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Meng J，Wu Y X，Li Y N. 2022. First arrival time picking algorithm of micro-seismic based on improved STA/LTA and adaptive VMD. Acta Seismologica Sinica，44（3）：388−400. DOI: 10.11939/jass.20200199
Citation: Meng J，Wu Y X，Li Y N. 2022. First arrival time picking algorithm of micro-seismic based on improved STA/LTA and adaptive VMD. Acta Seismologica Sinica，44（3）：388−400. DOI: 10.11939/jass.20200199
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First arrival time picking algorithm of micro-seismic based on improved STA/LTA and adaptive VMD

  • Institute of Disaster Prevention，Hebei Sanhe 065201，China

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  • Received Date: December 03, 2020
  • Revised Date: May 25, 2021
  • Available Online: April 07, 2022
  • Published Date: June 26, 2022
    Graphical Abstract
    • Abstract
  • Accurate and reliable picking of the first arrival time is one of the critical steps in micro-seismic monitoring. Aiming at the problem of low accuracy of first arrival picking for micro-seisms under low signal-to-noise ratio, the traditional short term averaging/long term averaging algorithm is improved by introducing weight factor according to the change of signal amplitude to improve the accuracy of initial pickup. In order to further reduce the pickup error, variational mode decomposition （VMD） is optimized based on cross-correlation coefficient and permutation entropy criterion, and decomposition layers are determined adaptively. Then, the signals of 2−3 s before and after the initial pickup are decomposed by VMD, and the Kurtosis-Akaike information criterion （AIC） values of the decomposed intrinsic mode functions （IMF） are calculated to get the arrival time of each IMF, and the secondary arrival time is obtained by comprehensively weighting the picking results and energy ratios of each IMF. Simulation results show that the improved STA/LTA can reduce the initial picking error by more than 0.01 s at low SNR; compared with empirical mode decomposition （EMD） and wavelet packet decomposition, the adaptive VMD decomposition can reduce the picking error again, and the finalaverage picking error is less than 0.023 s. The first arrival time picking results of real micro-seismic signals show that the proposed algorithm can identify the first break of P-wave quickly and effectively, and the error is smaller than that of manual picking, which shows that the algorithm is effective and the picking accuracy is high.
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  • 贾瑞生,谭云亮,孙红梅,洪永发. 2015. 低信噪比微震P波震相初至自动拾取方法[J]. 煤炭学报,40(8):1845–1852.
    Jia R S,Tan Y L,Sun H M,Hong Y F. 2015. Method of automatic detection on micro-seismic P-arrival time under low signal-to-noise ratio[J]. Journal of China Coal Society,40(8):1845–1852 (in Chinese).
    李伟,江晓林,陈海波,金珠鹏,刘志军,李兴伟,林井祥. 2018. 基于EEMD_Hankel_SVD的矿山微震信号降噪方法[J]. 煤炭学报,43(7):1910–1917.
    Li W,Jiang X L,Chen H B,Jin Z P,Liu Z J,Li X W,Lin J X. 2018. Denosing method of mine microseismic signal based on EEMD_Hankel_SVD[J]. Journal of China Coal Society,43(7):1910–1917 (in Chinese).
    刘晗,张建中. 2014. 微震信号自动检测的STA/LTA算法及其改进分析[J]. 地球物理学进展,29(4):1708–1714. doi: 10.6038/pg20140429
    Liu H,Zhang J Z. 2014. STA/LTA algorithm analysis and improvement of microseismic signal automatic detection[J]. Progress in Geophysics,29(4):1708–1714 (in Chinese).
    刘晓明,赵君杰,王运敏,彭平安. 2017. 基于改进的STA/LTA方法的微地震P波自动拾取技术[J]. 东北大学学报(自然科学版),38(5):740–745.
    Liu X M,Zhao J J,Wang Y M,Peng P A. 2017. Automatic picking of microseismic events P-wave arrivals based on improved method of STA/LTA[J]. Journal of Northeastern University (Natural Science),38(5):740–745 (in Chinese).
    唐贵基,王晓龙. 2015. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J]. 西安交通大学学报,49(5):73–81. doi: 10.7652/xjtuxb201505012
    Tang G J,Wang X L. 2015. Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing[J]. Journal of Xi’an Jiaotong University,49(5):73–81 (in Chinese).
    田优平,赵爱华. 2016. 基于小波包和峰度赤池信息量准则的P波震相自动识别方法[J]. 地震学报,38(1):71–85. doi: 10.11939/jass.2016.01.007
    Tian Y P,Zhao A H. 2016. Automatic identification of P-phase based on wavelet packet and kurtosis-AIC method[J]. Acta Seismologica Sinica,38(1):71–85 (in Chinese).
    王志坚,常雪,王俊元,杜文华,段能全,党长营. 2018. 排列熵优化改进变模态分解算法诊断齿轮箱故障[J]. 农业工程学报,34(23):59–66. doi: 10.11975/j.issn.1002-6819.2018.23.007
    Wang Z J,Chang X,Wang J Y,Du W H,Duan N Q,Dang C Y. 2018. Gearbox fault diagnosis based on permutation entropy optimized variational mode decomposition[J]. Transactions of the Chinese Society of Agricultural Engineering,34(23):59–66 (in Chinese).
    赵大鹏,刘希强,刘尧兴,王志铄,赵晖,张亚琳. 2013. 高阶统计量及AIC方法在区域地震事件和直达P波初动识别中的应用[J]. 地震地磁观测与研究,34(5/6):61–69.
    Zhao D P,Liu X Q,Liu Y X,Wang Z S,Zhao H,Zhang Y L. 2013. Detection of regional seismic events by high order statistics method and automatic identification of direct P-wave first motion by AIC method[J]. Seismological and Geomagnetic Observation and Research,34(5/6):61–69 (in Chinese).
    郑近德,程军圣,杨宇. 2013. 改进的EEMD算法及其应用研究[J]. 振动与冲击,32(21):21–26. doi: 10.3969/j.issn.1000-3835.2013.21.004
    Zheng J D,Cheng J S,Yang Y. 2013. Modified EEMD algorithm and its applications[J]. Journal of Vibration and Shock,32(21):21–26 (in Chinese).
    郑小霞,周国旺,任浩翰,符杨. 2017. 基于变分模态分解和排列熵的滚动轴承故障诊断[J]. 振动与冲击,36(22):22–28.
    Zheng X X,Zhou G W,Ren H H,Fu Y. 2017. A rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy[J]. Journal of Vibration and Shock,36(22):22–28 (in Chinese).
    朱权洁,姜福兴,魏全德,王博,刘金海,刘晓辉. 2018. 煤层水力压裂微震信号P波初至的自动拾取方法[J]. 岩石力学与工程学报,37(10):2319–2333.
    Zhu Q J,Jiang F X,Wei Q D,Wang B,Liu J H,Liu X H. 2018. An automatic method determining arrival times of microseismic P-phase in Hydraulic fracturing of coal seam[J]. Chinese Journal of Rock Mechanics and Engineering,37(10):2319–2333 (in Chinese).
    Bandt C,Pompe B. 2002. Permutation entropy:A natural complexity measure for time series[J]. Phys Rev Lett,88(17):174102. doi: 10.1103/PhysRevLett.88.174102
    Charles M,Ge M C. 2018. Enhancing manual P-phase arrival detection and automatic onset time picking in a noisy microseismic data in underground mines[J]. Int J Min Sci Technol,28(4):691–699. doi: 10.1016/j.ijmst.2017.05.024
    Dragomiretskiy K,Zosso D. 2014. Variational mode decomposition[J]. IEEE Trans Signal Proc,62(3):531–544. doi: 10.1109/TSP.2013.2288675
    Gaci S. 2014. The use of wavelet-based denoising techniques to enhance the first-arrival picking on seismic traces[J]. IEEE Trans Geosci Remote Sens,52(8):4558–4563. doi: 10.1109/TGRS.2013.2282422
    Kirbas I,Peker M. 2017. Signal detection based on empirical mode decomposition and Teager-Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis[J]. Neural Comput Appl,28(10):3035–3045. doi: 10.1007/s00521-016-2333-5
    Li F Y,Zhang B,Verma S,Marfurt K J. 2018. Seismic signal denoising using thresholded variational mode decomposition[J]. Explora Geophys,49(4):450–461. doi: 10.1071/EG17004
    Li X B,Shang X Y,Wang Z W,Dong L J,Weng L. 2016. Identifying P-phase arrivals with noise:An improved Kurtosis method based on DWT and STA/LTA[J]. J Appl Geophys,133:50–61. doi: 10.1016/j.jappgeo.2016.07.022
    Li X B, Shang X Y, Morales-Esteban A, Wang Z W. 2017. Identifying P phase arrival of weak events: The Akaike information criterion picking application based on the empirical mode decomposition[J] Comput Geosci, 100: 57–66.
    Liu M Z,Yang J X,Cao Y P,Fu W N,Cao Y L. 2017. A new method for arrival time determination of impact signal based on HHT and AIC[J]. Mech Syst Signal Proc,86:177–187. doi: 10.1016/j.ymssp.2016.10.003
    Shang X Y,Li X B,Morales-Esteban A,Dong L J. 2018. Enhancing micro-seismic P-phase arrival picking:EMD-cosine function-based denoising with an application to the AIC picker[J]. J Appl Geophys,150:325–337. doi: 10.1016/j.jappgeo.2017.09.012
    Xue Y J,Cao J X,Wang D X,Du H K,Yao Y. 2016. Application of the variational-mode decomposition for seismic time-frequency analysis[J]. IEEE J Select Top Appl Earth Observ Remote Sens,9(8):3821–3831. doi: 10.1109/JSTARS.2016.2529702
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