A New Discovery of Ag-Pb-Zn Mineralization via Modern Portable Analytical Technology and Stream Sediment Data Processing Methods in Dajiacuo Area, Western Tibet(China)

Tibet, which is rich in mineral resources, is a treasure trove for geological explorers. However, prospecting work has been slow, especially in the western part, due to the precipitous terrain, changeable climate and low access. Hence, modern advanced field analytical technology and effective data processing methods play significant roles in rapid and efficient exploration in Tibet. In this paper, spectrum-area fractal modeling and portable X-ray fluorescence analysis(pXRFA) were used to identify and verify geochemical anomalies associated with Ag-Pb-Zn mineralization based on a stream-sediment dataset of 39 elements in the Dajiacuo-Xurucuo region of western Tibet. First, staged factor analysis(SFA) was used to obtain the Ag-Pb-Zn-Cd geochemical assemblage. Second, the first-factor pattern obtained using SFA was dissociated by a spectrum-area(S-A) fractal model and a digital elevation model(DEM)-based geochemical model(DGM) was constructed. Finally, the sections of Ag, Cd, Pb, and Zn were obtained using pXRFA. The results show that Ag-Pb-Zn-Cd enrichment zones were mostly located around the contact belt of volcanic rocks and intrusions, or along SE-NW trending faults. Considering the variable terrain and catchment basin, the extension of long axes of Ag-Pb-Zn-Cd anomalies into higher elevation areas that are favorable for Ag-Pb-Zn mineralization should be investigated. Anomaly maps created with the aid of a DGM show promising potential for mineralization in the Dajiacuo-Xurucuo region, and abundant Ag-Pb-Zn mineralization was identified with the assistance of pXRFA in the source areas for the geochemical anomalies in the Dajiacuo. We conclude that SFA and the S-A fractal model constitute a valid tool to identify or verify geochemical anomalies in areas of low-density stream-sediment sampling. The pXRFA can accurately determine the source of geochemical anomalies and improve anomaly verification efficiency.