The continuous growth in global demand for mineral resources and the increasing difficulty of mineral exploration have created bottlenecks for traditional mineral prediction methods in handling complex geological information and large amounts of data. This review aims to explore the latest research progress in machine learning technology in the field of large-scale mineral prediction from 2016 to 2025. By systematically searching the Web of Science core database, we have screened and analyzed 255 high-quality scientific studies. These studies cover key areas such as mineral information extraction, target area selection, mineral regularity modeling, and resource potential evaluation. The applied machine learning technologies include Random Forests, Support Vector Machines, Convolutional Neural Networks, Recurrent Neural Networks, etc., and have been widely used in the exploration and prediction of various mineral deposits such as porphyry copper, sandstone uranium, and tin. The findings indicate a substantial shift within the discipline towards the utilization of deep learning methodologies and the integration of multi-source geological data. There is a notable rise in the deployment of cutting-edge techniques, including automatic feature extraction, transfer learning, and few-shot learning. This review endeavors to synthesize the prevailing state and prospective developmental trajectory of machine learning within the domain of large-scale mineral prediction. It seeks to delineate the field’s progression, spotlight pivotal research dilemmas, and pinpoint innovative breakthroughs.

The Sanbao Mn–Ag (Zn-Pb) deposit located in the Laojunshan ore district is one of the most important deposits that has produced most Ag and Mn metals in southeastern Yunnan Province, China. Few studies are available concerning the distribution and mineralization of Ag, restricting further resource exploration. In this study, detailed mineralogy, chronology, and geochemistry are examined with the aim of revealing Ag occurrence and its associated primary base-metal and supergene mineralization. Results show that manganite and romanèchite are the major Ag-bearing minerals. Cassiterite from the Mn–Ag ores yielded a U–Pb age of 436 ± 17 Ma, consistent with the Caledonian age of the Nanwenhe granitic pluton. Combined with other geochemical proxies (Zn-Pb-Cu-Sn), the Sanbao Mn–Ag deposit may originally be of magmatic hydrothermal origin, rather than sedimentary. The Ag-rich (Zn-Pb (Sn)-bearing) ore-forming fluids generated during the intrusion of the granite flowed through fractures and overprinted the earlier Mn mineralization. Secondary Ag (and possibly other base-metals) enrichment occurred through later supergene weathering and oxidation.

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.