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| Citation: | Sun Huiling, Tang Rui, Li Yang, Zhao Jing, Zhang Tong, Xiao Keyan, Chen Jiangjun, Zhao Jing, Li Yaoyong, Yan Ruihua, Tong Hui, An Yanli, Bai Libing, 2026. Three-Dimensional Mineral Prospectivity Prediction Based on Mineralization Process Numerical Simulation and Machine Learning: A Case Study of the Maodeng Deposit in Inner Mongolia. Earth Science, 51(3): 921-939. doi: 10.3799/dqkx.2026.012
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To address the challenges of deep mineral body prediction, this study proposes a three-dimensional mineral prospectivity prediction method that integrates mineralization process numerical simulation and machine learning, using the Maodeng copper-tin deposit in Inner Mongolia as a case study. The Flac3D is used for numerical simulation of the mineralization process to obtain key physical field parameters that control mineralization, such as stress, temperature, and fluid pressure. These physical results, combined with geological data, are then used in the XGBoost machine learning model for three-dimensional quantitative mineral prospectivity prediction. It is demonstrated that the method successfully simulated the stress field, temperature field, and fluid migration process of the mining area. The AUC value of the XGBoost model reached 99.26%, showing excellent predictive ability. SHAP analysis reveals that shear stress, pore pressure, and temperature are the main factors affecting the distribution of mineral bodies. The predicted results highly correlate with the known mineral bodies, providing a reliable basis for mineral body prediction and ultimately identifying two mineral prospecting target areas. The study demonstrates that the combination of mineralization process numerical simulation and machine learning prediction methods can effectively improve the accuracy of deep mineral resource predictions, offering new technical insights into mineral resources assessment in similar regions.
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