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Artificial neural networks as a tool for mineral potential mapping with GIS

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Abstract:

A back-propagation artificial neural network (ANN) model is proposed to discriminate zones of high mineral potential in the Rodalquilar gold field, south-east Spain, using remote sensing and mineral exploration data stored in a GIS database. A neural network model with three hidden units was selected by means of the k-fold cross-validation method. The trained network estimated a gold potential map efficiently, indicating that both previously known and unknown potentially mineralized areas can be detected. These initial results suggest that ANN can be an effective tool for mineral exploration spatial data modelling.

Document Type: Research Article

DOI: https://doi.org/10.1080/0143116021000031791

Affiliations: 1: Department of Geology, University of Jaen, 23071, Jaen, Spain 2: RSGIS Lab, Department of Geodynamics, University of Granada/IACT, Avda. Fuentenueva s/n, 18071, Granada, Spain

Publication date: 2003-03-01

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