Application of Radial Basis Functional Link Networks to Exploration for Proterozoic Mineral Deposits in Central Iran |
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Authors: | Pouran Behnia |
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Institution: | (1) Geomatics Department, Geological Survey of Iran, Tehran, Iran |
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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.
An erratum to this article can be found at |
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Keywords: | GIS mineral-potential mapping neural networks |
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