., 1997, The Precambrian geology of the Aravalli Region: Geol. Survey India, Mem. v. 123, Geol. Survey India, Hyderabad, India, 262p.
Haldar, S.K., 2001, Grade-tonnage model for lead-zinc deposits of Rajasthan, India: Proc. International Workshop on Sediment-hosted Lead-Zinc Deposits in the Northwestern Indian Shield, New Delhi and Udaipur, India, p. 153–160.
Harris, D.P., and Pan, G.C., 1991, Consistent geological areas for epithermal gold-silver deposits in the Walker Lake quadrangle of Nevada and California delineated by quantitative methods: Econ. Geology, v.86, no.1, p. 142–165.
Harris, D.P., and Pan, G.C., 1999, Mineral favorability mapping: a comparison of artificial neural networks, logistic regression and discriminate analysis: Natural Resources Research, v.8, no.2, p. 93–109.
Haykin, S., 1994, Neural networks: a comprehensive foundation (2nd edition): Prentice Hall, Upper Saddle River, NJ, 842p.
Heron, A.M., 1953, The geology of central Rajputana: Geol. Survey India Mem. v.79, no.1, Geol. Survey India, Calcutta, India, 389p.
Kemp, L.D., Bonham-Carter, G.F., Raines, G.L., and Looney, C.G., 2001, Arc-SDM: ArcView extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, http://ntserv.gis.nrcan.gc.ca/sdm/.
Looney, C.G., 1997, Pattern recognition using neural networks: theory and algorithms for engineers and scientists: Oxford Univ. Press, New York, 458p.
Looney, C.G., 2002, Radial basis functional link nets and fuzzy reasoning: Neurocomputing, v.48, no.1–4, p. 489–509.
Looney, C.G., and Yu, H., 2001, Special software development for neural network and fuzzy clustering analysis in geological information system, http://ntserv.gis.nrcan.gc.ca/sdm/.
Macleod, I.N., Jones, K., and Dai, T.F., 1993, 3D analytic signal in the interpretation of total magnetic field data at low magnetic latitudes: Exploration Geophysics, v.24, no.3–4, p. 679–687.
Masters, T., 1993, Practical neural network recipes in C++: Academic Press, New York, 493p.
Masters, T., 1995, Advanced algorithms for neural networks: A C++ Source book: Academic Press, New York, 431p.
Menzie, W.D., and Mosier, D.L., 1986, Grade and tonnage model of sedimentary exhalative Zn-Pb, in Cox, D.P. and Singer, D.A. Mineral deposit models: U.S. Geol. Survey Bull. 1693, p. 212–215.
Milligan, P.R., Morse, M.P., and Rajagopalan, S., 1992, Pixel map preparation using the HSV colour model: Exploration Geophysics, v.23, no.1–2, p. 219–224.
Parzen, E., 1962, On estimation of a probability density function and mode: Annals Math. Statistics, v.33, no.4, p. 1065–1076.
Porwal, A., Carranza, E.J.M., and Hale, M., 2003, Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping: Natural Resources Research, v.12, no.1, p. 1–25.
Roest, W.E., Verhoef, J., and Pilkington, M., 1992, Magnetic interpretation using 3D analytic signal: Geophysics, v.57, no.1, p. 116–125.
Roy, A.B., 1988, Stratigraphic and tectonic frame work of the Aravalli Mountain Range, in Roy, A.B., ed. Precambrian of the Aravalli Mountain Rajasthan, India: Geol. Soc. India Mem. v.7, p. 3–31.
Singer, D.A., and Kouda, R., 1996, Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district, Japan: Math. Geology, v.28, no.8, p. 1017–1023.
Singer, D.A., and Kouda, R., 1997a, Classification of mineral deposits into types using mineralogy with a probabilistic neural network: Nonrenewable Resources, v.6, no.1, p. 69–81.
Singer, D.A., and Kouda, R., 1997b, Use of a neural network to integrate geoscience information in the classification of mineral deposits and occurrences. in Gubins, A.G. ed. Proc. Exploration 97: Fourth Decennial Intern. Conf. Mineral Exploration, p. 127–134.
Singer, D.A., and Kouda, R., 1999, Comparison of weights of evidence and probabilistic neural networks: Natural Resources Research, v.8, no.4, p. 287–298.
Sugden, T.J., Deb, M., and Windley, B.F., 1990, The tectonic setting of mineralization in the Proterozoic Aravalli-Delhi orogenic belt, NW India, in Naqvi, S.M. ed., Precambrian Continental Crust and its Economic Resources: Elsevier, Amsterdam, p. 367–390.