Support vector machine for multi-classification of mineral prospectivity areas

In this paper on mineral prospectivity mapping, a supervised classification method called Support Vector Machine (SVM) is used to explore porphyry-Cu deposits. Different data layers of geological, geophysical and geochemical themes are integrated to evaluate the Now Chun porphyry-Cu deposit, located in the Kerman province of Iran, and to prepare a prospectivity map for mineral exploration. The SVM method, a data-driven approach to pattern recognition, had a correct-classification rate of 52.38% for twenty-one boreholes divided into five classes. The results of the study indicated the capability of SVM as a supervised learning algorithm tool for the predictive mapping of mineral prospects. Multi-classification of the prospect for detailed study could increase the resolution of the prospectivity map and decrease the drilling risk.

► A supervised classification method called Support Vector Machine (SVM) is used. ► Different data layers, geological, geophysical and geochemical are integrated to prepare prospectivity map. ► The SVM method to the pattern recognition prioritizes high potential area. ► We increase resolution of prospectivity map into multi-class.