| Abstrakt: |
Mineralization temperature determination is fundamental to economic geology research, yet quantifying it across mineralization remains a challenge. Sphalerite is ubiquitous in various types of mineral deposits and particularly abundant in Pb–Zn deposits, and its trace element composition is temperature-dependent, making it an ideal candidate for geothermometry. Here, we first compiled a global sphalerite trace element composition dataset (n = 1416, T = 75–430 °C), encompassing different Pb–Zn deposit types (Mississippi Valley-type, epithermal, sedimentary-exhalative, skarn-type, and volcanic massive sulfide deposits). After data processing following statistical norms, the different machine learning algorithms (random forest (RF), gradient boosted decision trees, artificial neural networks, least absolute shrinkage and selection operator, support vector machine, k-nearest neighbors, and linear regression) were employed to train different models to explore the potential link between the sphalerite-forming temperature and trace element geochemistry. Each of the model's performance was evaluated using the leave-one-out cross-validation approach, which revealed the RF (R2 = 0.88, RMSE = 26 °C) as the best-performing algorithm. Meanwhile, five-fold cross-validation results indicated that the RF model (R2 = 0.87, RMSE = 25 °C) outperformed the GGIMFis thermometer (R2 = 0.53, RMSE = 50 °C). Meanwhile, the feature importance analysis revealed that Ge and Mn displayed significant impacts on temperature prediction as the high temperature generally favors Mn, but not Ge, incorporation into the sphalerite structure. Finally, a model was trained with the entire dataset, generating a reliable sphalerite thermometer (SPRFT software, freely provided here) suitable for low to moderate temperature (75–430 °C) hydrothermal environments. This SPRFT thermometer was applied to evaluate the temperature of Pb–Zn mineralization in the Sichuan–Yunnan–Guizhou Pb–Zn metallogenic belt (SW China) and it provides an innovative perspective into the ore-fluid evolution. This study demonstrated a robust approach for calculating mineralization temperatures using machine learning. This novel methodology opens new avenues for investigating and recalculating more mineral geothermometers. [ABSTRACT FROM AUTHOR] |
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