Microseismic source location using the Log-Cosh function and distant sensor-removed P-wave arrival data

Abstract

Source location is the core foundation of microseismic monitoring. To date, commonly used location methods have usually been based on the ray-tracing travel-time technique, which generally adopts an L1 or L2 norm to establish the location objective function. However, the L1 norm usually achieves low location accuracy, whereas the L2 norm is easily affected by large P-wave arrival-time picking errors. In addition, traditional location methods may be affected by the initial iteration point used to find a local optimum location. Furthermore, the P-wave arrival-time data that have travelled long distances are usually poor in quality. To address these problems, this paper presents a microseismic source location method using the Log-Cosh function and distant sensor-removed P-wave arrival data. Its basic principles are as follows: First, the source location objective function is established using the Log-Cosh function. This function has the stability of the L1 norm and location accuracy of the L2 norm. Then, multiple initial points are generated randomly in the mining area, and the established Log-Cosh location objective function is used to obtain multiple corresponding location results. The average value of the 50 location points with the largest data field potential values is treated as the initial location result. Next, the P-wave travel times from the initial location result to triggered sensors are calculated, and then the P-wave arrival data with travel times exceeding 0.2 s are removed. Finally, the aforementioned location steps are repeated with the denoised P-wave arrival dataset to obtain a high-precision location result. Two synthetic events and eight blasting events from the Yongshaba mine, China, were used to test the proposed method. Regardless of whether the P-wave arrival data with long travel times were eliminated, the location error of the proposed method was smaller than that of the L1/L2 norm and trigger-time-based location method (TT1/TT2 method). Furthermore, after eliminating the P-wave arrival data with long travel distances, the location accuracy of these three location methods increased, indicating that the proposed location method has good application prospects.

Keywords

seismic source location / Log-Cosh function / data field theory / location stability

References

[1] DongL-J, TangZ, LiX-B, et al.. Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform [J]. Journal of Central South University, 2020, 27: 3078-3089

[2] CaoW-Z, DurucanS, CaiW, et al.. The role of mining intensity and pre-existing fracture attributes on spatial, temporal and magnitude characteristics of microseismicity in longwall coal mining [J]. Rock Mechanics and Rock Engineering, 2020, 53: 4139-4162

[3] CaoW-Z, DurucanS, CaiW, et al.. A physics-based probabilistic forecasting methodology for hazardous microseismicity associated with longwall coal mining [J]. International Journal of Coal Geology, 2020, 232: 103627

[4] GongF-Q, WuW-X, LiT-B, et al.. Experimental simulation and investigation of spalling failure of rectangular tunnel under different three-dimensional stress states [J]. International Journal of Rock Mechanics and Mining Sciences, 2019, 1221365-1609

[5] CaoW-Z, ShiJ-Q, DurucanS, et al.. Evaluation of shear slip stress transfer mechanism for induced microseismicity at in Salah CO₂ storage site [J]. International Journal of Greenhouse Gas Control, 2021, 107: 103302

[6] GongF-Q, WangY-L, LuoS. Rockburst proneness criteria for rock materials: Review and new insights [J]. Journal of Central South University, 2020, 27(10): 2793-2821

[7] WarpinskiN. Microseismic monitoring: inside and out [J]. Journal of Petroleum Technology, 2009, 61(11): 80-85

[8] GeM-C. Efficient mine microseismic monitoring [J]. International Journal of Coal Geology, 2005, 64(12): 44-56

[9] DuraiswamiR, ZotkinD, DavisL. Exact solutions for the problem of source location from measured time differences of arrival [J]. Journal of the Acoustical Society of America, 1999, 106(4): 277-2277

[10] DaiF, GuoL, XuN-W. Improvement of microseismic location based on an anisotropic velocity model [J]. Chinese Journal of Geophysics-Chinese Edition, 2016, 59(9): 3291-3301

[11] LI Xi-bing, WANG Ze-wei, DONG Long-jun. Locating single-point sources from arrival times containing large picking errors (LPEs): the virtual field optimization method (VFOM) [J]. Sci Rep, 2016, 6. DOI: https://doi.org/10.1038/srep19205.

[12] LiN, WangE-Y, GeM-C. A nonlinear microseismic source location method based on Simplex method and its residual analysis [J]. Arab J Geosci, 2014, 74477-4486

[13] LI Gao-ming, CHEN Jing-yi, HAN Mei. Accurate microseismic event location inversion using a gradient-based method [C]// The SPE Annual Technical Conference and Exhibition. San Antonio, Texas, USA, 2012. DOI: https://doi.org/10.2118/159187-MS.

[14] DONG Long-jun, ZOU Wei, SUN Dao-yuan. Some developments and new insights for microseismic/acoustic emission source localization [J]. Shock and Vibration in Deep Mining Science, 2019: 9732606. DOI: https://doi.org/10.1155/2019/9732606.

[15] NelsonG D, VidaleJ E. Earthquake locations by 3D finite difference travel times [J]. Bulletin of the Seismological Society of America, 1990, 80(2): 395-410

[16] OyeV, RothM. Automated seismic event location for hydrocarbon reservoirs [J]. Computers & Geosciences, 2003, 29(7): 851-63

[17] SongW-Q, YangX-D. A joint inversion combining the gridsearch algorithm and the genetic algorithm under solution-domain constraints for microseismic events [J]. Oil Geophysical Prospecting, 2011, 46(2): 259-266

[18] SpendleyW, HextG R, HimsworthF R. Sequential application of simplex designs in optimisation and evolutionary operation [J]. Technometrics, 1962, 4(4): 441-461

[19] PengK, GuoH-Y, ShangX-Y. Data field application in removing large P-phase arrival picking errors and relocating a mine microseismic event [J]. Soil Dynamics and Earthquake Engineering, 2020, 139: 106359

[20] WangZ-W, LiX-B, ZhaoD-P, et al.. Time-lapse seismic tomography of an underground mining zone [J]. International Journal of Rock Mechanics and Mining Sciences, 2018, 107136-149

[21] 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

[22] ChangX, LiZ, WangP, et al.. Micro-seismic location based on frequency attenuation compensation [J]. Acta Geophysica Sinica, 2018, 61(1): 250-257

[23] WangS-L, GanW-Y, LiD-Y, et al.. Data field for hierarchical clustering [J]. International Journal of Data Warehousing and Mining (IJDWM), 2011, 7(4): 43-63

[24] LiX-B, ShangX-Y, Morales-EstebanA, et al.. Identifying P phase arrival of weak events: The Akaike Information Criterion picking application based on the Empirical Mode Decomposition [J]. Computers & Geosciences, 2017, 100: 57-66

[25] 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

[26] DongL-J, HuQ-C, TongX-J, et al.. Velocity-free MS/AE source location method for three-dimensional hole-containing structures [J]. Engineering, 2020, 6(7): 827-834

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

[28] JiangR-C, DaiF, LiuY, et al.. Fast marching method for microseismic source location in cavern-containing rockmass: performance analysis and engineering application [J]. Engineering, 2021, 771023-1034

[29] WANG Yi, SHANG Xue-yi, PENG Kang, et al. Rock fracture monitoring based on high-precision microseismic event location using 3D multiscale waveform inversion [J]. Geofluids, 2020: e8825140. DOI: https://doi.org/10.1155/2020/8825140.