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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

  • Shengquan He
  • Dazhao SongEmail author
  • Zhenlei Li
  • Xueqiu He
  • Jianqiang Chen
  • Donghui Li
  • Xianghui Tian
Original Paper
  • 89 Downloads

Abstract

Rock burst is one of the main coal and rock dynamic disasters in steeply inclined and extremely thick coal seams (SIETCS). Timely identification of potential precursor information enables effective and specifically targeted measures to mitigate hazards. To provide a reference for the determination of precursor information in SIETCS with similar conditions, an in situ investigation that lasted for more than 1 year was conducted on the + 450 horizontal no. B3 + 6 fully mechanized top-coal caving face of the Wudong coal mine. This mine has an average dip angle of 87° and experienced a total of three rock bursts. The investigation focused on the consequences of rock burst (including the source location, energy, damage range, and modes of ensuing damage), the temporal and spatial evolution laws of the monitoring parameters of microseismic (MS) and acoustic emissions (AE), and the relationship between precursory information of both the AE and MS monitoring systems. Furthermore, a comparison of the evolution laws of the precursory characteristics between steeply and gently inclined coal seams was conducted. The obtained results indicate that rock burst failure mainly shows a directionality from south to north of the roadway, and the main factor causing the rock burst was the suspended rock pillar. Prior to the rock burst, sources of MS events gradually clustered and congregated around the rock pillar or coalface, and high-energy events (energy > 103 J) noticeably increased. Similar variations of sharp rise–sharp drop were found for both daily total energy and event counts. Prior to rock burst or large energy mine tremors, the AE energy deviation continued to rise, a peak was observed in the total number of high-value daily AE deviations, and the precursory information of AE was ahead of the precursory information of the MS monitoring system. The evolution laws of MS precursor information prior to rock burst differed between SIETCS and gently inclined coal seams. These evolution laws can be used as precursory warning for rock burst in SIETCS. Integrated monitoring of the AE and MS monitoring systems realizes the real-time monitoring and early warning from the germination stage to the occurrence of rock burst, and can determine the danger area in space. This can improve the early warning efficiency and provides a solid foundation for safe mining in SIETCS.

Keywords

Rock burst Steeply inclined and extremely thick coal seam Microseismic Precursor information Spatio-temporal evolution Early warning 

Abbreviations

MS

Microseismic

AE

Acoustic emission

EMR

Electromagnetic radiation

WCM

Wudong coal mine

SIETCS

Steeply inclined and extremely thick coal seam

List of symbols

Ed

Daily total energy

Pt

Acoustic emission (AE) energy of the current moment

Bt

Mean value of the AE energy of all AE events within 24 h preceding the current time

Dt

Energy deviation

Dt i

The ith energy deviation calculated for the day

Ni

Judgment result corresponding to the ith energy deviation of the day

C

Threshold of the energy deviation value

n

Total number of MS events that occur daily

m

Total number of daily AE energy deviations

Ds

Total number of deviation values on each day that exceed the threshold value

Notes

Acknowledgements

Special thanks should be extended to Wudong coal mine for the provided raw data. This work was supported, and financed, by the State Key Research Development Programme of China (no. 2016YFC0801408), the National Natural Science Foundation of China (nos. 51634001, 51774023).

References

  1. Abdul-Wahed M, Heib M, Senfaute G (2006) Mining-induced seismicity: seismic measurement using multiplet approach and numerical modeling. Int J Coal Geol 66:137–147CrossRefGoogle Scholar
  2. Alber M, Fritschen R, Bischoff M, Meier T (2009) Rock mechanical investigations of seismic events in a deep longwall coal mine. Int J Rock Mech Min Sci 46(2):408–420CrossRefGoogle Scholar
  3. Amitrano D (2012) Variability in the power-law distributions of rupture events. Eur Phys J Spec Top 205(1):199–215CrossRefGoogle Scholar
  4. Cai M, Kaiser PK, Morioka H, Minami M, Maejima T, Tasaka Y, Kurose H (2007) FLAC/PFC coupled numerical simulation of AE in large-scale underground excavations. Int J Rock Mech Min Sci 44:550–564CrossRefGoogle Scholar
  5. Cai W, Dou LM, Zhang M, Cao WZ, Shi JQ, Feng LF (2018) A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring. Tunn Undergr Space Technol 80:232–245CrossRefGoogle Scholar
  6. Dou LM, Chen TJ, Gong SY, He H, Zhang SB (2012) Rock burst hazard determination by using computed tomography technology in deep workface. Saf Sci 50:736–740CrossRefGoogle Scholar
  7. Feng GL, Feng XT, Chen BR, Xiao YX, Yu Y (2015) A microseismic method for dynamic warning of rock burst development processes in tunnels. Rock Mech Rock Eng 48(5):2061–2076CrossRefGoogle Scholar
  8. Gu S, Wang C, Jiang B, Tan Y, Li N (2012) Field test of rock burst danger based on drilling pulverized coal parameters. Disaster Adv 5:237–240Google Scholar
  9. He XQ, Chen WX, Nie BS, Mitri H (2011) Electromagnetic emission theory and its application to dynamic phenomena in coal-rock. Int J Rock Mech Min Sci 48:1352–1358CrossRefGoogle Scholar
  10. He MC, Nie W, Zhao ZY, Guo W (2012) Experimental investigation of bedding plane orientation on the rockburst behavior of sandstone. Rock Mech Rock Eng 45:311–326CrossRefGoogle Scholar
  11. He J, Dou LM, Gong SY, Li J, Ma ZQ (2017) Rock burst assessment and prediction by dynamic and static stress analysis based on micro-seismic monitoring. Int J Rock Mech Min Sci 93:46–53CrossRefGoogle Scholar
  12. Hosseini N (2017) Evaluation of the rockburst potential in longwall coal mining using passive seismic velocity tomography and image subtraction technique. J Seismol 21(1):1–10CrossRefGoogle Scholar
  13. Jiang BY, Wang LG, Lu YL, Wang CQ, Ma D (2016) Combined early warning method for rockburst in a deep island, fully mechanized caving face. Arab J Geosci 9(20):743–754CrossRefGoogle Scholar
  14. Lesniak A, Isakow Z (2009) Space–time clustering of seismic events and hazard assessment in the Zabrze-Bielszowice coal mine, Poland. Int J Rock Mech Min Sci 46:918–928CrossRefGoogle Scholar
  15. Li ZL, Dou LM, Cai W, Wang GF, He J, Gong SY, Ding YL (2014) Investigation and analysis of the rock burst mechanism induced within fault-pillars. Int J Rock Mech Min Sci 70:192–200CrossRefGoogle Scholar
  16. Li G, Liang ZZ, Tang CA (2015) Morphologic interpretation of rock failure mechanisms under uniaxial compression based on 3D multiscale high-resolution numerical modeling. Rock Mech Rock Eng 48:2235–2262CrossRefGoogle Scholar
  17. Li ZH, Lou Q, Wang EY, Liu SJ, Niu Y (2017) Study on acoustic-electric-heat effect of coal and rock failure processes under uniaxial compression. J Geophys Eng 15:71–80CrossRefGoogle Scholar
  18. Li ZL, He XQ, Dou LM, Wang GF (2018) Rock burst occurrences and microseismicity in a longwall panel experiencing frequent rockbursts. Geosci J 623–639Google Scholar
  19. Lu CP, Dou LM, Zhang N, Xue JH, Wang XN, Liu H, Zhang JW (2013) Microseismic frequency-spectrum evolutionary rule of rock burst triggered by roof fall. Int J Rock Mech Min Sci 64(6):6–16CrossRefGoogle Scholar
  20. Lu CP, Liu GJ, Liu Y, Zhang N, Xue JH, Zhang L (2015) Microseismic multi-parameter characteristics of rock burst hazard induced by hard roof fall and high stress concentration. Int J Rock Mech Min Sci 76:18–32CrossRefGoogle Scholar
  21. Lu CP, Liu GJ, Zhang N, Zhao TB, Liu Y (2016a) Inversion of stress field evolution consisting of static and dynamic stresses by microseismic velocity tomography. Int J Rock Mech Min Sci 87:8–22CrossRefGoogle Scholar
  22. Lu CP, Liu Y, Wang HY, Liu PF (2016b) Microseismic signals of double-layer hard and thick igneous strata separation and fracturing. Int J Coal Geol 160–161:28–41CrossRefGoogle Scholar
  23. Meng FZ, Zhou H, Wang ZQ, Zhang LM, Kong L, Li SJ, Zhang CQ (2016) Experimental study on the prediction of rockburst hazards induced by dynamic structural plane shearing in deeply buried hard rock tunnels. Int J Rock Mech Min Sci 86:210–223CrossRefGoogle Scholar
  24. Mitri HS, Tang B, Simon R (1999) FE modelling of mining-induced energy release and storage rates. J S Afr Inst Min Metall 99(2):103–110Google Scholar
  25. Parsons T (2008) Persistent earthquake clusters and gaps from slip on irregular faults. Nat Geosci 1:59–63CrossRefGoogle Scholar
  26. Scholz C (1968) The frequency-magnitude relation of microfracturing in rock and its relation to earthquakes. Bull Seismol Soc Am 58:399–415Google Scholar
  27. Song DZ, Wang EY, Wang C, Xu FL (2010) Electromagnetic radiation early warning criterion of rock burst based on statistical theory. Int J Min Sci Technol 20(5):686–690Google Scholar
  28. Song DZ, Wang EY, Li ZH, Qiu LM, Xu ZY (2017) EMR: an effective method for monitoring and warning of rock burst hazard. Geomech Eng 12(1):53–69CrossRefGoogle Scholar
  29. Su GH, Shi YJ, Feng XT, Jiang JQ, Zhang J, Jiang Q (2017) True-triaxial experimental study of the evolutionary features of the acoustic emissions and sounds of rockburst processes. Rock Mech Rock Eng 51:1–15Google Scholar
  30. Tan YL, Yin YC, Gu ST, Tian ZW (2015) Multi-index monitoring and evaluation on rock burst in Yangcheng mine. Shock Vib 2015 (2):1–5Google Scholar
  31. Tokuji U, Yosihiko O, Ritsuko S, Matsu’ura M (1995) The centenary of the Omori formula for a decay law of aftershock activity. Earth Planets Space 43(1):1–33Google Scholar
  32. Wang HW, Jiang YD, Zhu J, Shan RY, Wang C (2013) Numerical investigation on the assessment and mitigation of coal bump in an island longwall panel. Int J Min Sci Technol 23(5):625–630CrossRefGoogle Scholar
  33. Zhang N, Zhang NC, Han CL, Qian DY, Xue F (2014a) Borehole stress monitoring analysis on advanced abutment pressure induced by Longwall mining. Arab J Geosci 7:457–463CrossRefGoogle Scholar
  34. Zhang ZZ, Gao F, Shang XJ (2014b) Rock burst proneness prediction by acoustic emission test during rock deformation. J Cent South Univ 21(1):373–380CrossRefGoogle Scholar
  35. Zhao YZ, Wu ZL, Jiang CS, Zhu CZ (2008) Present deep deformation along the longmenshan fault by seismic data and implications for the tectonic context of the Wenchuan earthquake. Acta Geol Sin 82(12):1778–1787Google Scholar
  36. Zhu ST, Feng Y, Jiang FX, Liu JH (2018) Mechanism and risk assessment of overall-instability-induced rockbursts in deep island longwall panels. Int J Rock Mech Min Sci 106:342–349CrossRefGoogle Scholar
  37. Zou YH (2009) The study of acoustic emission (AE) forecasting coal and rock disaster technique. Int J Coal Sci Technol 15(2):157–160Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Shengquan He
    • 1
    • 2
  • Dazhao Song
    • 1
    • 2
    Email author
  • Zhenlei Li
    • 1
    • 2
  • Xueqiu He
    • 1
    • 2
    • 3
  • Jianqiang Chen
    • 4
  • Donghui Li
    • 1
    • 2
  • Xianghui Tian
    • 1
    • 2
  1. 1.School of Civil and Resources EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal MineUniversity of Science and Technology BeijingBeijingChina
  3. 3.Zhong-an Academy of Safety EngineeringBeijingChina
  4. 4.Shenhua Xinjiang Energy Company LimitedUrumqiChina

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