Development Status and Trends of Coal and Gas Outburst Prediction and Early Warning Technologies in China Over the Past Decade

Authors

  • Yi Zhang

DOI:

https://doi.org/10.54097/th7f9t58

Keywords:

Coal and Gas Outburst; Prediction and Early Warning Technology; Development Over the Past Decade; Intelligence; Multi-Source Information Fusion

Abstract

Coal and gas outburst is a major disaster in coal mining, posing a severe threat to the life safety of miners and coal mine production. Over the past decade, China has made remarkable progress in the field of coal and gas outburst prediction and early warning technologies, evolving from static index methods to multi-source information fusion-based early warning and upgrading from traditional monitoring approaches to the application of intelligent technologies. This paper elaborates on the development trajectory of coal and gas outburst prediction and early warning technologies in China over the past decade, encompassing advancements in foundational theoretical research, prediction method models, monitoring equipment development, and early warning system construction. It analyzes the current challenges faced by these technologies and provides an outlook on future development trends, aiming to offer technical references for safe coal mine production and drive the advancement of coal and gas outburst prediction and early warning technologies to a higher level.

References

[1]Wang Luou, Zhang Ping. Statistical and regularity analysis of coal and gas outburst accidents in China from 2020 to 2024[J]. Mining Technology, 2025, 25(03): 259-262.

[2]Cao Jie. Statistical patterns and dynamic effect characteristics of coal and gas outburst accidents in China over the past decade[J]. Mining Safety & Environmental Protection, 2024, 51(03): 36-42+49.

[3]Liu Yi, Lu Shouqing, Zhao Kang, et al. Literature review and research progress on coal and gas outburst prediction indicators based on CiteSpace[J]. Safety in Coal Mines, 2025, 56(05): 30-39.

[4]Qi Liming, Lu Yunting, Guan Lianhe, et al. Exploration and application of methods for determining sensitive indicators for coal and gas outburst prediction[J]. Mining Safety & Environmental Protection, 2021, 48(03): 85-89.

[5]Wang Chaoqun. Research on coal and gas outburst risk prediction technology based on the sensitive indicator method[J]. Coal Technology, 2023, 42(04): 161-163.

[6]Chen Cheng, Tian Shixiang, Zhao Jiajia, et al. Outburst risk prediction model based on gas emission patterns from boreholes[J]. China Sciencepaper, 2024, 19(01): 43-49.

[7]Liang Yunpei, Zheng Menghao, Li Quangui, et al. Research status of coal and gas outburst prediction and early warning in China[J]. Journal of China Coal Society, 2023, 48(08): 2976-2994.

[8]Li Jiangtao, Wang Fei. A combined model for predicting coal and gas outburst volume and risk level using neural networks and machine learning[J]. China Mining Magazine, 2024, 33(S2): 176-184.

[9]Xu Yaosong, Cheng Yewei. Coal and gas outburst risk prediction based on SKPCA and NEAT algorithms[J]. Journal of Safety and Environment, 2021, 21(04): 1427-1433.

[10]Zheng Xiaoliang, Lai Wenhao, Xue Sheng. Application of MI and SVM algorithms in coal and gas outburst prediction[J]. China Safety Science Journal, 2021, 31(01): 75-80.

[11]Gao Zhenbiao. Research on multi-parameter fusion prediction and real-time early warning of coal mine gas[D]. Liaoning Technical University, 2019.

[12]Ning Xiaoliang. A dynamic early warning model for coal and gas outbursts based on multi-source information fusion[J]. Mining Safety & Environmental Protection, 2020, 47(03): 1-5+16.

[13]Li Bing. Research on coal and gas outburst early warning methods based on deep learning and multi-source information fusion[D]. China University of Mining and Technology, 2021.

[14]Chen Zuguo, Zhang Ningbo, Gao Yuanyuan, et al. Research on coal and gas outburst early warning model based on multi-sensor information fusion[J]. China Coal, 2024, 50(06): 67-73.

Downloads

Published

11-12-2025

Issue

Section

Articles