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An Artificial Neural Network Method for Mineral Prospectivity Mapping: A Comparison with Fuzzy Logic and Bayesian Probability Methods

  • Warick M. Brown
  • David I. Groves
  • Tamás D. Gedeon
Chapter
Part of the Modern Approaches in Geophysics book series (MAGE, volume 21)

Abstract

A multilayer perceptron (MLP) neural network is used to combine multi-source exploration data in a Geographic Information System (GIS) database and produce a mineral prospectivity map for gold deposits in the Tenterfield 1:100,000 sheet area, NSW, Australia. Statistical and probability measures of map quality indicate that the neural network method performs better than the empirical weights-of-evidence (Bayesian probability) and conceptual fuzzy logic methods in the generation of the mineral prospectivity map. The neural network has several important advantages over existing methods, including the ability to respond to critical combinations of parameters, rather than automatically increasing the prospectivity estimate in response to every favourable parameter.

Keywords

Geographic Infonnation System Fuzzy Logic Gold Deposit Fuzzy Membership Thematic Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Warick M. Brown
    • 1
  • David I. Groves
    • 1
  • Tamás D. Gedeon
    • 2
  1. 1.Centre for Global Metallogeny, Department of Geology and GeophysicsUniversity of Western AustraliaPerthAustralia
  2. 2.School of Information TechnologyMurdoch UniversityPerthAustralia

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