A comparative study of the XGBoost ensemble learning and multilayer perceptron in mineral prospectivity modeling: a case study of the Torud-Chahshirin belt, NE Iran

Precisely selecting the exploration criteria and building robust machine-learning models are two critical issues for enhancing the efficiency of mineral prospectivity mapping (MPM) for delimiting highly favorable mineralization zones. The efficient exploration features linked to geochemical, geological, and remote sensing criteria were distinguished in the Torud-Chahshirin (TCS) volcano-intrusive belt, NE Iran using success-rate curves. Then, the Extreme Gradient Boosting (XGBoost) as an ensemble learning method was employed on a coherent group of exploratory evidence layers for highlighting the epithermal-Cu prospectivity areas in the TCS belt. In the next step, the artificial neural networks (here, MLP-ANN) as a data-driven machine learning technique was applied to compare the results which was obtained by the XGBoost algorithm. The outcomes of the receiver operating characteristics (ROC) curves illustrate that both predictive models succeeded in delineating target zones. However, regarding the area under the curve (AUC) values, the XGBoost model successfully delineates the exploration target by mostly Cu mineral occurrences rather than the MLP-ANN model.