Knowledge recovery for continental-scale mineral exploration by neural networks

• Published in: Natural resources research (New York, N.Y.) N.Y.), 2003-09, Vol.12 (3), p.173-181
• Main Authors: BOUGRAIN, Laurent, GONZALEZ, Maria, BOUCHOT, Vincent, CASSARD, Daniel, LIPS, Andor L. W, ALEXANDRE, Frédéric, STEIN, Gilbert
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Brain damage; Brain injury; Computer Science; Deposits; Digital data; Earth sciences; Earth, ocean, space; Exact sciences and technology; Geochemical exploration, methodology, general; Geographic information systems; Heavy metals; Information systems; Metallic and non-metallic deposits; Mineral deposits; Mineral exploration; Multilayer perceptrons; Neural networks; Other; Remote sensing
• ISSN: 1520-7439; 1573-8981
• DOI: 10.1023/A:1025123920475

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.