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Apatite major and trace element chemistry is a widely used tracer of mineralization as it sensitively records the characteristics of the magmatic-hydrothermal system at the time of its crystallization. Previous studies have proposed useful indicators and binary discrimination diagrams to distinguish between apatites from mineralized and unmineralized rocks; however, their efficiency has been found to be somewhat limited in other systems and larger-scale data sets. This work applied a machine learning (ML) method to classify the chemical compositions of apatites from both fertile and barren rocks, aiming to help determine the mineralization potential of an unknown system. Approximately 13 328 apatite compositional analyses were compiled and labeled from 241 locations in 27 countries worldwide, and three apatite geochemical data sets were established for XGBoost ML model training. The classification results suggest that the developed models (accuracy: 0.851–0.992; F1 score: 0.839–0.993) are much more accurate and efficient than conventional methods (accuracy: 0.242–0.553). Feature importance analysis of the models demonstrates that Cl, F, S, V, Sr/Y, V/Y, Eu*, (La/Yb)N, and La/Sm are important variables in apatite that discriminate fertile and barren host rocks and indicates that V/Y and Cl/F ratios and the S content, in particular, are crucial parameters to discriminating metal enrichment and mineralization potential. This study suggests that ML is a robust tool for processing high-dimensional geochemical data and presents a novel approach that can be applied to mineral exploration. [ABSTRACT FROM AUTHOR]

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]

Quartz cathodoluminescence (CL) images are commonly combined with trace element concentrations to decipher complex histories of hydrothermal systems. However, the correlations between aluminum content and CL zoning of low-temperature hydrothermal quartz and their genesis remain controversial. In this contribution, a multiparametric study was carried out on CL-aluminum zoning of low-temperature hydrothermal quartz (<350 °C) from the Shihu and Rushan quartz-vein type Au deposits in the North China Craton. The results show that aluminum concentration correlates negatively with CL intensity in quartz from the Shihu Au deposit. CL-dark quartz zoning has significant Al concentrations as well as detectable Al-H bonds. However, in the Rushan Au deposit, the correlation is positive, and aluminum is enriched in the CL-bright quartz zoning. The Al content is positively correlated with K content with r2 = 0.769. Combined with the electron backscatter diffraction (EBSD), X-ray single crystal diffraction (XRD), and transmission electron microscope (TEM) data, we infer that the genesis of CL zoning in the low-temperature hydrothermal quartz is closely related to Al3+-H+ and Al3+-K+ concentrations. The Al3+-K+ may act as the CL-activator, while the Al3+-H+ may act as the CL-dampener. Where Al3+-Si4+ substitution is charge balanced by hydrogen, the intensity of CL response decreases; where Al3+-Si4+ substitution is charge balanced by potassium, the intensity of CL response increases. The correlations between CL intensity and aluminum concentration in the low-temperature hydrothermal quartz reflect pH fluctuations of hydrothermal system. [ABSTRACT FROM AUTHOR]