摘要：Introduction 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 Nykänen, 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. Materials and methods To apply a process-based understanding of porphyry copper-gold deposit system on the mapping of prospectivity, a conceptual model must be first developed (Fatehi and Asadi, 2017). Such a model should depict critical scale-dependent processes involved in the mineral deposit formation, and a mineral system approach can be followed to aid understanding where, when and why mineral deposits form (Parsa et al., 2016). The spatial data sets used to model porphyry Cu-Au prospectivity of the study area include geological, remote sensing, geophysical, and geochemical data. In this study, semi-supervised support vector machine (TSVM) prospectivity technique is utilized to model porphyry Cu-Au target areas. The TSVM is an extension of SVM that uses the unlabeled data to improve the performances of the classifier. The aim of TSVM algorithm is to find the decision hyper-plane subject to maximize the margin distance in labeled and unlabeled data. Result In the present study, TSVM and SVM models were applied to Cu-Au prospectivity modeling in the study area. The models were trained based on the location of known Cu-Au mineralization occurrences and non-deposit location using e1071 package in R open-source statistical software (70% of the labeled data were used in training and 30% in the testing phase of learning in both algorithms). The RBF kernel function were used and the optimal values of the kernel parameters were assessed using a 10-fold cross-validation procedure and the best learning performance was selected by correct classification rate. The output of the models highlighted the target areas for porphyry Cu-Au mineralization in the study area. The receiver operating characteristics (ROC) analysis shows that both models perform well, however, the TSVM model yields the best performance. Discussion To identify exploratory target areas on a regional scale, various supervised and unsupervised approaches have been developed in mineral potential modeling. Supervised methods such as SVM use labeled data to classify exploratory datasets. In this research, a new semi- supervised learning method, TSVM, was applied to model the mineral potential for porphyry Cu-Au mineralization in the Dehsalm exploration zone. The introduced target areas by TSVM method, within the known mineral indices, covered smaller areas than targets identified by SVM model, so planning the detailed exploration phase will be optimal. The result of this research demonstrates the superiority of the semi-supervised learning method in identifying the target areas for planning the exploratory operations. Acknowledgements We would like to thank the Geological Survey of Iran for providing the exploration data used in this research. The financial support of the South Khorasan Industry, Mine Trade Organization is gratefully thanked. References Abedi, M., Norouzi, G.H. and Bahroudi, A., 2012. Support vector machine for multi-classification of mineral prospectivity areas. Computers Geosciences, 46: 272–283. https://doi.org/10.1016/j.cageo.2011.12.014 Beydokhti, R.M., Karimpour, M.H., Mazaheri, S.A., Santos, J.F. and Klötzli, U., 2015. U-Pb zircon geochronology, Sr-Nd geochemistry, petrogenesis and tectonic setting of Mahoor granitoid rocks (Lut Block, Eastern Iran). Journal of Asian Earth Sciences, 111: 192–205. https://doi.org/10.1016/j.jseaes.2015.07.028 Chen, Y. and Wu, W., 2016. A prospecting cost-benefit strategy for mineral potential mapping based on ROC curve analysis. Ore Geology Reviews, 74: 26–38. https://doi.org/10.1016/j.oregeorev.2015.11.011 Fatehi, M. and Asadi, H.H., 2017. 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