•  
  •  
 

Coal Geology & Exploration

Abstract

Background General-purpose large language models (LLMs) have remarkable capabilities in natural language understanding and complex-task reasoning. However, when applied to geological guarantee in coal mining, these models still suffer from several inherent limitations, including insufficient professional geological knowledge, limited insights into industrial terminology, and inadequate integration of engineering logic and rules. Consequently, they face challenges in accurately capturing domain-specific knowledge and reasoning mechanisms required for geological interpretation, early warning of disasters, and decision-making for disaster prevention and control. These issues lead to limited applicability and reliability of general-purpose LLMs. On the other hand, geological guarantee in coal mining involves multi-source heterogeneous data, such as geological reports, borehole data, professional maps, monitoring data, and mining information. These data are characterized by complex types, close knowledge connection, and high demands for operational coordination. Therefore, there is an urgent need to construct vertical large models oriented to coal mining geology. Methods To construct vertical large models tailored to coal mining geology, this study proposes a technical architecture consisting of data driving, knowledge fusion, and empowered application. Accordingly, key technologies oriented to geological guarantee in coal mining are systematically investigated, including corpus construction, preprocessing of heterogeneous documents, structured information extraction, injection of domain-specific knowledge, model fine-tuning, and performance assessment. Given the diverse formats and complex semantic relationships of data on coal mining geology, a comprehensive corpus system is constructed, which incorporates various professional data including specifications and standards, geological reports, and exploration logs. Furthermore, multiple methods including intelligent document parsing, prompt engineering, supervised fine-tuning, expert feedback-based optimization, and algorithm interface calling are employed to enhance the models’ capacity to comprehend geological entities, spatial relationships, disaster mechanisms, and engineering rules. Results and Conclusions Based on the technology roadmap proposed in this study, the Xiaowu geological vertical large model was developed. This model is distinguished by geology-specific semantic understanding, multimodal data analysis, enhanced knowledge retrieval, structured content generation, intelligent algorithm scheduling, and cross-system collaboration service. These features enable this model to transform scattered geological data, professional knowledge, and engineering experience into queryable, interpretable, and reusable intelligence. The Xiaowu model has been successfully deployed in several mines and received multiple honors, including a representative application case of artificial intelligence-empowered new industrialization conferred by the Ministry of Industry and Information Technology. The results of this study promote a shift from experience-dominated approaches to data- and knowledge-driven collaboration in geological understanding and production decision-making in the field of coal mining, thereby providing technical support for safe production, enhancement of intelligent mining efficiency, and precise prevention and control of geological hazards in coal mines.

Keywords

geological vertical large model, corpus construction, model fine-tuning, geological guarantee, geological forecast, multi-system collaboration

DOI

10.12363/issn.1001-1986.25.10.0792

Reference

[1] 董书宁,刘再斌,程建远,等. 煤炭智能开采地质保障技术及展望[J]. 煤田地质与勘探,2021,49(1):21−31 DONG Shuning,LIU Zaibin,CHENG Jianyuan,et al. Technologies and prospect of geological guarantee for intelligent coal mining[J]. Coal Geology & Exploration,2021,49(1):21−31

[2] 程建远,王保利,范涛,等. 煤矿地质透明化典型应用场景及关键技术[J]. 煤炭科学技术,2022,50(7):1−12 CHENG Jianyuan,WANG Baoli,FAN Tao,et al. Typical application scenes and key technologies of coal mine geological transparency[J]. Coal Science and Technology,2022,50(7):1−12

[3] 贾建称,贾茜,桑向阳,等. 我国煤矿地质保障系统建设30年:回顾与展望[J]. 煤田地质与勘探,2023,51(1):86−106 JIA Jiancheng,JIA Qian,SANG Xiangyang,et al. Review and prospect of coal mine geological guarantee system in China during 30 years of construction[J]. Coal Geology & Exploration,2023,51(1):86−106

[4] 王国法,张建中,薛国华,等. 煤矿回采工作面智能地质保障技术进展与思考[J]. 煤田地质与勘探,2023,51(2):12−26 WANG Guofa,ZHANG Jianzhong,XUE Guohua,et al. Progress and reflection of intelligent geological guarantee technology in coal mining face[J]. Coal Geology & Exploration,2023,51(2):12−26

[5] 刘再斌,刘见中,范涛,等. 基于系统工程的透明矿井构建技术与应用实践[J]. 煤炭学报,2025,50(8):4110−4122 LIU Zaibin,LIU Jianzhong,FAN Tao,et al. Construction technology and application practice of transparent mines based on systems engineering[J]. Journal of China Coal Society,2025,50(8):4110−4122

[6] 崔龙飞,王宗水,鲍盈旭,等. 大模型时代自动问答系统及评价体系综述[J]. 计算机工程与应用,2026,62(5):1−17 CUI Longfei,WANG Zongshui,BAO Yingxu,et al. Survey on question answering systems and evaluation in the era of large models[J]. Computer Engineering and Applications,2026,62(5):1−17

[7] WU Yonghui，SCHUSTER M，CHEN Zhifeng，et al. Google’s neural machine translation system：Bridging the gap between human and machine translation[EB/OL]. 2016：arXiv：1609. 08144. https://arxiv.org/abs/1609.08144.

[8] VASWANI A，SHAZEER N，PARMAR N，et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017，30：6000–6010.

[9] DEVLIN J，CHANG Mingwei，LEE K，et al. BERT：Pre–training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies，Volume 1 (Long and Short Papers). Minneapolis：Association for Computational Linguistics，2019：4171–4186.

[10] RADFORD A，NARASIMHAN K，SALIMANS T，et al. Improving language understanding by generative pre–training[EB/OL]. 2018. https://www.academia.edu/129447679/Improving_Language_Understanding_by_Generative_Pre_Training_OpenAI.

[11] RADFORD A,WU J,CHILD R,et al. Language models are unsupervised multitask learners[J]. OpenAI Blog,2019,1(8):9.

[12] BROWN T B，MANN B，RYDER N，et al. Language models are few–shot learners[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. ACM，2020：1877–1901.

[13] OUYANG L，WU J，JIANG Xu，et al. Training language models to follow instructions with human feedback[C]//Proceedings of the 36th International Conference on Neural Information Processing Systems. New Orleans：Neural Information Processing Systems Foundation，2022：27730–27744.

[14] OPENAI，ACHIAM J，ADLER S，et al. GPT–4 technical report[EB/OL]. 2023：arXiv：2303. 08774. https://arxiv.org/abs/2303.08774.

[15] DRIESS D，XIA Fei，SAJJADI M S M，et al. PaLM–E：An embodied multimodal language model[C]//40th International Conference on Machine Learning. PMLR，2023，202：8469–8488.

[16] GREENBLATT R，DENISON C，WRIGHT B，et al. Alignment faking in large language models[EB/OL]. 2024：arXiv：2412. 14093. https://arxiv.org/abs/2412.14093.

[17] 陈浩泷,陈罕之,韩凯峰,等. 垂直领域大模型的定制化:理论基础与关键技术[J]. 数据采集与处理,2024,39(3):524−546 CHEN Haolong,CHEN Hanzhi,HAN Kaifeng,et al. Domain–specific foundation–model customization:Theoretical foundation and key technology[J]. Journal of Data Acquisition & Processing,2024,39(3):524−546

[18] 贾运红,徐慈强,田原,等. 矿山人工智能大模型技术研究现状及展望[J]. 中国矿业,2025,34(10):135−143 JIA Yunhong,XU Ciqiang,TIAN Yuan,et al. Research status and application prospect of mining artificial intelligence large models technology[J]. China Mining Magazine,2025,34(10):135−143

[19] 王海军. 矿山行业大模型建设路径探索与应用展望[J]. 煤炭科学技术,2024,52(11):45−59 WANG Haijun. Construction exploration and application prospect of the large model in mining industry[J]. Coal Science and Technology,2024,52(11):45−59

[20] WU Gang,WANG Haitao,ZHANG Kunyan,et al. GeoProspect:A domain–specific geological large language model with enhanced continual learning[J]. Neurocomputing,2025,650:130801.

[21] 张亚洲,刘祈蒙,戎璐,等. 语音大模型:架构、训练与挑战分析[J]. 电子学报,2025,53(9):3454−3472 ZHANG Yazhou,LIU Qimeng,RONG Lu,et al. Speech large language models:Architecture,training and challenges analysis[J]. Acta Electronica Sinica,2025,53(9):3454−3472

[22] 高彦杰,陈跃国. 大语言模型预训练系统关键技术综述[J]. 软件学报,2026,37(1):200−229 GAO Yanjie,CHEN Yueguo. Survey on key technologies for large language model pre–training systems[J]. Journal of Software,2026,37(1):200−229

[23] PARK J,SEO W,YUN T S. End–to–end data extraction framework from unstructured geotechnical investigation reports via integrated deep learning and text mining techniques[J]. Developments in the Built Environment,2025,23:100733.

[24] 商锦铃,张建勇. 基于ChatGPT和提示工程的查询式摘要数据集AMTQFSum构建研究[J]. 数据分析与知识发现,2024,8(8/9):122−132 SHANG Jinling,ZHANG Jianyong. Constructing query–focused summarization dataset AMTQFSum based on ChatGPT and prompt engineering[J]. Data Analysis and Knowledge Discovery,2024,8(8/9):122−132

[25] LU Wei,LUU R K,BUEHLER M J. Fine–tuning large language models for domain adaptation:Exploration of training strategies,scaling,model merging and synergistic capabilities[J]. npj Computational Materials,2025,11:84.

[26] GAJULAMANDYAM D K，VEERLA S，EMAMI Y，et al. Domain specific finetuning of LLMs using PEFT techniques[C]//2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC). Las Vegas：IEEE，2025：484–490.

[27] 崔亚仲,贺建荣,任艳艳. 神东矿区人工智能安全生产管控平台应用研究[J]. 煤炭科学技术,2025,53(增刊1):275−283 CUI Yazhong,HE Jianrong,REN Yanyan. Research on application of artificial intelligence safety production management and control platform in Shendong mining area[J]. Coal Science and Technology,2025,53(Sup.1):275−283

[28] 刘再斌,董书宁,李鹏,等. 智能开采透明工作面技术架构与展望[J]. 智能矿山,2020,1(1):46−51

[29] 范京道,闫振国,李川,等. 煤矿智能化开采协同控制理论与关键技术研究及应用[J]. 煤炭学报,2026,51(1):813−825 FAN Jingdao,YAN Zhenguo,LI Chuan,et al. Research and application of cooperative control theory and key technologies for intelligent coal mining[J]. Journal of China Coal Society,2026,51(1):813−825

[30] 张浩然,李君,邢立宁,等. 大模型与智能优化算法集成研究综述[J]. 控制与决策,2026,41(4):871−891 ZHANG Haoran,LI Jun,XING Lining,et al. A research review on integration of large models and intelligent optimization algorithms[J]. Control and Decision,2026,41(4):871−891

Download Chinese Version

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.