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The Spatiotemporal Distribution Law of Microseismic Events and Rockburst Characteristics of the Deeply Buried Tunnel Group

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Listed:
  • Heng Zhang

    (Key Laboratory of Transportation Tunnel Engineering, Ministry of Education,Southwest Jiaotong University, Chengdu 610031, China)

  • Liang Chen

    (School of civil engineering, Changsha University of Science and Technology, Changsha 410076, China
    Guangxi Communication Investment Group CO., Ltd., Nanning 530022, China)

  • Shougen Chen

    (Key Laboratory of Transportation Tunnel Engineering, Ministry of Education,Southwest Jiaotong University, Chengdu 610031, China)

  • Jianchun Sun

    (Key Laboratory of Transportation Tunnel Engineering, Ministry of Education,Southwest Jiaotong University, Chengdu 610031, China)

  • Jiasong Yang

    (China Railway No. 2 Engineering Group Co., Ltd., Chengdu 610032, China)

Abstract

Rockburst disaster is one of the prominent problems faced by deep underground engineering. Microseismic (MS) monitoring techniques can be used to warn of rockburst in tunnels to provide scientific basis for rock burst prevention and control measures. Described in this paper, is an MS monitoring system based on MS source location with hierarchical strategy implemented in the tunnel group of the Jinping II hydropower station in Sichuan Province, China. The spatiotemporal distribution of MS events was analyzed in the construction process and the size effect of rockburst risk discussed by statistical analysis and numerical calculation of rockburst in seven tunnels. The results show that the active period of microseisms and the high-incidence period of rockburst are within 1.5 h after the rock disturbance. The MS events within 1D from the tunnel wall are the most intensive and are mainly concentrated near the heading face, the side wall, and the left spandrel. At the construction site, the accuracy rate of rockburst prediction is 61.8%, of which the accuracy rate of the medium and strong rockburst is 80.3%. Based on field statistics of rockburst, the increase of the tunnel excavation section size can reduce the rockburst strength to a certain extent, which is consistent with the numerical simulation results.

Suggested Citation

  • Heng Zhang & Liang Chen & Shougen Chen & Jianchun Sun & Jiasong Yang, 2018. "The Spatiotemporal Distribution Law of Microseismic Events and Rockburst Characteristics of the Deeply Buried Tunnel Group," Energies, MDPI, vol. 11(12), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3257-:d:184877
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    References listed on IDEAS

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    1. Zheng-yi Wang & Lin-ming Dou & Gui-feng Wang, 2018. "Mechanism Analysis of Roadway Rockbursts Induced by Dynamic Mining Loading and Its Application," Energies, MDPI, vol. 11(9), pages 1-24, September.
    2. Abdul Muntaqim Naji & Hafeezur Rehman & Muhammad Zaka Emad & Hankyu Yoo, 2018. "Impact of Shear Zone on Rockburst in the Deep Neelum-Jehlum Hydropower Tunnel: A Numerical Modeling Approach," Energies, MDPI, vol. 11(8), pages 1-16, July.
    3. Yi Xue & Zhengzheng Cao & Feng Du & Lin Zhu, 2018. "The Influence of the Backfilling Roadway Driving Sequence on the Rockburst Risk of a Coal Pillar Based on an Energy Density Criterion," Sustainability, MDPI, vol. 10(8), pages 1-21, July.
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    Cited by:

    1. Hang Zhang & Chunchi Ma & Tianbin Li, 2019. "Quantitative Evaluation of the “Non-Enclosed” Microseismic Array: A Case Study in a Deeply Buried Twin-Tube Tunnel," Energies, MDPI, vol. 12(10), pages 1-17, May.
    2. Xuewei Liu & Quansheng Liu & Bin Liu & Yongshui Kang, 2020. "A Modified Bursting Energy Index for Evaluating Coal Burst Proneness and Its Application in Ordos Coalfield, China," Energies, MDPI, vol. 13(7), pages 1-19, April.
    3. Zhiqiang Zhang & Peng Xu & Heng Zhang & Kangjian Zhang, 2019. "Dynamic Change Characteristics of Groundwater Affected by Super-Long Tunnel Construction in the Western Mountainous Area of China," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
    4. Guangliang Feng & Guoqing Xia & Bingrui Chen & Yaxun Xiao & Ruichen Zhou, 2019. "A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model," Sustainability, MDPI, vol. 11(11), pages 1-17, June.
    5. Hanna Michalak & Paweł Przybysz, 2021. "The Use of 3D Numerical Modeling in Conceptual Design: A Case Study," Energies, MDPI, vol. 14(16), pages 1-21, August.
    6. Lei Li & Yujiang Xie & Jingqiang Tan, 2020. "Application of Waveform Stacking Methods for Seismic Location at Multiple Scales," Energies, MDPI, vol. 13(18), pages 1-15, September.
    7. Zhiqiang Zhang & Ruikai Gong & Heng Zhang & Wanping He, 2020. "The Sustainability Performance of Reinforced Concrete Structures in Tunnel Lining Induced by Long-Term Coastal Environment," Sustainability, MDPI, vol. 12(10), pages 1-23, May.
    8. Guangliang Feng & Manqing Lin & Yang Yu & Yu Fu, 2020. "A Microseismicity-Based Method of Rockburst Intensity Warning in Deep Tunnels in the Initial Period of Microseismic Monitoring," Energies, MDPI, vol. 13(11), pages 1-15, May.
    9. Zhiqiang Zhang & Chun Luo & Heng Zhang & Ruikai Gong, 2020. "Rockburst Identification Method Based on Energy Storage Limit of Surrounding Rock," Energies, MDPI, vol. 13(2), pages 1-24, January.

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