GIS-based mineral prospectivity mapping using machine learning methods: A case study from Zhuonuo ore district, Tibet

Abstract

The Zhunuo ore concentration area (ZOCA) is the most potential prospective area of Cu-Au (Mo) in the west of the southern subterrane, Tibet. Single traditional prospective methods (e.g., stream sedimentary geochemistry) often produced larger area and false abnormal information in the Gangdese orogenic belt because of the high altitude and the intense weather and erosion, which can not meet the urgent demand of the current situation for Cu resources. In this study, we combinedamineral system approachwith GIS-basedmachine learningapproachsto obtain geologically meaningful mineral prospective maps. The detail steps include: (i) establishing the mineral system conception model of porphyry copper deposits (PCDs); (ii) transforming the targeted porphyry metallogenic system components into spatial proxies associated with the crucial ore-forming processes; (iii) extracting the spatial proxies: proximity to intrusive rocks (source), NE orientation faults (transport and/or physical trap), Fe-oxide and propylitization hydrothermal alterations zone (hydrothermal fluids) and the metallogenic strength diagram of Cu-Mo-W-Bi-Au-Ag-Pb-Zn (deposition); (iv) Radial Basis Functions Link Networks (RBFLN), Random forests (RF) Supervised and Fuzzy Clustering (FC) unsupervised machine learning methods were applied to capture the complex and crucial mineralization information between known deposit types and evidence layers; (vi) model estimation and delineating prospective potential targets: Receiver operating characteristic curve (ROC), predictive-area (P-A) plotting and normalised density (Nd) were used to evaluate the predictive models results. The results indicate that the RBFLN model, RF model, and FC model show high predictive accuracy. The AUC values under the ROC area of the RBFLN model, RF model, and FC model are 0.99, 0.96, and 0.94, respectively. The RBFLN model outperforms the RF model and FC model, the predictive-area plotting of RBFLN occupies 12% of the study area containing 88% of the known deposits. The predictive-area plotting of the RF model and FC model showed that 14% and 21% of the study area contained 86% and 79% of the known deposits, respectively. The normalized density (Nd) of a layer is defined as the ratio of the prediction success rate (Pr) of the P-A plotting to the corresponding area (Oa). The normalized density of the RBFLN model, the RF model, and the FC model are 7.33, 6.14, and 3.76, respectively, which revealed that the results of the three predictive models all have positive indications. These studies show that RBFLN supervised machine learning method is a more robustness and generalization capability. The predictive results also provide prospective potential targets (e.g., northern Cimabanshuo, northwest Wubaduolai, and southwestern and western Zhunuo PCD) for further exploration, and this method can be also applicable to other mineral systems and districts.