LAUSR

Abstract Predictive modelling of mineral prospectivity using GIS is a valid and progressively more accepted tool for delineating reproducible mineral exploration targets. In this study, machine learning methods, including support vector machine (SVM), artificial neural networks (ANN) and random forest (RF), were employed to conduct GIS-based mineral prospectivity mapping of the Tongling ore district, eastern China. The mineral systems approach was used to translate our understanding of the skarn Cu mineral system into mappable exploration criteria, resulting in 12 predictor maps that represent source, transport, physical trap and chemical deposition processes critical for ore formation. Predictive SVM, ANN and RF models were trained by way of predictor maps, and corroborated using a 10-fold cross-validation. The overall performance of the resulting predictive models was assessed in both training and test datasets using a confusion matrix, set of statistical measurements, receiver operating characteristic curve, and success-rate curve. The assessment results indicate that the three machine learning models presented in this study achieved satisfactory performance levels characterized by high predictive accuracy. In addition, all models exhibited well interpretability that provided consistent ranking information about the relative importance of the evidential features contributing to the final predictions. In comparison, the RF model outperformed the SVM and ANN models, having achieved greater consistency with respect to variations in the model parameters and better predictive accuracy. Importantly, the RF model exhibited the highest predictive efficiency capturing most of the known deposits within the smallest prospective tracts. The above results suggest that the RF model is the most appropriate model for Cu potential mapping in the Tongling ore district, and, therefore, was used to generate a prospectivity map containing very-high, high, moderate, and low potential areas in support of follow-up exploration. The prospective areas delineated in this map occupy 13.97% of the study area and capture 80.95% of the known deposits. The fact that two newly discovered deposits occur within the prospective areas predicted by the prospectivity model indicates that the model is robust and effective regarding exploration target generation.