Coal mine microseismic velocity model inversion based on first arrival time difference

  • Sen CongEmail author
  • Yun-hong Wang
  • Jian-Yuan Cheng
Original Paper


The formation velocity is an important factor affecting the precise location of microseismic source. The establishment of elastic wave velocity model in the monitoring area to satisfy the requirements for precise location of seismic source has been a technical problem for the mine microseismic monitoring. Based on the assumption of horizontal layered medium condition, a new velocity model inversion method has been proposed. According to the concept of equal difference time surface, the first arrival travel time difference between the measured points and datum points is investigated on the basis of the observation point of first arrival travel time duration placed in the middle in the observational network. The minimal difference (double time difference) between the measured first arrival time difference and the calculated first arrival time difference is taken as the constraint condition, and the objective function is constructed to solve the velocity model. The DIRECT fast search algorithm with global optimization characteristics is applied to solve the objective function. This method is used to carry out the trial treatment for the mine microseismic model data and the measured data. The results show that the stratified velocity model under the horizontal layered medium can be obtained by his method using the microseismic data of known seismic source, with a better adaptability to different monitoring systems. Through the test for the actual data of “well-ground” joint microseismic monitoring, the velocity model obtained by the method in this paper can get more accurate location of the seismic source.


Microseismic monitoring First arrival time difference Velocity model DIRECT algorithm Microseismic source location 


  1. Chen BR, Feng XT, Li SL, Yuan JP, Xu SC (2009) Microseism source location with hierarchical strategy based on particle swarm optimization. Chin J Rock Mech Eng (in Chinese) 28(4):740–749. CrossRefGoogle Scholar
  2. Collins DS, Pinnock I, Toya Y (2014) Seismic event location and source mechanism accounting for complex block geology and voids. In: Smeallie P, Robert J (eds) Proceedings of the 48th ARMA US Rock Mechanics/Geomechanics Symposium, Alexandria, p 14–7530Google Scholar
  3. Dai F, Guo L, Xu NW, Fan YL, Xu J, Jiang P (2016) Improvement of microseismic location based on an anisotropic velocity model. Chin J Geophys (in Chinese) 59:3292–3301. CrossRefGoogle Scholar
  4. Dong LJ, Li XB (2013) A microseismic/acoustic emission source locationmethod using arrival times of PS waves for unknown velocity system. International Journal of Distributed Sensor Networks 2013:1–8.
  5. Dong LJ, Li XB, Ma J, Tang LZ (2017) Three-dimensional analytical comprehensive solutions for acoustic emission/ microseismic sources of unknown velocity system. Chin J Rock Mech Eng(in Chinese) 36:186–197. CrossRefGoogle Scholar
  6. Dou LM, He XQ (2011) Theory and technology of rock burst prevention. China University of Mining and Technology Press, XuzhouGoogle Scholar
  7. Duraiswami R, Zotkin D, Davis LS (1999) Exact solutions for the problem of source location from measured time differences of arrival. J Acoust Soc Am 106(4):2277–2279. CrossRefGoogle Scholar
  8. Feng GL, Feng XT, Chen BR, Xiao YX, Jiang Q (2015) Sectional velocity model for microseismic source location in tunnels. Tunn Undergr Space Technol 45:73–83. CrossRefGoogle Scholar
  9. Gong SY, Dou LM, Ma XP, Mu ZL, He H, He J (2012) Study on the construction and solution technique of anisotropic velocity model in the location of coal mine tremor. Chin J Geophys (in Chinese) 55(5):1757–1763. CrossRefGoogle Scholar
  10. Jiang YD, Pan YS, Jiang FX, Dou LM, Ju Y (2014) State of the art review on mechanism and prevention of coal bumps in China. J China 39(2):205–213. CrossRefGoogle Scholar
  11. Jones DR, Perttunen CD, Stuchman BE (1993) Lipschitzian optimization without the Lipschitz constant. J Optim Theory Appl 79:157–181. CrossRefGoogle Scholar
  12. Li XB, Dong LJ (2011) Comparison of two methods in acoustic emission source location using four sensors without measuring sonic speed. Sens Lett 9:1501–1505. CrossRefGoogle Scholar
  13. Li HY, Jiang FX, Yang SH (2006) Research and application of microseismic monitoring location of strata fracturing based on Matlab. J China Coal Soc 31(2):154–158. CrossRefGoogle Scholar
  14. Li J, Gao YT, Xie YL, Zhou Y, Yang K (2014) Improvement of microseism locating based on simplex method without velocity measuring. Chin J Rock Mech Eng(in Chinese) 33:1336–1345. CrossRefGoogle Scholar
  15. Lurka A, Swanson P (2009) Improvements in seismic event locations in a deep western U.S.coal mine using tomographic velocity models and an evolutionary serch algorithm. Min Sci Technol 19(5):599–603. CrossRefGoogle Scholar
  16. Niewiadomski J (1989) Application of singular value decomposition method for location of seismic events in mines. Pure Appl Geophys Pageoph 129(3–4):553–570. CrossRefGoogle Scholar
  17. Pei DH, Quirein JA, Cornish BE, Dan Q, Warpinski NR (2009) Velocity calibration for microseismic monitoring: a very fast simulated annealing approach for joint-objective optimization. Geophysics 74:47–55.
  18. Sambridge M, Gallagher K (1983) Earthquake hypocenter location using genetic algorithms. Bull Seismol Soc Am 83(5):19–1491 doi:2-s2.0–0027881943Google Scholar
  19. Thurber CH (1985) Nonlinear earthquake location:theory and examples. Bull Seismol Soc Am 75(0):779–790Google Scholar
  20. Tian Y, Chen XF (2005) A rapid and accurate two-point ray tracing method in horizontally layered. Acta Seismol Sin 27(2):147–154. CrossRefGoogle Scholar
  21. Waldhauser F, Ellsworth WL (2000) A double-difference earth-quake location algorithrn:method and application to the Northern Hayward fault. California. Bull Seismol Soc Am 90(6):1353–1368. CrossRefGoogle Scholar
  22. Wang YH (2016) Grid-search method on microseismic source fast location based on DIRECT Algorithm. Prog Geophys 31(4):1700–1708.
  23. Wang YH, Dong RJ (2016) Study on micro-seismic forward modeling in coalbed methane well hydraulic fracturing. Coal Sci Technol 44(s1):137–141Google Scholar
  24. Wei X (2012)Research of global optimization algorithm base on meta model. Dissertation, Huazhong University of Science & TechnologyGoogle Scholar
  25. Wu HG, Li TJ (2007) Application of DIRECT algorithm in PMD compensation. Optical Technique 33(5):669–672. CrossRefGoogle Scholar
  26. Zhang H, Zhang ZL, Yao H et al(2014) The study on precise positioning of earthquake in dachang mining area based on genetic algorithm 11:260–265. doi:
  27. Zhou YM (1994) Rapid three-dimensional hypocentral determination using a master station method. Geophy Res 99:1549–1555. CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  1. 1.College of Geology and EnvironmentXi’an University of Science and TechnologyXi’anChina
  2. 2.Xi’an Research Institute of China Coal Technology and Engineering Group CorpXi’anChina

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