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Microseismic event detection based on multiscale detection convolutional neural network

YAN ZHANG1,2,3 ,  XIAO-QIU LIU1,2,3 ,  LI-WEI SONG1,2,3 ,  HONG-LI DONG4,5
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1 School of Physics and Electronic Engineering, Northeast Petroleum University, Da Qing, Heilongjiang 163318, P .R. China.,
5 Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Da Qing, Heilongjiang 163318, P .R. China.,
3 Key Laboratory of Networking and Intelligent Control of Heilongjiang Province, Da Qing, Heilongjiang 163000, P .R. China.,
4 School of Physics and Electronic Engineering, Northeast Petroleum University, Da Qing, Heilongjiang 163318; P .R. China.,
JSE 2023, 32(5), 455–481;
Submitted: 5 August 2023 | Accepted: 15 September 2023 | Published: 1 October 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

Zhang, Y ., Liu, X.Q., Song, L.W. and Dong, H.L., 2023. Microseismic event detection based on multiscale detection convolutional neural network. Journal of Seismic Exploration, 32: 455-477. The traditional microseismic event detection method is mainly based on the characteristic calculation of microseismic signals. Its accuracy is greatly affected by the empirical parameter setting of the algorithm, characteristics selection of signal, and signal-to-noise ratio of microseismic signals. Furthermore, it also takes a long computation time when dealing with massive microseismic data. Therefore, this paper presents a method of microseismic event detection based on the multiscale neural network. Firstly, according to the characteristics of microseismic signals, one- dimensional convolutional neural network is built to extract the fine-grained features of the shallow layers and the semantic features of the deep layers. Then, the credibility factor model is established for the detection results of the different scale feature expressions, and the final recognition results are obtained by uncertainty reasoning. Compared with wavelet analysis, BP neural network, and traditional convolution neural network, the experimental results show that the proposed model is superior to other methods, and has better anti-noise and generalization ability. In addition, this method also provides a new strategy for processing other monitoring signals with large interference.

Keywords
microseismic event detection
neural network
certainty factor
multiscale
References
  1. Boureau, Y .L, Bach, F., LeCun, Y . and Ponce, J., 2010. Learning mid-level features for recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE,: 2559-2566.
  2. Cai, Z., Fan, Q., Feris, R.S. and Vasconcelos, N., 2016. A unified multiscale deep convolutional neural network for fast object detection. Proc. Europ. Conf. Comput. Vis., Springer Internat. Publish.: 354-370.
  3. Dai, J., Li, Y ., He, K. and Sun, 2016. R-FCN: Object detection via region-based fully convolutional networks. Proc. 30th Ann Conf. Neur. Inform. Process. Syst., Barcelona: 379-387.
  4. Kulesh, M., Holschneider, M. and Diallo, M.S., 2008. Geophysical wavelet library:
  5. Applications of the continuous wavelet transform to the polarization and dispersion analysis of signals. Comput. Geosci., 34: 1732-1752.
  6. Li, Y ., Yang, T.H. and Liu, H.L., 2016. Real-time microseismic monitoring and its characteristic analysis in working face with high-intensity mining. J. Appl. Geophys., 132: 152-163.
  7. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y . and Berg. A.C., 2016.
  8. SSD: Single shot multibox detector. Europ. Conf. Comput. Vision, Springer International Publishing: 21-37.
  9. Liu, W, Rabinovich, A., Berg, A.C. and Parsenet, B., 2015. Looking wider to see better. arXiv preprint arXiv:1506.04579.
  10. Maochen, G., 2005. Efficient mine microseismic monitoring. Internat. J. Coal Geol., 64,: 44-56.
  11. Maxwell, S.C., Rutledge, J., Jones, R. and Fehler, M., 2010. Petroleum reservoir characterization using downhole microseismic monitoring. Geophysics, 75(5): A129-A137.
  12. Redmon, J. and Farhadi, A., 2017. YOLO9000: better, faster, stronger. Proc. IEEE Conf.
  13. Comput. Vis. Pattern Recognit., Piscataway: 7263-7271.
  14. Redmon, J. and Farhadi, A., 2018. YOLOv3: An incremental improvement. Proc. IEEE
  15. Conf. Comput. Vis. Pattern Recognit., Piscataway: 1-6.
  16. Shang, X., Li, X., Morales-Esteban, A. and Chen, G.H., 2017. Improving microseismic event and quarry blast classification using Artificial Neural Networks based on Principal Component Analysis. Earthq. Engineer., 99: 42-149.
  17. Sheng, G.Q., Tang, X.G., Xie, K. and Jie, X., 2019. Hydraulic fracturing microseismic first arrival picking method based on non-subsampled shearlet transform and higher-order-statistics. J. Seismic Explor., 28: 593-618.
  18. Shi-Chao, L.V ., Song, W.Q., Liu, Y .M., Guo, X.Z. and Zhang, H.F., 2013. The polarization constrained LTA/STA method for automatic detection of microseismic. Geophys. Geochem. Explor., 37: 488-493.
  19. Singh, B., Najibi , M., Davis , L.S., 2018. SNIPER: Efficient multiscale training. Proc. 32nd Conf. Neural Informat. Process. Syst., Montreal: 9310-9320.
  20. Sleeman, R. and Eck, T.V ., 1999. Robust automatic P-phase picking: an on-line implementation in the analysis of broadBand seismogram recordings. Phys. Earth Planet. Inter., 113: 265-275.
  21. Sun, J., Wang, L. and Hou, H., 2012. Application of micro-seismic monitoring technology in mining engineering. Internat. J. Mining Sci. Technol., 22: 79-83.
  22. Tan, Y . and Nava, F.A., 1988. Automatic seismic wave detection and autoregressive model method. Mathemat. Geol., 20: 37-48.
  23. Velis, D., Sabbione, J.I. and Sacchi, M.D., 2015. Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering. Geophysics, 80(6): WC25-WC38.
  24. Xie, J., Yang, C., Gupta, N., King, M.J. and Datta-Gupta, A., 2015. Integration of shale gas production data and micro-seismic for fracture and reservoir properties using fast marching method. SPE Journal, 20: 347-359.
  25. Xu, S., Zhang, C.R., Chen, Z.Y ., Li, Y .H. and Liu, J.P., 2021. Accurate identification of microseismic waveforms based on an improved neural network model. J. Appl. Geophys., 190: 926-951.
  26. Li, Y ., Yang, T.H., Liu, H.L., Wang, H., Hou, X.-G., Zhang, P.-H. and Wang, P.-T., 2016.
  27. Real-time microseismic monitoring and its characteristic analysis in working face with high-intensity mining. J. Appl. Geophys., 132: 152-163.
  28. Zhang, H., 2003. Automatic P-wave arrival detection and picking with multiscale wavelet analysis for single-component recordings. Bull. Seismol. Soc. Am., 93: 1904-1912.
  29. Zhang, Y ., Chan, W. and Jaitly, N., 2017. Very deep convolutional networks for end-toend speech recognition. IEEE Internat. Conf. Acoust., Speech Signal Process.: 4845-4849.
  30. Zhao, H., Shi, J., Qi, X., Wang, X. and Jia, J. , 2017. Pyramid scene parsing network.
  31. IEEE Conf. Comput. Vis. Pattern Recognit., Piscataway: 2881-2890.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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