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Microseismic Temporal-Spatial Precursory Characteristics and Early Warning Method of Rockburst in Steeply Inclined and Extremely Thick Coal Seam

Author

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  • Zhenlei Li

    (School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine, University of Science and Technology Beijing, Beijing 100083, China)

  • Shengquan He

    (School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine, University of Science and Technology Beijing, Beijing 100083, China)

  • Dazhao Song

    (School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine, University of Science and Technology Beijing, Beijing 100083, China)

  • Xueqiu He

    (School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine, University of Science and Technology Beijing, Beijing 100083, China
    Zhong-an Academy of Safety Engineering, Beijing 100083, China
    School of Civil, Mining and Environmental Engineering, University of Wollongong, Wollongong, NSW 2522, Australia)

  • Linming Dou

    (State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China)

  • Jianqiang Chen

    (Shenhua Xinjiang Energy Company Limited, Urumqi 830027, China)

  • Xudong Liu

    (Shenhua Xinjiang Energy Company Limited, Urumqi 830027, China)

  • Panfei Feng

    (Shenhua Xinjiang Energy Company Limited, Urumqi 830027, China)

Abstract

Early warning of a potential rockburst risk and its area of occurrence helps to take effective and targeted measures to mitigate rockburst hazards. This study investigates the microseismic (MS) spatial-temporal precursory characteristic parameters in a typical steeply inclined and extremely thick coal seam (SIETCS) with high rockburst risk and proposes three spatial/temporal quantification parameters and a spatial-temporal early warning method. Analysis results of temporal parameters show that the sharp-rise-sharp-drop variation in total daily energy and event count can be regarded as a precursor for high energy tremor. The appearance of peak values of both energy deviation (≥20) and event count deviation (≥1) can be regarded as precursors that indicate imminent rockburst danger. A laboratory acoustic emission (AE) experiment reveals that precursor characteristics obtained from the study can be feasibly used to warn the rockburst risk. The spatial evolution laws of spatial parameters show that the high energy density index of MS (EDIM), velocity, velocity anomaly regions correlate well with stress concentration and rockburst risk areas. The field application verifies that the temporal-spatial early warning method can identify the potential rockburst risk in a temporal sequence and rockburst risk areas during the temporal early warning period.

Suggested Citation

  • Zhenlei Li & Shengquan He & Dazhao Song & Xueqiu He & Linming Dou & Jianqiang Chen & Xudong Liu & Panfei Feng, 2021. "Microseismic Temporal-Spatial Precursory Characteristics and Early Warning Method of Rockburst in Steeply Inclined and Extremely Thick Coal Seam," Energies, MDPI, vol. 14(4), pages 1-27, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1186-:d:504079
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    References listed on IDEAS

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    1. Chengguo Zhang & Faham Tahmasebinia & Ismet Canbulat & Onur Vardar & Serkan Saydam, 2018. "Analytical Determination of Energy Release in a Coal Mass," Energies, MDPI, vol. 11(2), pages 1-16, January.
    2. Faham Tahmasebinia & Chengguo Zhang & Ismet Canbulat & Samad Sepasgozar & Serkan Saydam, 2020. "A Novel Damage Model for Strata Layers and Coal Mass," Energies, MDPI, vol. 13(8), pages 1-17, April.
    3. Ning Li & R. Jimenez, 2018. "A logistic regression classifier for long-term probabilistic prediction of rock burst hazard," 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. 90(1), pages 197-215, January.
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    Cited by:

    1. Piotr Bańka & Adam Lurka & Łukasz Szuła, 2023. "Ground Motion Prediction of High-Energy Mining Seismic Events: A Bootstrap Approach," Energies, MDPI, vol. 16(10), pages 1-15, May.

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