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Quantitative analysis of seismic velocity tomography in rock burst hazard assessment

Author

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  • Wu Cai
  • Linming Dou
  • Siyuan Gong
  • Zhenlei Li
  • Shasha Yuan

Abstract

In order to quantitatively evaluate the relationship between the tomographic images of P wave velocity and rock burst hazard, the seismic velocity tomography was used to generate the P wave velocity tomograms during the retreat of a longwall panel in a coal mine. Subsequently, a novel index (bursting strain energy) was proposed to characterize the mining seismic hazard map. Finally, the structural similarity (SSIM) index in the discipline of image quality assessment was introduced to quantitatively assess the relation between the bursting strain energy index images and the tomographic images of P wave velocity. The results show that the bursting strain energy index is appropriate for quantitative analysis and seems to be better for expressing the mining seismic hazard than the conventional map. The SSIM values of the future bursting strain energy compared with the P wave velocity and the current bursting strain energy reach up to 0.8908 and 0.8462, respectively, which illustrate that the P wave velocity and the bursting strain energy both are able to detect the rock burst hazard region. Specifically, seismic velocity tomography is superior to the bursting strain energy index in the detection range and the precision and accuracy of detection results. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Wu Cai & Linming Dou & Siyuan Gong & Zhenlei Li & Shasha Yuan, 2015. "Quantitative analysis of seismic velocity tomography in rock burst hazard assessment," 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. 75(3), pages 2453-2465, February.
    • Handle: RePEc:spr:nathaz:v:75:y:2015:i:3:p:2453-2465
    DOI: 10.1007/s11069-014-1443-6
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    Citations

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    Cited by:

    1. Deyuan Fan & Xuesheng Liu & Yunliang Tan & Shilin Song & Qingheng Gu & Lei Yan & Qiang Xu, 2019. "Roof Cutting Parameters Design for Gob-Side Entry in Deep Coal Mine: A Case Study," Energies, MDPI, vol. 12(10), pages 1-25, May.
    2. Cong Wang & Kai Zhan & Xigui Zheng & Cancan Liu & Chao Kong, 2024. "A Method for Evaluating the Data Integrity of Microseismic Monitoring Systems in Mines Based on a Gradient Boosting Algorithm," Mathematics, MDPI, vol. 12(12), pages 1-19, June.
    3. Meng Wu & Yicheng Ye & Qihu Wang & Zhen Zhang & Yan Li & Wen Li, 2023. "Study on the Mechanisms of Rock Mass Watering for Rockburst Prevention in Phosphorite Mines from Laboratory Results," Sustainability, MDPI, vol. 15(11), pages 1-17, June.
    4. 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.
    5. Yiwen Lan & Rui Gao & Bin Yu & Xiangbin Meng, 2018. "In Situ Studies on the Characteristics of Strata Structures and Behaviors in Mining of a Thick Coal Seam with Hard Roofs," Energies, MDPI, vol. 11(9), pages 1-10, September.

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