Projection Pursuit Random Forest for Mineral Prospectivity Mapping

Mineral prospectivity mapping (MPM) is a fundamental task in mineral exploration. In recent years, the random forest (RF), which is recognized as a significant model for ensemble machine learning algorithms, has been widely used in MPM due to its advantages of high performance and robustness. Nevertheless, the RF method does not fully consider the correlation between variables. In this regard, a hybrid model, namely, the projection pursuit RF (PPRF), has been proposed. In this study, for validation and comparison purposes, the PPRF and ordinary RF methods were employed for the MPM of porphyry Cu–Mo deposits in the Eastern Tianshan orogenic belt, northwestern China, under the same conditions. In the case study, the performance of these two methods was critically investigated. The overall prediction accuracies of the PPRF and RF methods were evaluated by the area under the receiver operating characteristic curve and prediction–area plot approaches. To analyze the sensitivity of the PPRF and RF methods to unbalanced data caused by the limited number of known deposits in the study area, the synthetic minority oversampling technique was used to generate a balanced dataset. The results demonstrated that the PPRF method not only outperforms the ordinary RF method in the overall accuracy of MPM but is also less sensitive to unbalanced data than the RF method. Therefore, the PPRF method provides a novel and useful data-driven method with excellent performance for MPM. It utilizes the combination of predictor variables in the projection feature space rather than the variables themselves to identify the inherent characteristics of a given dataset and may thus discover deep hidden features that the ordinary RF method cannot find.