P- and S-wave arrival time combined Bayesian location method for a microseismic event

Abstract

Microseismic (MS) source location is a critical technology in MS monitoring. Traditionally, P-wave or S-wave travel time-based ray-tracing algorithms are adopted for a mine MS event location. However, only a few data are available for an MS event location usually, which may result in a large location error. A P- and S-wave arrival time combined objective function can obtain a relatively better location result. However, previous studies have encountered several challenges: 1) the combined weighting should be a free parameter determined by the quality of P- and S-wave arrival time data; 2) all the arrival times including bad data are adopted for an MS event location. To handle this, a P- and S-wave arrival time combined Bayesian location (P_SBL) method has been proposed for an MS event location. To reduce the influence of large picking errors, 80% of arrival time data were randomly selected in each iteration. Two synthetic events and eight blasting events were used to test the proposed method. Synthetic results show that the average location error of the P_SBL method is only 9.96 m, when 2 and 4 ms Gaussian noises were separately included for the P-wave and S-wave travel time data. The application results demonstrate that the average location error of the P_SBL method is 31.97 m, which improves the location accuracy by 25.40% and 60.78% compared with the P_BL and S_BL methods, respectively.

Keywords

microseismic monitoring / source location / Bayesian method / joint inversion

References

[1] GeM-C. Efficient Mine microseismic monitoring [J]. International Journal of Coal Geology, 2005, 64(1–2): 44-56

[2] MaK, TangC-A, XuN-W, et al. . Failure precursor of surrounding rock mass around cross tunnel in high-steep rock slope [J]. Journal of Central South University, 2013, 20: 207-217

[3] MaK, ShenQ-Q, SunX-Y, et al. . Rockburst prediction model using machine learning based on microseismic parameters of Qinling water conveyance tunnel [J]. Journal of Central South University, 2023, 30(1): 289-305

[4] LuoZ-Q, WangW, QinY-G, et al. . Early warning of rock mass instability based on multi-field coupling analysis and microseismic monitoring [J]. Transactions of Nonferrous Metals Society of China, 2019, 29(6): 1285-1293

[5] JiangC-B, LiuC-Y, ShangX-Y. Double event joint location method considering P-wave arrival time system errors [J]. Soil Dynamics and Earthquake Engineering, 2021, 149: 106890

[6] KUANG Wen-huan, ZHANG Wei, ZHANG Jie. Locating microseismic events with S-wave data only [C]//SEG Technical Program Expanded Abstracts 2013. Society of Exploration Geophysicists, 2013. DOI: https://doi.org/10.1190/segam2013-0993.1.

[7] ZhouH-C, ZhangW, ZhangJ. Downhole microseismic monitoring for low signal-to-noise ratio events [J]. Journal of Geophysics and Engineering, 2016, 13805-816

[8] ZhouW, WangL-S, GuanL-P, et al. . Microseismic event location using an inverse method of joint P-S phase arrival difference and P-wave arrival difference in a borehole system [J]. Journal of Geophysics and Engineering, 2015, 12(2): 220-226

[9] TianX, ZhangW, ZhangJ. Cross double-difference inversion for simultaneous velocity model update and microseismic event location [J]. Geophysical Prospecting, 2017, 65: 259-273

[10] WangZ-G, LiJ, LiB. Source location mechanism of microseismic monitoring using P-S waves and its effect analysis [J]. DYNA, 2022, 97(1): 39-45

[11] TanY-Y, HeC, MaoZ-H. Microseismic velocity model inversion and source location: The use of neighborhood algorithm and master station method [J]. Geophysics, 2018, 83(4): KS49-KS63

[12] TianX, ZhangW, ZhangJ. Cross double-difference inversion for microseismic event location using data from a single monitoring well [J]. Geophysics, 2016, 81(5): KS183-KS194

[13] OyeV, RothM. Automated seismic event location for hydrocarbon reservoirs [J]. Computers & Geosciences, 2003, 29(7): 851-863

[14] TanY-Y, ZhangH-J, LiJ-L, et al. . Focal mechanism determination for induced seismicity using the neighbourhood algorithm [J]. Geophysical Journal International, 2018, 214: 1715-1731

[15] DongL-J, ZouW, LiX-B, et al. . Collaborative localization method using analytical and iterative solutions for microseismic/acoustic emission sources in the rockmass structure for underground mining [J]. Engineering Fracture Mechanics, 2019, 210: 95-112

[16] LiuC-Y, ShangX-Y, MiaoR-X. Acoustic emission source location on a cylindrical shell structure through grouped sensors based analytical solution and data field theory [J]. Applied Acoustics, 2022, 192: 108747

[17] ShangX-Y, TkalcicH. Point-source inversion of small and moderate earthquakes from P-wave polarities and P/S amplitude ratios within a hierarchical Bayesian framework: Implications for the geysers earthquakes [J]. Journal of Geophysical Research: Solid Earth, 2020, 125(2): e2019JB018492

[18] ShangX-Y, WangY, MiaoR-X. Acoustic emission source location from P-wave arrival time corrected data and virtual field optimization method [J]. Mechanical Systems and Signal Processing, 2022, 163108129

[19] GesretA, DesassisN, NobleM, et al. . Propagation of the velocity model uncertainties to the seismic event location [J]. Geophysical Journal International, 2015, 200152-66

[20] SchumacherT, StraubD, HigginsC. Toward a probabilistic acoustic emission source location algorithm: A Bayesian approach [J]. Journal of Sound and Vibration, 2012, 3314233-4245

[21] MadarshahianR, ZiehlP, CaicedoJ M. Acoustic emission Bayesian source location: Onset time challenge [J]. Mechanical Systems and Signal Processing, 2019, 123: 483-495

[22] BeckJ L, KatafygiotisL S. Updating models and their uncertainties. I: Bayesian statistical framework [J]. Journal of Engineering Mechanics, 1998, 124(4): 455-461

[23] ZhangZ-S, DuJ, GaoF-C. Simultaneous inversion for microseismic event location and velocity model in Vaca Muerta Formation [J]. Geophysics, 2018, 83(3): KS23-KS34

[24] KolbjørnsenO, BulandA, HaugeR, et al. . Bayesian seismic inversion for stratigraphic horizon, lithology, and fluid prediction [J]. Geophysics, 2020, 85(3): R207-R221

[25] PengP-A, WangL-G. Targeted location of microseismic events based on a 3D heterogeneous velocity model in underground mining [J]. PLoS One, 2019, 14(2): e0212881

[26] ZhangC-X, LiX-B, DongL-J, et al. . The three functions four indicators S-wave phase picking method and application for microseismic signal in mines [J]. Chinese Journal of Rock Mechanics and Engineering, 2015, 3481650-1659

[27] ShangX-Y, LiX-B, Morales-EstebanA, et al. . Enhancing micro-seismic P-phase arrival picking: EMD-cosine function-based denoising with an application to the AIC picker [J]. Journal of Applied Geophysics, 2018, 150: 325-337

[28] LiX-B, WangZ-W, DongL-J. Locating single-point sources from arrival times containing large picking errors (LPEs): The virtual field optimization method (VFOM) [J]. Scientific Reports, 2016, 6: 19205

[29] PengK, GuoH-Y, ShangX-Y. Microseismic source location using the Log-Cosh function and distant sensor-removed P-wave arrival data [J]. Journal of Central South University, 2022, 29(2): 712-725

[30] WangY, ShangX-Y, PengK, et al. . Rock fracture monitoring based on high-precision microseismic event location using 3D multiscale waveform inversion [J]. Geofuids, 2020, 20201-18

[31] WangY, ShangX-Y, PengK. Relocating mining microseismic earthquakes in a 3-D velocity model using a windowed cross-correlation technique [J]. IEEE Access, 2020, 8: 37866-37878

[32] PesicekJ D, ChildD, ArtmanB, et al. . Picking versus stacking in a modern microearthquake location: Comparison of results from a surface passive seismic monitoring array in Oklahoma [J]. Geophysics, 2014, 79(6): KS61-KS68