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Microseismic Data-Driven Short-Term Rockburst Evaluation in Underground Engineering with Strategic Data Augmentation and Extremely Randomized Forest

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  • Shouye Cheng

    (Research Institute of Mine Construction, Tiandi Science and Technology Company Limited, Beijing 100013, China
    State Key Laboratory of Intelligent Coal Mining and Strata Control, Beijing 100013, China)

  • Xin Yin

    (School of Civil Engineering, Wuhan University, Wuhan 430072, China)

  • Feng Gao

    (Research Institute of Mine Construction, Tiandi Science and Technology Company Limited, Beijing 100013, China
    State Key Laboratory of Intelligent Coal Mining and Strata Control, Beijing 100013, China)

  • Yucong Pan

    (School of Civil Engineering, Wuhan University, Wuhan 430072, China)

Abstract

Rockburst is a common dynamic geological disaster in underground mining and tunneling engineering, characterized by randomness, abruptness, and impact. Short-term evaluation of rockburst potential plays an outsize role in ensuring the safety of workers, equipment, and projects. As is well known, microseismic monitoring serves as a reliable short-term early-warning technique for rockburst. However, the large amount of microseismic data brings many challenges to traditional manual analysis, such as the timeliness of data processing and the accuracy of rockburst prediction. To this end, this study integrates artificial intelligence with microseismic monitoring. On the basis of a comprehensive consideration of class imbalance and multicollinearity, an innovative modeling framework that combines local outlier factor-guided synthetic minority oversampling and an extremely randomized forest with C5.0 decision trees is proposed for the short-term evaluation of rockburst potential. To determine the optimal hyperparameters, the whale optimization algorithm is embedded. To prove the efficacy of the model, a total of 93 rockburst cases are collected from various engineering projects. The results show that the proposed approach achieves an accuracy of 90.91% and a macro F 1 -score of 0.9141. Additionally, the local F 1 -scores on low-intensity and high-intensity rockburst are 0.9600 and 0.9474, respectively. Finally, the advantages of the proposed approach are further validated through an extended comparative analysis. The insights derived from this research provide a reference for microseismic data-based short-term rockburst prediction when faced with class imbalance and multicollinearity.

Suggested Citation

  • Shouye Cheng & Xin Yin & Feng Gao & Yucong Pan, 2024. "Microseismic Data-Driven Short-Term Rockburst Evaluation in Underground Engineering with Strategic Data Augmentation and Extremely Randomized Forest," Mathematics, MDPI, vol. 12(22), pages 1-19, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3502-:d:1517466
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    References listed on IDEAS

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    1. Weizhang Liang & Asli Sari & Guoyan Zhao & Stephen D. McKinnon & Hao Wu, 2020. "Short-term rockburst risk prediction using ensemble learning methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1923-1946, November.
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