Title: Artiﬁcial Neural Networks as a Tool for Mineral Potential Mapping with GIS
Organization: Vale – University of Granada
Start and Estimated Duration: 01, May 2018 - 24 Months
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 mineralized areas can be detected. These initial results suggest that ANN can be an eﬀective tool for mineral exploration spatial data modeling.