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Knowledge Recovery for Continental-Scale Mineral Exploration by Neural Networks

Abstract : This study is concerned with understanding of the formation of ore deposits (precious and base metals) and contributes to the exploration and discovery of new occurrences using artificial neural networks. From the different digital data sets available in BRGM's GIS Andes (a comprehensive metallogenic continental-scale Geographic Information System) 25 attributes are identified as known factors or potential factors controlling the formation of gold deposits in the Andes Cordillera. Various multilayer perceptrons were applied to discriminate possible ore deposits from barren sites. Subsequently, because artificial neural networks can be used to construct a revised model for knowledge extraction, the optimal brain damage algorithm by LeCun was applied to order the 25 attributes by their relevance to the classification. The approach demonstrates how neural networks can be used efficiently in a practical problem of mineral exploration, where general domain knowledge alone is insufficient to satisfactorily model the potential controls on deposit formation using the available information in continent-scale information systems.
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https://hal.inria.fr/inria-00099699
Contributor : Publications Loria <>
Submitted on : Tuesday, September 26, 2006 - 9:40:24 AM
Last modification on : Friday, February 26, 2021 - 3:28:03 PM

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  • HAL Id : inria-00099699, version 1

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Laurent Bougrain, Maria Gonzalez, Vincent Bouchot, Daniel Cassard, Andor L.W. Lips, et al.. Knowledge Recovery for Continental-Scale Mineral Exploration by Neural Networks. Natural Resources Research, Springer Verlag, 2003, 12 (3), pp.173-181. ⟨inria-00099699⟩

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