The southwestern South China Block is one of the most important Sn polymetallic ore districts in the world, of which the Dulong Sn-Zn polymetallic deposit, closely related to Late Cretaceous granitic magmatism, contains 0.4 Mt Sn, 5.0 Mt Zn, 0.2 Mt Pb, and 7 Kt In, and is one of the largest Sn-Zn polymetallic deposits in this region. In this paper, on the basis of a 3D model of ore bodies established by the cut-off grade of the main ore-forming elements, the In grades were estimated by the ordinary Kriging method and the In-rich cells were extracted. The 3D models of strata, faults, granites, and granite porphyries in the mining area were established and assigned the attributes to the cells, which built buffer zones representing the influence space of the geological factors. The weight of evidence and artificial neural network methods were used to quantitatively evaluate the contribution of each geological factor to mineralization. The results show that the Neoproterozoic Xinzhai Formation (Pt₃x), fault (F₁), and Silurian granites (S₃L) have considerable control effects on the occurrence of In-rich ore bodies. The metallogenic predictions according to the spatial coupling relationship of each geological factor in 3D space were carried out, and then the 3D-space-prospecting target areas of In-rich ore bodies were delineated. In addition, the early geological maps and data information of the mining area were comprehensively integrated in 3D space. The feasibility of 3D quantitative metallogenic prediction based on the deposit model was explored by comparing the two methods, and then, the 3D-space prospecting target area was delineated. The ROC curve evaluation shows that the results of two methods have indicative value for prospecting. The modeling results may support its use for future deep prospecting and exploitation of the Dulong and other similar deposits.

The Bainiuchang Ag polymetallic deposit is located at the junction between the Cathaysia, Yangtze, China and Indosinian blocks. It has experienced many geological events, and records excellent conditions for multiple mineralization. In this paper, elemental correlation analysis, cluster analysis, factor analysis, a semivariogram of Zn/Pb values, mineralization distribution and trend surface analysis have been carried out based on the prospecting database and ore body model. Our results show that Ag–Pb–Zn were mineralized at moderate temperatures. Tin was mineralized at high temperatures, and Sn and Zn/Pb values are well correlated. The Zn/Pb values can be used for tracing the ore-forming fluid. The semivariogram revealed that the Zn/Pb values are moderately spatially dependent, with good mineralization continuity in the 100° and 10° directions. The spatial pattern of the elemental grade correlates with mineralization enrichment. The trend surface analysis shows that the Ag, Pb, Zn, and Cu mineralization is weak in the south and strong in the north of the deposit, and the Sn grades and Zn/Pb values are high in the south and low in the north. High-temperature Sn, medium-temperature Cu, and medium-temperature Ag–Pb–Zn mineralization have occurred in a south-to-north trend. Therefore, the source of the ore-forming fluid was in the southern part of the mining area. During the migration of the ore-forming fluid from south to north, different minerals were precipitated due to changes in the physicochemical environment. The spatial patterns of mineralization may provide a basis for studying the formation of the ore deposit, and can guide ore exploration and mining in the mine area and similar ore deposits elsewhere.