Aplicação de redes neurais no controle de teores de cobre e ouro do depósito de Chapada (GO)

This study deals with application of artificial neural networks (ANNs) on grade control at mine sites inputting both geological and geotechnical variables. Case study is Chapada copper-gold deposit (Goiás, Brazil), located in the neoproterozoic Chapada-Mara Rosa volcano-sedimentary sequence. Ore is closely related to hydrothermal alteration, structurally controlled. The geological and geotechnical database contain 21,212 records on 21 variables taken from 237 diamond drill holes. Input variables include lithology, sulfide percentage, chalcopyrite/pyrite ratio, fracture frequency, RQD, and hydrothermal alterations such as chloritization, sericitization, silicification, epidotization, carbonatization and pyritization. Output variables are gold and copper grades. Neural network model is feedforward multi-layer perceptron (MLP), fully connected with 30 hidden and 2 output neurons. Network was trained with Levenberg-Marquardt backpropagation algorithm associated with bayesian regularization. Success rate on predicting copper grades on simulated mine benches was over 80%.