Abstract
The metallogeny of Central Iran is characterized mainly by the presence of several iron, apatite, and uranium deposits of Proterozoic age. Radial Basis Function Link Networks (RBFLN) were used as a data-driven method for GIS-based predictive mapping of Proterozoic mineralization in this area. To generate the input data for RBFLN, the evidential maps comprising stratigraphic, structural, geophysical, and geochemical data were used. Fifty-eight deposits and 58 ‘nondeposits’ were used to train the network. The operations for the application of neural networks employed in this study involve both multiclass and binary representation of evidential maps. Running RBFLN on different input data showed that an increase in the number of evidential maps and classes leads to a larger classification sum of squared error (SSE). As a whole, an increase in the number of iterations resulted in the improvement of training SSE. The results of applying RBFLN showed that a successful classification depends on the existence of spatially well distributed deposits and nondeposits throughout the study area.
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References
Behnia, P., 2004, Geospatial data modeling for mineral exploration in Saghand-Chadormalu area, Central Iran: unpubl. doctoral dissertation, Wuhan Univ., Wuhan, China, 184 p
Bohman-Carter G. F. (1994) Geographic Information Systems for geoscientists: Modelling with GIS. Pergamom Press, Oxford, p. 398
Broomhead D. S., Lowe D. (1988) Multivariable functional interpolation and adaptive networks. Complex Systems 2:321–355
Cheng Q., Agterberg F. P., Ballantyne S. B. (1994), The separation of geochemical anomalies from background by fractal methods. Jour. Geochemical Exploration 51(2):109–130
Earle, S. A. M., 1978, Spatial presentation of data from regional geochemical stream surveys: Inst. Mining and Metallurgy, Applied Earth Science (IMM Transactions section B), p. B61–B65
ESRI, 1995, Understanding GIS: The ARC/INFO Method: GeoInformation International, United Kingdom and John Wiley & Sons, New York, 551 p.
ESRI, 1996, ArcView Spatial Analyst. http://www.esri.com/software/arcview/extensions/spatialanalyst/
Hitzman, M. W., Oreskes, N., and Einaudi, M. T., 1992, Geological characteristics and tectonic setting of Proterozoic iron oxide (Cu-U-Au-REE) deposits: Precambrian Research: v. 58, no. 1–4, p. 241–287
Kemp, L. D., Bonham-Carter, G. F., Raines, G. L., and Looney, C. G., 2001, Arc-SDM: ArcView extension for spatial data modeling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, http://www.ige.unicamp.br/sdm
Looney C. (2002) Radial basis functional link nets and fuzzy reasoning. Neurocomputing 48:489–509
Moody J. E., Darken C. J. (1989) Fast learning in networks of locally-tuned processing units. Neural Computation. 1(2):281–294
Pan G. C., Harris D. P. (2000) Information synthesis for mineral exploration. Oxford Univ. Press, New York, p. 461
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An erratum to this article can be found at http://dx.doi.org/10.1007/s11053-007-9047-4
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Behnia, P. Application of Radial Basis Functional Link Networks to Exploration for Proterozoic Mineral Deposits in Central Iran. Nat Resour Res 16, 147–155 (2007). https://doi.org/10.1007/s11053-007-9036-7
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DOI: https://doi.org/10.1007/s11053-007-9036-7