Advertisement

Identification of Seismic Wave First Arrivals from Earthquake Records via Deep Learning

  • Yang Yu
  • Jianfeng Lin
  • Lei Zhang
  • Guiquan Liu
  • Jing Hu
  • Yuyang Tan
  • Haijiang Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)

Abstract

For seismic location and tomography, it is important to pick P- and S-wave first arrivals. However, traditional methods mainly determine P- and S-wave first arrivals separately from a signal processing perspective, which requires the extraction of waveform attributes and tuning parameters manually. Also, traditional methods suffer from noise as they are operated on the whole earthquake record. In this paper, we propose a deep neural network framework to enhance picking P- and S-wave first arrivals from a sequential perspective. Specifically, we first transform the picking first arrival problem as a sequence labelling problem. Then, the rough ranges for P- and S-wave first arrivals are determined simultaneously through the proposed deep neural network model. Based on these rough ranges, the performance of existing picking methods can be greatly enhanced. Experimental results on two real-world datasets demonstrate the effectiveness of the proposed framework.

Keywords

Wave first arrivals Sequence labelling Deep learning 

Notes

Acknowledgement

This work was supported in part by the Natural Science Foundation of China (Grant No. 61502001) and by the Academic and Technology Leader Imported Project of Anhui University (No. J01006057). The authors would like to thank Data Management Centre of China National Seismic Network at Institute of Geophysics, China Earthquake Administration and Northern California Earthquake Data Center (NCEDC) for providing waveform data for this study.

References

  1. 1.
    NCEDC: Northern California Earthquake Data Center, UC Berkeley Seismological Laboratory. Dataset (2014).  https://doi.org/10.7932/ncedc
  2. 2.
    Akram, J.: Downhole microseismic monitoring: processing, algorithms and error analysis. Ph.D. thesis. University of Calgary (2014)Google Scholar
  3. 3.
    Allen, R.V.: Automatic earthquake recognition and timing from single traces. Bull. Seismol. Soc. Am. 68(5), 1521–1532 (1978)Google Scholar
  4. 4.
    Graves, A., et al.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer, Berlin (2012).  https://doi.org/10.1007/978-3-642-24797-2CrossRefMATHGoogle Scholar
  5. 5.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  6. 6.
    Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning, vol. 951, pp. 282–289 (2001)Google Scholar
  7. 7.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  8. 8.
    Maity, D., Aminzadeh, F., Karrenbach, M.: Novel hybrid artificial neural network based autopicking workflow for passive seismic data. Geophys. Prospect. 62(4), 834–847 (2014)CrossRefGoogle Scholar
  9. 9.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  10. 10.
    Renals, S., Morgan, N., Bourlard, H., Cohen, M., Franco, H.: Connectionist probability estimators in hmm speech recognition. IEEE Trans. Speech Audio Process. 2(1), 161–174 (1994)CrossRefGoogle Scholar
  11. 11.
    Robinson, A.J.: An application of recurrent nets to phone probability estimation. IEEE Trans. Neural Netw. 5(2), 298–305 (1994)CrossRefGoogle Scholar
  12. 12.
    Sleeman, R., van Eck, T.: Robust automatic p-phase picking: an on-line implementation in the analysis of broadband seismogram recordings. Phys. Earth Planet. Inter. 113(1), 265–275 (1999)CrossRefGoogle Scholar
  13. 13.
    Thireou, T., Reczko, M.: Bidirectional long short-term memory networks for predicting the subcellular localization of eukaryotic proteins. IEEE/ACM Trans. Comput. Biol. Bioinform. 4(3), 441–446 (2007)CrossRefGoogle Scholar
  14. 14.
    Withers, M., et al.: A comparison of select trigger algorithms for automated global seismic phase and event detection. Bull. Seismol. Soc. Am. 88(1), 95–106 (1998)Google Scholar
  15. 15.
    Zheng, J., Lu, J., Peng, S., Jiang, T.: An automatic microseismic or acoustic emission arrival identification scheme with deep recurrent neural networks. Geophys. J. Int. 212(2), 1389–1397 (2017)CrossRefGoogle Scholar
  16. 16.
    Zheng, X.F., Yao, Z.X., Liang, J.H., Zheng, J.: The role played and opportunities provided by igp dmc of china national seismic network in wenchuan earthquake disaster relief and researches. Bull. Seismol. Soc. Am. 100(5B), 2866–2872 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yang Yu
    • 1
  • Jianfeng Lin
    • 1
  • Lei Zhang
    • 2
  • Guiquan Liu
    • 1
  • Jing Hu
    • 3
  • Yuyang Tan
    • 3
  • Haijiang Zhang
    • 3
  1. 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.School of Computer Science and TechnologyAnhui UniversityHefeiChina
  3. 3.School of Earth and Space SciencesUniversity of Science and Technology of ChinaHefeiChina

Personalised recommendations