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Volume 45 Issue 2
Mar.  2023
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Article Navigation >  Acta Seismologica Sinica  > 2023  >  45(2): 234-245
Jiao M R，Dong F J，Luo H，Yu J K，Ma L. 2023. P-arrival picking method of mine microseisms by fusing of GRU and self-attention mechanism. Acta Seismologica Sinica，45（2）：234−245 doi: 10.11939/jass.20220034
Citation: Jiao M R，Dong F J，Luo H，Yu J K，Ma L. 2023. P-arrival picking method of mine microseisms by fusing of GRU and self-attention mechanism. Acta Seismologica Sinica，45（2）：234−245 doi: 10.11939/jass.20220034
shu

P-arrival picking method of mine microseisms by fusing of GRU and self-attention mechanism

doi: 10.11939/jass.20220034
  • 1.

    School of Information，Liaoning University，Shenyang 110036，China

  • 2.

    Liaoning Earthquake Agency，Shenyang 110034，China

  • Received Date: 2022-03-21
  • Rev Recd Date: 2022-05-17
    • Available Online: 2023-03-20
  • Publish Date: 2023-03-15
    Abstract
  • Seismic phase picking is the first key step of mine microseisms detection, and its accuracy often directly affects the quality of subsequent event processing, so we proposed a method for P-arrival picking of mine microseisms which is based on deep learning method. Firstly the CNNDet model is constructed for events detection and P-arrival pre-picking, and then the CGANet model was constructed to accurately pick up the P-arrival time for the detected events by introducing the self-attention mechanism and the gated recurrent unit. Comparison with STA/LTA, DPick and PpkNet shows that the precision and the recall ratio of seismic event detection by our method are more than 98% for the test sets, and the mean error and the standard deviation of P-arrival are 0.014 s and 0.051 s, respectively. Our method is superior to the above three methods in terms of precision, the recall ratio and the standard deviation. In addition, the experimental tests on samples with different SNRs prove that our method can still maintain high precision on the condition of low SNR. In the source location, our method also shows more excellent performance. The P-arrival picking method proposed in this paper which is based on gated recurrent unit and self-attention mechanism provides a new idea for microseisms monitoring and accurate identification of rock burst and other disasters.

     

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  • [1]
    Chen G B,Teng P C,Li T,Wang C Y,Chen S J,Zhang G H. 2021. Evaluation model of rock burst in coal mine and its application[J]. Journal of Taiyuan University of Technology,52(6):966–973 (in Chinese).
    [2]
    Chen Z,Ding L L,Luo H,Song B Y,Zhang M,Pan Y S. 2020. Mine microseismic events classification based on improved wavelet decomposition and ELM[J]. Journal of China Coal Society,45(S2):637–648 (in Chinese).
    [3]
    Fu J H,Wang X,Li Z T,Tan Q,Wang J J. 2019. Automatic picking up earthquake’s P waves using signal-to-noise ratio under a strong noise environment[J]. Chinese Journal of Geophysics,62(4):1405–1412 (in Chinese).
    [4]
    Li A,Yang J S,Peng C Y,Zheng Y,Liu S. 2020. Seismic phase identification using the convolutional neural networks based on sample enhancement[J]. Acta Seismologica Sinica,42(2):163–176 (in Chinese).
    [5]
    Li C H,Zhang J L,Cai M F,Zhang L,Lin Q H. 2009. Simulating test research of impacting disasters in coal mines[J]. Journal of University of Science and Technology Beijing,31(1):1–9 (in Chinese).
    [6]
    Li Y,Han X H,Zhang L,Zhang H X,Li G. 2023. Seismic P-wave first-arrival pickup model based on spatiotemporal attention mechanism[J]. Computer Engineering and Applications,59(6):113–124 (in Chinese).
    [7]
    Wang Y Y,Ding R W,Li J P,Zhao L H,Zhao S,Zhang S W. 2021. Automatic pickup of microseismic P-wave arrival based on improved STA/LTA and MLoG operators[J]. Journal of Shandong University of Science and Technology (Natural Science),40(6):1–10 (in Chinese).
    [8]
    Zhang Y L,Yu Z C,Hu T Y,He C. 2021. Multi-trace joint downhole microseismic phase detection and arrival picking method based on U-Net[J]. Chinese Journal of Geophysics,64(6):2073–2085 (in Chinese).
    [9]
    Zhao H B,Liu R,Gu T,Liu Y H,Jiang D M. 2021. Research on automatic picking method of microseismic signal P wave based on deep learning mode[J]. Chinese Journal of Rock Mechanics and Engineering,40(S2):3084–3097 (in Chinese).
    [10]
    Zhao M,Chen S,Fang L H,Yuen D A. 2019. Earthquake phase arrival auto-picking based on U-shaped convolutional neural network[J]. Chinese Journal of Geophysics,62(8):3034–3042 (in Chinese).
    [11]
    Zhou F Y,Jin L P,Dong J. 2017. Review of convolutional neural network[J]. Chinese Journal of Computers,40(6):1229–1251 (in Chinese).
    [12]
    Akaike H. 1974. A new look at the statistical model identification[J]. IEEE Trans Automat Contr,19(6):716–723. doi: 10.1109/TAC.1974.1100705
    [13]
    Allen R. 1982. Automatic phase pickers:Their present use and future prospects[J]. Bull Seismol Soc Am,72(6B):S225–S242. doi: 10.1785/BSSA07206B0225
    [14]
    Chen B R,Feng X T,Li S L,Yuan J P,Xu S C. 2009. Microseism source location with hierarchical strategy based on particle swarm optimization[J]. Chin J Rock Mech Eng,28(4):740–749.
    [15]
    Dai H C,Macbeth C. 1995. Automatic picking of seismic arrivals in local earthquake data using an artificial neural network[J]. Geophys J Int,120(3):758–774. doi: 10.1111/j.1365-246X.1995.tb01851.x
    [16]
    Dai H C,Macbeth C. 1997. The application of back-propagation neural network to automatic picking seismic arrivals from single-component recordings[J]. J Geophys Res:Solid Earth,102(B7):15105–15113. doi: 10.1029/97JB00625
    [17]
    Dubey A C, Barnard R L. 1997. Detection and Remediation Technologies for Mines and Minelike Targets II[M]. Orlando, Florida: SPIE: 21–24.
    [18]
    Grayson R L,Kinilakodi H,Kecojevic V. 2009. Pilot sample risk analysis for underground coal mine fires and explosions using MSHA citation data[J]. Saf Sci,47(10):1371–1378. doi: 10.1016/j.ssci.2009.03.004
    [19]
    Mousavi S M,Ellsworth W L,Zhu W Q,Chuang L Y,Beroza G C. 2020. Earthquake transformer:An attentive deep-learning model for simultaneous earthquake detection and phase picking[J]. Nat Commun,11(1):3952. doi: 10.1038/s41467-020-17591-w
    [20]
    Perol T,Gharbi M,Denolle M. 2018. Convolutional neural network for earthquake detection and location[J]. Sci Adv,4(2):e1700578. doi: 10.1126/sciadv.1700578
    [21]
    Ronneberger O, Fischer P, Brox T. 2015. U-net: Convolutional networks for biomedical image segmentation[C]//18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer: 234–241.
    [22]
    Saad O M,Chen Y K. 2021. Earthquake detection and P-wave arrival time picking using capsule neural network[J]. IEEE Trans Geosci Remote Sens,59(7):6234–6243. doi: 10.1109/TGRS.2020.3019520
    [23]
    Tang S B,Wang J X,Tang C N. 2021. Identification of microseismic events in rock engineering by a convolutional neural network combined with an attention mechanism[J]. Rock Mech Rock Eng,54(1):47–69. doi: 10.1007/s00603-020-02259-0
    [24]
    Ursano R J,Cerise F P,DeMartino R,Reissman D B,Shear M K. 2006. The impact of disasters and their aftermath on mental health[J]. J Clin Psychiatry,67(1):7–14. doi: 10.4088/JCP.v67n0102
    [25]
    Vaezi Y,van der Baan M. 2015. Comparison of the STA/LTA and power spectral density methods for microseismic event detection[J]. Geophys J Int,203(3):1896–1908. doi: 10.1093/gji/ggv419
    [26]
    Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł, Polosukhin I. 2017. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA: Curran Associates Inc.: 6000–6010.
    [27]
    Wang Y W,Li X J,Wang Z F,Shi J P,Bao E H. 2021. Deep learning for P-wave arrival picking in earthquake early warning[J]. Earthq Eng Eng Vib,20(2):391–402. doi: 10.1007/s11803-021-2027-6
    [28]
    Zhang J J,Tang Y L,Li H J. 2018. STA/LTA fractal dimension algorithm of detecting the P-wave arrival[J]. Bull Seismol Soc Am,108(1):230–237. doi: 10.1785/0120170099
    [29]
    Zhou Y J,Yue H,Kong Q K,Zhou S Y. 2019. Hybrid event detection and phase-picking algorithm using convolutional and recurrent neural networks[J]. Seismol Res Lett,90(3):1079–1087. doi: 10.1785/0220180319
    [30]
    Zhu W Q,Beroza G C. 2018. PhaseNet:A deep-neural-network-based seismic arrival time picking method[J]. Geophys J Int,216(1):261–273.
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