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The mineral zircon has a robust crystal structure, preserving a wealth of geological information through deep time. Traditionally, trace elements in magmatic and hydrothermal zircon have been employed to distinguish between different primary igneous or metallogenic growth fluids. However, classical approaches based on mineral geochemistry are not only time consuming but often ambiguous due to apparent compositional overlap for different growth environments. Here, we report a compilation of 11 004 zircon trace element measurements from 280 published articles, 7173 from crystals in igneous rocks, and 3831 from ore deposits. Geochemical variables include Hf, Th, U, Y, Ti, Nb, Ta, and the REEs. Igneous rock types include kimberlite, carbonatite, gabbro, basalt, andesite, diorite, granodiorite, dacite, granite, rhyolite, and pegmatite. Ore types include porphyry Cu-Au-Mo, skarntype polymetallic, intrusion-related Au, skarn-type Fe-Cu, and Nb-Ta deposits. We develop Decision Tree, XGBoost, and Random Forest algorithms with this zircon geochemical information to predict lithology or deposit type. The F1-score indicates that the Random Forest algorithm has the best predictive performance for the classification of both lithology and deposit type. The eight most important zircon elements from the igneous rock (Hf, Nb, Ta, Th, U, Eu, Ti, Lu) and ore deposit (Y, Eu, Hf, U, Ce, Ti, Th, Lu) classification models, yielded reliable F1-scores of 0.919 and 0.891, respectively. We present a web page portal (http://60.205.170.161:8001/) for the classifier and employ it to a case study of Archean igneous rocks in Western Australia and ore deposits in Southwest China. The machine learning classifier successfully determines the known primary lithology of the samples, demonstrating significant promise as a classification tool where host rock and ore deposit types are unknown. [ABSTRACT FROM AUTHOR]

The redox condition, a key parameter of mineralization, is one of the most difficult parameters to constrain precisely. Here we employ electron probe and X-ray photoelectron spectroscopy (XPS) to analyse sulphur (S) in apatite grains from five granitoid plutons that are differentially related to gold mineralization in the world-class gold fields of Jiaodong, Xiaoqinling, and Wutai-Hengshan within the North China Craton (NCC) to constrain the redox condition of melt. The results show increasing order of the apatite S content and S6+/ΣS ratios in the Linglong (only spatially close the Jiaodong gold deposits), Sunzhuang (related with the Yixingzhai gold deposits), Huashan (weakly related with the Xiaoqinling gold deposits), Wenyu (closely related with the Xiaoqinling gold deposits) and Guojialing plutons (closely related with the world-class Jiaodong gold deposits), indicating that the oxidation state increased from the Linglong pluton to the Guojialing pluton. The S concentrations of Guojialing, Wenyu, Huashan and Sunzhuang plutons, are estimated using previous methods are, 120–208 ppm, 107–138 ppm, 72–77 ppm and 10–74 ppm, respectively. Combined with the regional differences in gold mineralization in the North China Craton, the gradual increase in mineralization scale is consistent with the trend of increasing oxidation state in the granitoid plutons from Wutai-Hengshan to Xiaoqinling to Jiaodong goldfields, thus also underpinning the contribution of Mesozoic granites to gold mineralization in the North China Craton through providing the thermal energy and medium for the activation and migration of ore-forming fluids. [ABSTRACT FROM AUTHOR]