Volume 32 Issue 5
Oct.  2026
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Article Navigation >  Journal of Geomechanics  > 2026  >  32(5): 1049-1061
DAI S F，XU N，ZHU W，et al.，2026. Artificial intelligence-empowered coal geology: advances, challenges, and prospects[J]. Journal of Geomechanics，32（5）：1049−1061 doi: 10.12090/j.issn.1006-6616.2026060
Citation: DAI S F，XU N，ZHU W，et al.，2026. Artificial intelligence-empowered coal geology: advances, challenges, and prospects[J]. Journal of Geomechanics，32（5）：1049−1061 doi: 10.12090/j.issn.1006-6616.2026060
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Artificial intelligence-empowered coal geology: advances, challenges, and prospects

doi: 10.12090/j.issn.1006-6616.2026060

  • College of Geoscience and Survey Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China

Funds:  This research was financially supported by the National Natural Science Foundation of China (Grants Nos. 42372191, 42321002, and 42472230).
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  • Author Bio:

    代世峰，中国科学院院士，中国矿业大学（北京）教授，2025年获第十九次李四光地质科学奖科研奖。主要从事地质学和煤系关键金属矿产的科研和教学工作。作为负责人主持了国家自然科学基金创新研究群体项目、国家973项目、国家自然科学基金重大研究计划、国家重点研发计划项目等重要研究任务。在国内外主流期刊上发表论文200余篇，总被引（他引）3万余次。任JCR一区国际期刊International Journal of Coal Geology主编。入选科睿唯安全球高被引科学家(2019—2025)、爱思唯尔中国高被引学者(2014—2024)。曾获国际有机岩石学会最高奖John Castano奖(2021)、美国地质学会能源地质奖Gilbert H. Cady奖(2023)、以第一完成人2次获得国际有机岩石学会Dal Swaine奖(2017和2024)

  • Received: 2026-05-21
  • Revised: 2026-09-02
  • Accepted: 2026-09-04
    • Available Online: 2026-09-04
  • Published: 2026-10-28
    Abstract
  •   Objective  The rapid development of artificial intelligence (AI) is reshaping the research paradigm of geosciences. Coal geology focuses on coal, coal seams, coal-bearing strata, coal basins, and the mineral resources that coexist or are associated with coal-bearing strata. These research objects exhibit highly complex material compositions, multiscale characteristics, and pronounced spatiotemporal variability. Consequently, major research directions of coal geology, including coal petrology, coal quality evaluation, critical metals in coal-bearing strata, and coalbed methane exploration and development, are particularly well suited to AI-based approaches.   Methods  AI is driving coal geology beyond traditional, experience-based identification and localized statistical analysis toward a new paradigm integrating multi-source data fusion, intelligent prediction, and mechanistic interpretation. It also provides new opportunities for discovering previously unrecognized geological phenomena.  Results  This paper systematically reviews recent advances in AI applications to the intelligent identification of coal macerals, prediction of coal quality, assessment of the occurrence modes and resource potentials of critical metals in coal-bearing strata, and prediction of coalbed methane. AI technologies have significantly improved the efficiency and accuracy of maceral image recognition, coal quality parameter prediction, identification of critical metal occurrence patterns in coal, and comprehensive resource evaluation. However, several challenges remain, including the scarcity of high-quality datasets, limited cross-regional generalization capability, weak model interpretability, insufficient consistency with geological mechanisms, inadequate knowledge constraints, and limited implementation in engineering scenarios.  Conclusions  Future research should strengthen the development of standardized large-scale databases, promote the deep integration of geological knowledge with data-driven models, develop interpretable and transferable AI models, and establish an intelligent research paradigm oriented toward major scientific questions in coal geology and the green and efficient development of critical mineral resources.  Significance  This review highlights the transformative role of AI in advancing coal geology from experience-driven analysis toward knowledge-guided, intelligent research, providing methodological support for major scientific discoveries and the green and efficient development of coal-related resources.

     

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