Porphyry Cu-Au prospectivity modelling using semi-supervised learning algorithm in Dehsalm district, eastern Iran

The identification of potentially mineralized areas has progressed with the use and interpretation of all available exploratory data in the form of mineral potential modeling (MPM) (Yousefi and Nyknen, 2017). Recently, machine learning methods have been a popular research topic in the field of MPM ((Chen and Wu, 2016). Machine learning algorithms that have been used in MPM generally fall into the categories of being supervised or unsupervised. Supervised models, use the location of the known mineral occurrences as training sites (or labeled data). Therefore, these models suffer stochastic bias and error (Zuo and Carranza, 2011). Unsupervised models classify mineral prospectivity of every location based solely on feature statistics of individual evidential data layers ((Abedi et al., 2012). The semi-supervised learning models are a hybrid of supervised and unsupervised learning models that use both labeled and unlabeled data to extract the hidden structure of the data, as well as the relation between the input exploration layers and the output labeled data (Fatehi and Asadi, 2017). The Dehsalm study area forms a part of the Lut metallogenic block of eastern Iran, which is characterized by the subduction zone setting and extensive magmatism (Beydokhti et al., 2015). The objective of this research is to present a prospectivity model to delineate exploration target areas for porphyry Cu-Au mineralization in the study area. For generating a prospectivity model, we used TSVM algorithm, a semi-supervised learning integration technique, to identify the anomalous areas related to the porphyry Cu-Au mineralization. The input layers are selected based on a conceptual model for porphyry Cu-Au mineral system. The performance of the mineral prospectivity maps (MPMs) is evaluated using the various techniques, including the receiver operating characteristic (ROC) curve, an area under curve (AUC) metric.