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Case study of microseismic tomography and multi-parameter characteristics under mining disturbances

采矿扰动下层析成像及微震多参数特征实例研究

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Abstract

Rockburst caused by mining disturbance may cause rock damage. Combining the microseismic (MS) multiparameter, high-magnitude MS event distribution, and MS tomography may be an effective method for hazard precursor identification in underground mining. Firstly, a self-developed MS monitoring system was built in an underground mine in Shaanxi, China. Then, based on the tomography results, a correlation model was established between velocity anomalies and mining activity regions, and rockburst risk areas were delineated. Furthermore, multi-parameters were analyzed, including b value (rock fracture parameter), S value (MS activity), tomography results, high-magnitude MS event distribution and the in-site survey in 795 and 860 levels of Shaanxi Zhen-ao Mine. Ultimately, based on the rockburst case on June 7 and the multi-parameters analysis of 795 level to 860 level of Zhen-ao Mine, the risk areas were identified. MS information can be used as an early warning identification of hazards, and has a wide application in the future.

摘要

采矿活动诱发的岩爆可引起矿区内强烈的振动和严重的岩石破坏。结合微震多参数、大震级事 件分布与层析成像特征综合井下分析, 综合为一种较好的预警方法。首先, 在中国陕西某地下矿山建 立了自主研发的微震监测系统。然后, 根据层析成像计算结果得出了波速场异常与采矿活动区的关联 模型, 初步确定了可能存在风险的区域与采矿活动的特征。此外, 对b 值、S 值、成像结果、大震级微 震事件分布及陕西矿区795 和860 水平现场调查情况等多个因素进行了分析。根据该矿山6 月7 日岩爆 案例, 将795 至860 水平现场的多个区域进行对比分析, 从工程角度发现了潜在的不安全区域, 对安 全施工管理提供了参考。微震监测系统可作为一个危险预警的手段, 具有广泛的应用前景。

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References

  1. DONG Long-jun, TONG Xiao-jie, LI Xi-bing, et al. Some developments and new insights of environmental problems and deep mining strategy for cleaner production in mines [J]. Journal of Cleaner Production, 2019, 210: 1562–1578. DOI: https://doi.org/10.1016/j.jclepro.2018.10.291.

    Article  Google Scholar 

  2. MA Ju, DONG Long-jun, ZHAO Guo-yan, et al. Discrimination of seismic sources in an underground mine using full waveform inversion [J]. International Journal of Rock Mechanics and Mining Sciences, 2018, 106: 213–222. DOI: https://doi.org/10.1016/j.ijrmms.2018.04.032.

    Article  Google Scholar 

  3. MA Ju, DONG Long-jun, ZHAO Guo-yan, et al. Qualitative method and case study for ground vibration of tunnels induced by fault-slip in underground mine [J]. Rock Mechanics and Rock Engineering, 2019, 52(6): 1887–1901. DOI: https://doi.org/10.1007/s00603-018-1631-x.

    Article  Google Scholar 

  4. DONG Long-jun, LUO Qiao-mu. Investigations and new insights on earthquake mechanics from fault slip experiments [J]. Earth-Science Reviews, 2022, 228: 104019. DOI: https://doi.org/10.1016/j.earscirev.2022.104019.

    Article  Google Scholar 

  5. DONG Long-jun, ZHANG Yi-han, BI Shui-jin, et al. Uncertainty investigation for the classification of rock microfracture types using acoustic emission parameters [J]. International Journal of Rock Mechanics and Mining Sciences, 2023, 162: 105292. DOI: https://doi.org/10.1016/j.ijrmms.2022.105292.

    Article  Google Scholar 

  6. ZHAO Da-peng, HASEGAWA A, HORIUCHI S. Tomographic imaging of P and S wave velocity structure beneath northeastern Japan [J]. Journal of Geophysical Research, 1992, 97(B13): 19909. DOI: https://doi.org/10.1029/92jb00603.

    Article  Google Scholar 

  7. ZHAO D, KANAMORI H, NEGISHI H, et al. Tomography of the source area of the 1995 Kobe earthquake: Evidence for fluids at the hypocenter? [J]. Science, 1996, 274(5294): 1891–1894. DOI: https://doi.org/10.1126/science.274.5294.1891.

    Article  Google Scholar 

  8. WANG Ze-wei, ZHAO Da-peng, LIU Xin, et al. Seismic attenuation tomography of the source zone of the 2016 Kumamoto earthquake (M 7.3) [J]. Journal of Geophysical Research: Solid Earth, 2017, 122(4): 2988–3007. DOI: https://doi.org/10.1002/2016jb013704.

    Article  Google Scholar 

  9. HUA Yuan-yuan, ZHAO Da-peng, TOYOKUNI G, et al. Tomography of the source zone of the great 2011 Tohoku earthquake [J]. Nature Communications, 2020, 11: 1163. DOI: https://doi.org/10.1038/s41467-020-14745-8.

    Article  Google Scholar 

  10. AKI K, LEE W H K. Determination of three-dimensional velocity anomalies under a seismic array using first P arrival times from local earthquakes: 1. A homogeneous initial model [J]. Journal of Geophysical Research, 1976, 81(23): 4381–4399. DOI: https://doi.org/10.1029/jb081i023p04381.

    Article  Google Scholar 

  11. AKI K, CHRISTOFFERSSON A, HUSEBYE E S. Determination of the three-dimensional seismic structure of the lithosphere [J]. Journal of Geophysical Research, 1977, 82(2): 277–296. DOI: https://doi.org/10.1029/jb082i002p00277.

    Article  Google Scholar 

  12. VIDALE J E. Finite-difference traveltime calculation [J]. Bulletin of the Seismological Society of America, 1999, 78(6): 2062–2076. DOI: https://doi.org/10.1785/bssa0800020395.

    Google Scholar 

  13. KANDA Y. Well-to-well seismic measurements [J]. Journal of the Japan Society of Engineering Geology, 1973, 14(4): 159–168. DOI: https://doi.org/10.5110/jjseg.14.159.

    Article  Google Scholar 

  14. LURKA A. Location of high seismic activity zones and seismic hazard assessment in Zabrze Bielszowice coal mine using passive tomography [J]. Journal of China University of Mining and Technology, 2008, 18(2): 177–181. DOI: https://doi.org/10.1016/S1006-1266(08)60038-3.

    Article  Google Scholar 

  15. HOSSEINI N, ORAEE K, SHAHRIAR K, et al. Passive seismic velocity tomography on longwall mining panel based on simultaneous iterative reconstructive technique (SIRT) [J]. Journal of Central South University, 2012, 19(8): 2297–2306. DOI: https://doi.org/10.1007/s11771-012-1275-z.

    Article  Google Scholar 

  16. GONG Si-yuan. Research and application of using mine tremor velocity tomography to forecast rockburst danger in coal mine [D]. Xuzhou: China University of Mining and Technology, 2010. (in Chinese)

    Google Scholar 

  17. CAI Wu, DOU Lin-ming, CAO An-ye, et al. Application of seismic velocity tomography in underground coal mines: A case study of Yima mining area, Henan, China [J]. Journal of Applied Geophysics, 2014, 109: 140–149. DOI: https://doi.org/10.1016/j.jappgeo.2014.07.021.

    Article  Google Scholar 

  18. CAO An-ye, DOU Lin-ming, CAI Wu, et al. Case study of seismic hazard assessment in underground coal mining using passive tomography [J]. International Journal of Rock Mechanics and Mining Sciences, 2015, 78: 1–9. DOI: https://doi.org/10.1016/j.ijrmms.2015.05.001.

    Article  Google Scholar 

  19. LI Jing, GONG Si-yuan, HE Jiang, et al. Spatio-temporal assessments of rockburst hazard combining b values and seismic tomography [J]. Acta Geophysica, 2017, 65(1): 77–88. DOI: https://doi.org/10.1007/s11600-017-0008-y.

    Article  Google Scholar 

  20. ZHOU Kun-you, DOU Lin-ming, GONG Si-yuan, et al. Study of rock burst risk evolution in front of deep longwall panel based on passive seismic velocity tomography [J]. Geofluids, 2020, 2020: 1–14. DOI: https://doi.org/10.1155/2020/8888413.

    Google Scholar 

  21. LI Gang. Research on the deep-seated fracture development features and evaluation on the facture rock mass quality of the slope at the ye Ba hydropower station [D]. Chengdu: Chengdu University of Technology, 2015. (in Chinese)

    Google Scholar 

  22. HOSSEINI N. Evaluation of the rockburst potential in longwall coal mining using passive seismic velocity tomography and image subtraction technique [J]. Journal of Seismology, 2017, 21(5): 1101–1110. DOI: https://doi.org/10.1007/s10950-017-9654-4.

    Article  Google Scholar 

  23. WANG Ze-wei, LI Xi-bing, ZHAO Da-peng, et al. Time-lapse seismic tomography of an underground mining zone [J]. International Journal of Rock Mechanics and Mining Sciences, 2018, 107: 136–149. DOI: https://doi.org/10.1016/j.ijrmms.2018.04.038.

    Article  Google Scholar 

  24. WANG Ze-wei, LI Xi-bing, SHANG Xue-yi. Distribution characteristics of mining-induced seismicity revealed by 3-D ray-tracing relocation and the FCM clustering method [J]. Rock Mechanics and Rock Engineering, 2019, 52(1): 183–197. DOI: https://doi.org/10.1007/s00603-018-1585-z.

    Article  Google Scholar 

  25. SHEN Wen-long, SHI Guo-cang, WANG Yun-gang, et al. Tomography of the dynamic stress coefficient for stress wave prediction in sedimentary rock layer under the mining additional stress [J]. International Journal of Mining Science and Technology, 2021, 31(4): 653–663. DOI: https://doi.org/10.1016/j.ijmst.2021.04.003.

    Article  Google Scholar 

  26. MA Xu, WESTMAN E, MALEK F, et al. Stress redistribution monitoring using passive seismic tomography at a deep nickel mine [J]. Rock Mechanics and Rock Engineering, 2019, 52(10): 3909–3919. DOI: https://doi.org/10.1007/s00603-019-01796-7.

    Article  Google Scholar 

  27. DONG Long-jun, PEI Zhong-wei, XIE Xin, et al. Early identification of abnormal regions in rock-mass using traveltime tomography [J]. Engineering, 2023, 22: 191–200. DOI: https://doi.org/10.1016/j.eng.2022.05.016.

    Article  Google Scholar 

  28. LU Cai-ping, LIU Guang-jian, LIU Yang, et al. Microseismic multi-parameter characteristics of rockburst hazard induced by hard roof fall and high stress concentration [J]. International Journal of Rock Mechanics and Mining Sciences, 2015, 76: 18–32. DOI: https://doi.org/10.1016/j.ijrmms.2015.02.005.

    Article  Google Scholar 

  29. LI Zhen-lei, HE Xue-qiu, DOU Lin-ming, et al. Rockburst occurrences and microseismicity in a longwall panel experiencing frequent rockbursts [J]. Geosciences Journal, 2018, 22(4): 623–639. DOI: https://doi.org/10.1007/s12303-017-0076-7.

    Article  Google Scholar 

  30. HE Sheng-quan, SONG Da-zhao, LI Zhen-lei, et al. Precursor of spatio-temporal evolution law of MS and AE activities for rock burst warning in steeply inclined and extremely thick coal seams under caving mining conditions [J]. Rock Mechanics and Rock Engineering, 2019, 52(7): 2415–2435. DOI: https://doi.org/10.1007/s00603-018-1690-z.

    Article  Google Scholar 

  31. LIU Jian-po, XU Shi-da, LI Yuan-hui, et al. Analysis of rock mass stability based on mining-induced seismicity: A case study at the Hongtoushan copper mine in China [J]. Rock Mechanics and Rock Engineering, 2019, 52(1): 265–276. DOI: https://doi.org/10.1007/s00603-018-1541-y.

    Article  Google Scholar 

  32. LIU Fei, TANG Chun-an, MA Tian-hui, et al. Characterizing rockbursts along a structural plane in a tunnel of the Hanjiang-to-weihe River diversion project by microseismic monitoring [J]. Rock Mechanics and Rock Engineering, 2019, 52(6): 1835–1856. DOI: https://doi.org/10.1007/s00603-018-1649-0.

    Article  Google Scholar 

  33. MONDAL D, ROY P N S, KUMAR M. Monitoring the strata behavior in the destressed zone of a shallow Indian longwall panel with hard sandstone cover using mine-microseismicity and borehole televiewer data [J]. Engineering Geology, 2020, 271: 105593. DOI: https://doi.org/10.1016/j.enggeo.2020.105593.

    Article  Google Scholar 

  34. YU Qun, ZHAO Dan-chen, XIA Ying-jie, et al. Multivariate early warning method for rockburst monitoring based on microseismic activity characteristics [J]. Frontiers in Earth Science, 2022, 10: 837333. DOI: https://doi.org/10.3389/feart.2022.837333.

    Article  Google Scholar 

  35. LIU Fei, MA Tian-hui, TANG Chun-an, et al. Prediction of rockburst in tunnels at the Jinping II hydropower station using microseismic monitoring technique [J]. Tunnelling and Underground Space Technology, 2018, 81: 480–493. DOI: https://doi.org/10.1016/j.tust.2018.08.010.

    Article  Google Scholar 

  36. WANG Yang, HE Man-chao, REN Fu-qiang, et al. Source analysis of acoustic emissions during granite strain burst [J]. Geomatics, Natural Hazards and Risk, 2019, 10(1): 1542–1562. DOI: https://doi.org/10.1080/19475705.2019.1593888.

    Article  Google Scholar 

  37. TANG Zhi-li, LIU Xiao-li, XU Qian-jun, et al. Stability evaluation of deep-buried TBM construction tunnel based on microseismic monitoring technology [J]. Tunnelling and Underground Space Technology, 2018, 81: 512–524. DOI: https://doi.org/10.1016/j.tust.2018.08.028.

    Article  Google Scholar 

  38. DONG Long-jun, SUN Dao-yuan, LI Xi-bing, et al. Theoretical and experimental studies of localization methodology for AE and microseismic sources without pre-measured wave velocity in mines [J]. IEEE Access, 2017, 5: 16818–16828. DOI: https://doi.org/10.1109/ACCESS.2017.2743115.

    Article  Google Scholar 

  39. DONG Long-jun, HU Qing-chun, TONG Xiao-jie, et al. Velocity-free MS/AE source location method for three-dimensional hole-containing structures [J]. Engineering, 2020, 6(7): 827–834. DOI: https://doi.org/10.1016/j.eng.2019.12.016.

    Article  Google Scholar 

  40. DONG Long-jun, TONG Xiao-jie, HU Qing-chun, et al. Empty region identification method and experimental verification for the two-dimensional complex structure [J]. International Journal of Rock Mechanics and Mining Sciences, 2021, 147: 104885. DOI: https://doi.org/10.1016/j.ijrmms.2021.104885.

    Article  Google Scholar 

  41. DONG Long-jun, TAO Qing, HU Qing-chun, et al. Acoustic emission source location method and experimental verification for structures containing unknown empty areas [J]. International Journal of Mining Science and Technology, 2022, 32(3): 487–497. DOI: https://doi.org/10.1016/j.ijmst.2022.01.002.

    Article  Google Scholar 

  42. BIANCO M J, GERSTOFT P. Travel time tomography with adaptive dictionaries [J]. IEEE Transactions on Computational Imaging, 2018, 4(4): 499–511. DOI: https://doi.org/10.1109/TCI.2018.2862644.

    Article  Google Scholar 

  43. GU Ji-cheng, WEI Fu-sheng. The quantinigation of seismic activity; seismicity [J]. Earthquake Research in China, 1987, 3(S1)232–243 (in Chinese)

    Google Scholar 

  44. LEI Xing-lin, LI Shi-nian, LIU Li-qiang. Seismic b-value for foreshock AE events preceding repeated stick-slips of pre-cut faults in granite [J]. Applied Sciences, 2018, 8(12): 2361. DOI: https://doi.org/10.3390/app8122361.

    Article  Google Scholar 

  45. YANG Jing, MU Zong-long, YANG Sheng-qi. Experimental study of acoustic emission multi-parameter information characterizing rock crack development [J]. Engineering Fracture Mechanics, 2020, 232: 107045. DOI: https://doi.org/10.1016/j.engfracmech.2020.107045.

    Article  Google Scholar 

  46. DONG Long-jun, TANG Zheng, LI Xi-bing, et al. Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform [J]. Journal of Central South University, 2020, 27(10): 3078–3089. DOI: https://doi.org/10.1007/s11771-020-4530-8.

    Article  Google Scholar 

  47. SHCHERBAKOV R, TURCOTTE D L, RUNDLE J B. A generalized Omori’s law for earthquake aftershock decay [J]. Geophysical Research Letters, 2004, 31(11): L11613. DOI: https://doi.org/10.1029/2004gl019808.

    Article  Google Scholar 

  48. DONG Long-jun, CHEN Yong-chao, SUN Dao-yuan, et al. Implications for rock instability precursors and principal stress direction from rock acoustic experiments [J]. International Journal of Mining Science and Technology, 2021, 31(5): 789–798. DOI: https://doi.org/10.1016/j.ijmst.2021.06.006.

    Article  Google Scholar 

  49. DONG Long-jun, YANG Long-bin, CHEN Yong-chao. Acoustic emission location accuracy and spatial evolution characteristics of granite fracture in complex stress conditions [J]. Rock Mechanics and Rock Engineering, 2023, 56(2): 1113–1130. DOI: https://doi.org/10.1007/s00603-022-03124-y.

    Article  Google Scholar 

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DONG Long-jun provided the concept and edited the draft of manuscript. YAN Xian-hang conducted the literature review and wrote the first draft of the manuscript. WANG Jian and TANG Zheng edited the draft of manuscript.

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Correspondence to Long-jun Dong  (董陇军).

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DONG Long-jun, YAN Xian-hang, WANG Jian, and TANG Zheng declare that they have no conflict of interest.

Additional information

Foundation item: Project(2021YFC2900500) supported by the National Research and Development Program of China; Project (52161135301) supported by the International Cooperation and Exchange of the National Natural Science Foundation of China

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Dong, Lj., Yan, Xh., Wang, J. et al. Case study of microseismic tomography and multi-parameter characteristics under mining disturbances. J. Cent. South Univ. 30, 2252–2265 (2023). https://doi.org/10.1007/s11771-023-5358-9

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