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

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


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.


Wave first arrivals Sequence labelling Deep learning 



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.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yang Yu
    • 1
  • Jianfeng Lin
    • 1
  • Lei Zhang
    • 2
  • Guiquan Liu
    • 1
    Email author
  • 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

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