Geochemical anomaly detection is an important issue in mineral exploration. The availability of a training dataset consisting of labeled geochemical samples of background and anomaly classes enables us to define a supervised pattern recognition framework for geochemical anomaly detection. Therefore, various classification and feature selection algorithms can be utilized to build a predictive model and classify the unseen geochemical samples into the pre-defined anomaly and background classes. In this study, some of the state-of-art feature selection and classification algorithms were utilized for supervised anomaly detection in the Kuh Panj porphyry-Cu district. Filter, wrapper and embedded mode feature selection algorithms were used to remove redundant and irrelevant elements from the classification procedure. Subsequently, AdaBoost (ADB), support vector machine (SVM) and Random Forest (RF …

Journal of Geochemical Exploration