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| Citation: | Mao Xiancheng, Duan Xinming, Deng Hao, Chen Jin, Liu Zhankun, Huang Jixian, 2026. Intelligent 3D Prediction of Deep Mineral Resources: Theory, Methods, and Challenges. Earth Science, 51(3): 793-815. doi: 10.3799/dqkx.2025.227
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Mineral resources are vital for national economic security and industrial development. As shallow resources become increasingly depleted, the exploration of alternative resources in the deeper parts of mines has become an inevitable option to ensure resource security. However, deep mineral prospectivity mapping faces significant challenges, including great depths, limited direct observations, and weak indirect information. There is an urgent need to overcome key technical challenges, including unclear deep ore deposit structures, obscured deep ore-controlling patterns, and significant difficulties in spatial positioning of deep ore bodies, whereas it is very difficult for traditional quantitative prediction methods for mineral resources to meet the demand for precise 3D spatial positioning of deep resources. To address these issues, this paper proposes novel theories and methods of 3D intelligent prediction of deep mineral deposits. Guided by the metallogenic system theories and data science, these theories and methods have preliminarily broken through two key scientific issues: "geological-geophysical-geochemical constraints on the 3D reconstruction of deep ore deposit structures" and "the controlling mechanism of deep 3D ore deposit structures on the spatial positioning of mineralization". It has established a methodological framework of "geological analysis - refined modeling-3D analysis-intelligent prediction", and innovatively developed a theoretical, methodological, and technical system centered on the 3D refined reconstruction of deep deposit structures, 3D geometric-material analysis of ore-forming space, and intelligent 3D positioning prediction of deep ore bodies. The core technologies include: (1) refined 3D reconstruction of deep deposit structures based on multi-source heterogeneous data assimilation and Bayesian inference; (2) intelligent extraction of 3D spatial geometric and material mineralization information using coupled simulation of multi-level structural styles and metallogenic processes; (3) intelligent 3D positioning prediction of deep ore bodies applying artificial intelligence techniques such as deep neural networks, domain adaptation, and multi-modal learning. The automation of refined deep structure reconstruction, the quantification of deep ore-controlling patterns representation, and the intellectualization of orebody positioning prediction have been realized, and significant breakthroughs have been achieved in deep ore prospecting in major mineral concentration areas in China, such as the Jiaodong Peninsula and the Jinchuan. Finally, this paper discusses the future challenges and development directions of 3D intelligent prediction of deep mineral resources from the perspectives of multi-source data assimilation for refined 3D modeling of deep structures, characterization of mineralization information based on the coupling of spatial structure and metallogenic materials, and large language model-driven 3D positioning prediction of deep ore bodies, aiming to further promote the development of in-depth intellectualization of deep mineral prospectivity mapping.
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