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Rockburst prediction model using machine learning based on microseismic parameters of Qinling water conveyance tunnel

基于秦岭输水隧洞微震参数的机器学习岩爆预测模型

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Abstract

The frequency and intensity of rockburst in underground engineering have increased with excavation depth. In order to predict rockburst intensity grade, this paper introduces six machine learning algorithms to establish six rockburst prediction models. Based on 289-day microseismic monitoring data and rockburst events of Qinling water conveyance tunnel, the rockburst intensity grade prediction dataset is constructed. In the process of model establishment, the impact of data imbalance on model performance is discussed first, and it is concluded that Borderline-SMOTE1 is the most effective method to eliminate data imbalance. Secondly, the analysis of six models’ performance indicators shows that the rockburst prediction model based on the Adaboost algorithm has the best performance, with the highest accuracy, macro-F1, and micro-F1, which are 0.938, 0.937, and 0.938, respectively. Finally, the Borderline-SMOTE1-Adaboost model was applied to the prediction of the rockburst intensity grade of Qinling water conveyance tunnel from June 1, 2020 to June 10, 2020. All ten strong rockbursts are accurately predicted, which verifies the effectiveness of predicting rockburst intensity grade through microseismic parameters. The results show that the Borderline-SMOTE1-Adaboost rockburst prediction model can provide a reference for the early warning of rockburst disasters during the construction of deep-buried tunnels.

摘要

随着地下工程不断向深部发展,岩爆的频次和强度日益增加。为了实现对岩爆烈度等级的预 测,本文引入6 种机器学习算法,建立了6 个岩爆预测模型。以秦岭输水隧洞工程289 天的微震监测数 据与岩爆案例为基础,构建岩爆烈度等级预测数据集。在建模过程中,首先讨论了数据不均衡对模型 性能的影响,得出Borderline-SMOTE1 是消除数据不均衡最有效的方法。其次,对6 个模型性能指标 进行分析,发现Adaboost 算法的岩爆预测模型性能最好,其精度、宏F1值、微F1值均最高,分别为 0.938、0.937 和0.938。最后将Borderline-SMOTE1-Adaboost 岩爆预测模型应用于秦岭输水隧洞工程 2020 年6 月1 日至2020 年6 月10 日的岩爆烈度等级预测,10 次强烈岩爆均被准确预测,验证了通过微 震参数来预测岩爆烈度等级的有效性,表明了Borderline-SMOTE1-Adaboost 岩爆预测模型可为深埋隧 道施工过程中岩爆灾害的预警提供参考。

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Authors and Affiliations

  1. State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China

    Ke Ma  (马克), Qing-qing Shen  (申青青), Xing-ye Sun  (孙兴业), Tian-hui Ma  (马天辉) & Chun-an Tang  (唐春安)

  2. Institute of Rock Instability and Seismicity Research, Dalian University of Technology, Dalian, 116024, China

    Ke Ma  (马克), Qing-qing Shen  (申青青), Xing-ye Sun  (孙兴业), Tian-hui Ma  (马天辉) & Chun-an Tang  (唐春安)

  3. Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China

    Jing Hu  (胡晶)

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Contributions

The frame of research goals was developed by MA Ke, SHEN Qing-qing and SUN Xing-ye. TANG Chun-an and MA Tian-hui provided the funding acquisition and resources. HU Jing was responsible for data curation. SHEN Qing-qing and SUN Xing-ye designed computer programs. The initial draft of the manuscript was written by SHEN Qing-qing and MA Ke. SUN Xing-ye reviewed and modified the manuscript.

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Correspondence to Ke Ma  (马克).

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Conflict of interest

MA Ke, SHEN Qing-qing, SUN Xing-ye, MA Tian-hui, HU Jing, and TANG Chun-an declare that they have no conflict of interest.

Foundation item: Projects(51974055, 42122052) supported by the National Natural Science Foundation of China; Project(2021JLM-11) supported by the Joint Fund of Natural Science Basic Research Program of Shaanxi Province, China; Project (202001AT070150) supported by Yunnan Fundamental Research Projects, China; Project(2020D-5007-0302) supported by the Fund of China Petroleum Technology and Innovation

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Ma, K., Shen, Qq., Sun, Xy. et al. Rockburst prediction model using machine learning based on microseismic parameters of Qinling water conveyance tunnel. J. Cent. South Univ. 30, 289–305 (2023). https://doi.org/10.1007/s11771-023-5233-8

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