Juraj Majzlan
Here we use explainable machine-learning (ML) analysis to determine which variables, so-called features, are most important for the prediction of trace concentrations of Cu, Pb, and Zn in a large data set for igneous rocks. After data filtering and preparation, a gradient-boosted decision tree regression model is trained using eXtreme Gradient Boosting (XGBoost). Feature importance was evaluated quantitatively for each of the trace elements. In addition, we performed SHapley Additive exPlanations (SHAP) analysis to test the robustness of the feature importance results. Simple calculation of historical and known reserves shows that < 0.1% of the weight fractions of Cu, Pb, and Zn are contained in the ore deposits. The rest must be dispersed in rocks. For Pb, the most important features, determined by ML, are K2O, Rb, U, and Th. They are interpreted in the sense that Pb is accumulated in the rest magma during fractionation and, after solidification, stored mostly in K-feldspars and in minerals with primary elevated content of U or Th, such as allanite or zircon. For Zn, the most important features are TiO2, MnO, Ga, and Sc. They are assigned to ilmenite, a lesser extent magnetite, and amphiboles/pyroxenes, as the Zn reservoirs. These interpretations are supported by a compilation of known distribution coefficients.
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title={2026 Majzlan Cu Pb Zn Reservoirs Machine Learning},
author={Juraj Majzlan},
year={2026},
language={en}
}TY - JOUR TI - 2026 Majzlan Cu Pb Zn Reservoirs Machine Learning AU - Juraj Majzlan PY - 2026 LA - en ER -
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