Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil

This work presents an application of probabilistic neural networks to map the potential for platinum group elements (PGE) mineralization sites in the northeast portion of the Carajás Mineral Province (CMP), Brazilian Amazon. Geological and geophysical gamma-spectrometric and magnetic data were used to generate evidential maps to derive input feature vectors. Feature vectors representing known mineralized locations were used as training data. The networks were created based on the training dataset and the evidential maps were classified in terms of probabilities using these networks. We have produced mineral potential models that depict classes with high, moderate and low favorability for Au-PGE mineralization sites and a model with high and low favorability classes for Cr-PGE mineralization sites. The cut-off values for each class were selected as the inflexion points of the curves of favorability against cumulative percentage of the study area. These curves were also used to check for the efficiency of the models by plotting the favorability values at the training sites. Leave-one-out tests were applied to validate the models and the overall accuracy is 87.5%. For Au-PGE mineralization sites, the high favorability areas accounts for 0.57% of the study area and are comprised mainly within meta-pelites and meta-siltites. For Cr-PGE mineralization sites, the high favorability areas are much more restrict and accounts for only 0.17% of the study area, being associated chiefly with mafic and ultramafic rocks. These mineral potential maps can be used as reconnaissance guides for future detailed ground surveys of possible new PGE occurrences, which is of critical importance to shorten exploration time and costs in such densely forested Amazonian terrains.