Maximum Entropy and Random Forest Modeling of Mineral Potential: Analysis of Gold Prospectivity in the Hezuo–Meiwu District, West Qinling Orogen, China

Abstract

This study tested and compared the mineral potential mapping capabilities of the random forest (RF) and maximum entropy (MaxEnt) algorithms using gold deposit occurrences within the Hezuo–Meiwu district, West Qinling Orogen, China. Eighteen orogenic gold deposits in this district and associated regional exploration datasets were used to construct data-driven predictive models to identify locations prospective for gold mineralization. The 18 orogenic gold deposits used in the modeling can be divided into magmatic-hydrothermal gold deposits and mesothermal gold deposits in terms of metallogenic characteristics and nine evidential maps associated with Au deposit occurrences (i.e., distance to intrusions and faults; Au, As, Ag, Cu, and Sb singularity indices; and principal component scores (PC1 and PC2) based on isometric logratio-transformed geochemical data were selected as inputs to the models). The PC1 represents a primary geochemical signature of tectonic process or their products (i.e., fault system), whereas PC2 represents a secondary geochemical signature. Both RF and MaxEnt models were then used to quantitatively rank the importance and identify the sensitivity of the evidential maps based on their spatial relationships to the known gold deposits in the study area. The two groups of populations in the response curves and marginal effect curves indicate that the mineral potential mapping should be performed by zones in consideration of different metallogenic characteristics of gold deposits. The accuracy of the resulting models was then assessed, and the results of the mineral potential mapping were examined using receiver operating characteristic (ROC) analysis, capture-efficiency curve, and success rate curve. Both mineral potential mapping by zones with RF and MaxEnt models have higher area under the ROC curve (AUC) values than the models performed in the study area and delineate 19% of the study area containing > 88% of the known deposit occurrences. Finally, according to the concentration–area (C-A) thresholds for prospectivity maps, two ternary prospectivity maps were generated for further mineral exploration. The results indicate that the RF and MaxEnt algorithms can be used effectively for mineral potential mapping and represent machine learning algorithms that can be used in areas with a few known mineral occurrences.