{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "DAI Shifeng"
    },
    {
      "name": "XU Na"
    },
    {
      "name": "ZHU Wei"
    },
    {
      "name": "LI Pengfei"
    },
    {
      "name": "QI Anliangyu"
    },
    {
      "name": "LIU Yu"
    },
    {
      "name": "YAN Ruiwen"
    },
    {
      "name": "ZHAO Zhengfu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c0xtgc6eamhjw.bib",
  "cited_by": 0,
  "doi": "10.12090/j.issn.1006-6616.2026060",
  "fulltext_url": "https://journal.geomech.ac.cn//article/doi/10.12090/j.issn.1006-6616.2026060",
  "id": "c0xtgc6eamhjw",
  "issns": [
    "1006-6616"
  ],
  "landing_url": "https://doi.org/10.12090/j.issn.1006-6616.2026060",
  "language": "zh",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QE1-996.5"
    ],
    [
      "openalex",
      "1906|Coal and Its By-products"
    ]
  ],
  "openalex": "W7224098807",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "Institute of Geomechanics, Chinese Academy of Geological Sciences",
  "ris": "https://research.draconitic.com/paper/c0xtgc6eamhjw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:47:27Z",
      "native_id": "3f651af72b1546d1bc481dd551cd2d03",
      "source": "doaj",
      "url": "https://doi.org/10.12090/j.issn.1006-6616.2026060"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1906|Coal and Its By-products",
      "subfield": "geology-geochemistry"
    }
  ],
  "title": "Artificial intelligence-empowered coal geology: advances, challenges, and prospects",
  "title_text": "Artificial intelligence-empowered coal geology: advances, challenges, and prospects",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:27Z",
  "url": "https://research.draconitic.com/paper/c0xtgc6eamhjw",
  "venue": "Dizhi lixue xuebao",
  "versions": []
}