Located at the intersection of the Tethys and Pacific Rim metallogenic belts, the Laojunshan polymetallic metallogenic province in SE Yunnan Province hosts many large-scale W–Sn and Sn–Zn polymetallic deposits. The newly discovered Caiyuanzi medium-sized Pb–Zn deposit is located in the northern part of this province and has eight sulfide ore bodies. All the ore bodies occur in the siliceous rocks of the Lower Devonian Pojiao Formation (D₁p). The ore bodies are conformable with stratigraphy and controlled by a lithologic horizon. The sulfide ores have banded or laminated structures. The ore minerals are mainly pyrite, chalcopyrite, sphalerite, and galena. In this study, in situ sulfur and lead isotopes were used to constrain the origin of the Caiyuanzi Pb–Zn deposit. The results show that the in situ δ³⁴S values of pyrite, chalcopyrite, and sphalerite range from 0.1‰ to 6.0‰, with an average of 4.7‰. This δ³⁴S signature reflects the mixing between magmatic-derived and reduced seawater sulfate sulfur. The in situ Pb isotopes characteristics of pyrite, galena, and sphalerite suggest that the sulfur and lead of ore minerals come from the upper crust. Integrating the data obtained from the studies including regional geology, ore geology, and S–Pb isotope geochemistry, we proposed that the Caiyuanzi Pb–Zn deposit is a hydrothermal deposit formed by sedimentary exhalative and magmatic hydrothermal superimposition.

With the rapid development of the global new-energy industry, lithium, as a key strategic mineral resource, has faced increasingly prominent supply–demand contradictions. The Anning area in Central Yunnan serves as an important clay-type lithium resource base in China, and its mineralization prediction research holds significant theoretical value and practical implications. This study focuses on the lithium-rich claystone of the Daoshitou Formation in the Anning area of Central Yunnan. Based on an in-depth analysis of typical clay-type lithium mineralization characteristics, this research analyzes regional lithium metallogenic conditions, identifies key ore-controlling factors and prospecting indicators, comprehensively collects geological, geophysical, geochemical, and remote sensing data, and completes digital processing. By integrating multi-source geoscience data, a multi-source geoscience information spatial database was constructed. Using the Random Forest algorithm as the core method and comparing with machine learning models such as XGBoost and Support Vector Machine (SVM), a mineralization prediction model was established to achieve quantitative prediction of clay-type lithium mineralization probability. The specific research findings are as follows: (1) The metallogenic model and prospecting indicators of clay-type lithium deposits were clarified. The Anning area of Central Yunnan mainly develops sedimentary clay-type lithium deposits, and mineralization is jointly controlled by lithium-rich source material supply, chemical weathering under warm and humid paleoclimate conditions, and weak reductive sedimentary environments of coastal-swamp facies; (2) a multi-source geoscience information database was constructed, extracting 16 predictive variables and establishing a metallogenic prediction sample dataset; (3) the Random Forest model performed optimally with an AUC value of 0.84, accuracy of 88.3%, precision of 89.4%, recall of 87.2%, and F1 score of 0.88; (4) three metallogenic prospect areas were delineated. The results indicate that under the study area conditions and existing data, the Random Forest model demonstrates good applicability and stability. The research results provide important target areas for lithium mineral exploration and deployment in the Anning area of Central Yunnan, and offer methodological references for similar deposit prediction research.