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Simultaneous enhancement and detection of microseismic events based on autocorrelation

LEI FENG,  ZHIXIAN GUI* ,  ZHI LI CHEN,  YIZHUO LIU,  QIANG WEI
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Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of Education, Wuhan 430100, P.R. China.,
Cooperative Innovation Center of Unconventional Oil and Gas (Yangtze University), Wuhan 430100, P.R. China.,
College of Geophysics and Petroleum Resources, Yangtze University, Wuhan 430100, P.R. China.,
JSE 2023, 32(4), 337–356;
Submitted: 20 March 2023 | Accepted: 5 July 2023 | Published: 1 August 2023
© 2023 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Feng, L., Gui, Z.X., Chen, Z., Liu, Y.H. and Wei, Q., 2023. Simultaneous enhancement and detection of microseismic events based on autocorrelation. Journal of Seismic Exploration, 32: 337-356. Microseismic monitoring is the main method of hydraulic fracturing evaluation, which is realized by locating the source. Before locating the source, it is necessary to determine the position of the valid signal in the receiving channel. But the data received is very complicated, including the dead trace, strong noise, invalid data, etc. In order to solve some cases, Methods of data enhancement and data detection are mentioned. However, there are some problems in the application of the enhancement method and detection method in microseismic monitoring. Therefore, This research improves the enhancement method to reduce the suppression of the P-wave by the original method and proposes a more efficient detection method based on autocorrelation. The test of synthetic data and filed microseismic data shows that the enhancement method improved and the detection method is effective.

Keywords
microseismic monitoring
denoising
detection
autocorrelation
sensor array
References
  1. Akram, J., 2018. An application of waveform denoising for microseismic data usingpolarization—linearity and time-requency thresholding. Geophys. Prosp., 66: 872-
  2. Arro, H., Prikk, A., Pihu, T. and Opik, I., 2002. Utilization of semi-coke of Estonianshale oil industry. Oil Shale, 19: 117-125.
  3. Eisner, L., Grechka, V. and Williams-Stroud, S., 2010. Future of microseismic analysis:
  4. Integration of monitoring and reservoir simulation. AAPG Hedberg Conf.
  5. Han, J. and van der Baan, M., 2015. Microseismic and seismic denoising via ensembleempirical mode decomposition and adaptive thresholding. Geophysics, 80(6):KS69-KS80.
  6. Iqbal, N., Liu, E., McClellan, J.H., Al-Shuhail, A., Kaka, S. and Zerguine, A., 2018.
  7. Detection and denoising of microseismic events using time—frequencyrepresentation and tensor decomposition. IEEE access, 6: 22993-23006.
  8. Jiang, Y., Wang, R. and Chen, X., 2019. Automatic microseismic events detection usingmultiscale morphological characteristic function. IEEE Transact. Geosci. RemoteSens., 58: 3341-3351.
  9. Li, Z., Gao, J., Liu, N., Sun, F. and Jiang, X., 2019. Random noise suppression ofseismic data by time-frequency peak filtering with variational modedecomposition. Explor. Geophys., 50: 634-644.
  10. Liu, E., Zhu, L., Raj, A.-G., McClellan, J.H., Al-Shuhail, A., Kaka, S.I. and Iqbal, N.,
  11. Microseismic events enhancement and detection in sensor arrays usingautocorrelation-based filtering. Geophys. Prosp., 65: 1496-1509.
  12. Liu, L., Song, W., Zeng, C. and Yang, X., 2021. Microseismic event detection andclassification based on convolutional neural network. J. Appl. Geophys., 104380.
  13. Long, Y., Lin, J., Li, B., Wang, H., and Chen, Z., 2019. Fast-AIC method for automaticfirst arrivals picking of microseismic event with multitrace energy stackingenvelope summation. IEEE Geosci. Remote Sens. Lett., 17: 1832-1836.
  14. Maxwell, S.C., 2005. A brief guide to passive seismic monitoring. In CSEG NationalConvention: 177-178.
  15. McClellan, J.H., Eisner, L., Liu, E., Iqbal, N., Al-Shuhail, A.A. and Kaka, S.L. 2018.
  16. Array processing in microseismic monitoring: Detection, enhancement, andlocalization of induced seismicity. IEEE Signal Process. Magaz., 35(2): 99-111.
  17. Mousavi, S.M. and Langston, C., 2016. Fast and novel microseismic detection usingtime-frequency analysis. Expanded Abstr., 86th Ann. Internat. SEG Mtg., Dallas:2632-2636.
  18. Mousavi, S.M., Langston, C.A. and Horton, S.P., 2016, Automatic microseismicdenoising and onset detection using the synchrosqueezed continuous wavelettransform. Geophysics, 81(4): V341-V355
  19. Pilikos, G. and Faul, A.C., 2017. Bayesian feature learning for seismic compressivesensing and denoising. Geophysics, 82(6): 091-0104.
  20. Shao, J., Wang, Y., Yao, Y., Wu, S., Xue, Q. and Chang, X., 2019. Simultaneousdenoising of multicomponent microseismic data by joint sparse representationwith dictionary learning. Geophysics, 84(5): KS155-KS172.
  21. Song, F., Kuleli, H.S., Tokséz, M.N., Ay, E. and Zhang, H., 2010. An improved methodfor hydrofracture-induced microseismic event detection and phase picking.Geophysics, 75(6): A47-A52.
  22. Trow, A.J., Zhang, H., Record, A.S., Mendoza, K.A., Pankow, K.L. and Wannamaker, P.
  23. E., 2018. Microseismic event detection using multiple geophone arrays insouthwestern Utah. Seismol. Res. Lett., 89: 1660-1670.
  24. Vaezi, Y. and Van der Baan, M., 2015. Comparison of the STA/LTA and power spectraldensity methods for microseismic event detection. Geophys. Supplem. MonthlyNotices Royal Astronom. Soc., 203(3): 1896-1908.
  25. Vera Rodriguez, I., Bonar, D. and Sacchi, M., 2012. Microseismic data denoising using a3C group sparsity constrained time-frequency transform. Geophysics, 77(2): V21-V29. doi: 10.1190/geo2011-0260.1.
  26. Wang, H., Zhang, Q., Zhang, G., Fang, J. and Chen, Y., 2020. Self-training and learningthe waveform features of microseismic data using an adaptive dictionary.Geophysics, 85(3): KS51-KS61.
  27. Zhang, C. and van der Baan, M. (2019). Microseismic denoising and reconstruction byunsupervised machine learning. IEEE Geosci. Remote Sens. Lett., 17: 1114-1118.
  28. Zhang, G., Lin, C. and Chen, Y., 2020. Convolutional neural networks for microseismicwaveform classification and arrival picking. Geophysics, 85(4): WA227-WA240.
  29. Zhang, J., Dong, L. and Xu, N., 2020. Noise suppression of microseismic signals viaadaptive variational mode decomposition and Akaike information criterion.Appl. Sci., 10: 3790.
  30. Zuo, L.Q., Sun, H.M., Mao, Q.C., Liu, X.Y. and Jia, R.S., 2019. Noise suppressionmethod of microseismic signal based on complementary ensemble empiricalmode decomposition and wavelet packet threshold. IEEE Access, 7: 176504-
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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