Artificial Neural Networks as a Tool for Mineral Potential Mapping with GIS

Organisation: Vale – University of Granada
Start and Estimated Duration: 01 – May – 2018, 24 Months

Summary: A back-propagation Artificial Neural Network (ANN) model is proposed to discriminate zones of high mineral potential in the Sudbury nickel field, Ontario, Canada, using remote sensing and mineral exploration data stored in a GIS database. A neural network model with four hidden layers was made using the k-fold cross-validation method. The trained network estimated a nickel potential map efficiently, indicating that both previously known and unknown potentially mineralised areas can be detected. These initial results suggest that ANN can be an effective tool for mineral exploration spatial data modelling.