Estimation of Mineral Resources with Machine Learning Techniques

Abstract

In this study, the application of adaptive fuzzy inference systems (ANFISs) and artificial neural networks (NNs) for grade and reserve estimation of a copper deposit was studied. More specifically, a feedforward NN with backpropagation and two Sugeno- type ANFIS were developed for grade and reserve estimation. Borehole assay data were used for training, validation, and testing of the NN and ANFIS. Grade estimates and tonnage–grade curves were produced and compared to those obtained using a geostatistical approach (Kriging).

Keywords

• mineral resources
• reserve estimation
• artificial neural networks
• adaptive neuro-fuzzy inference systems