EL-Picker: 基于集成学习的余震P波初动实时拾取方法

Translated title of the contribution: El-Picker: a machine learning-enhanced robust P-phase picker for real-time seismic monitoring

Dazhong Shen, Qi Zhang, Tong Xu, Hengshu Zhu, Wenjia Zhao, Zikai Yin, Peilun Zhou, Lihua Fang, Enhong Chen, Hui Xiong

Research output: Contribution to journalArticlepeer-review


Identifying the arrival times of seismic P-phases plays a significant role in real-time seismic monitoring, which provides critical guidance for emergency response activities. While considerable research has been conducted on this topic, efficiently capturing the arrival times of seismic P-phases hidden within intensively distributed and noisy seismic waves, such as those generated by the aftershocks of destructive earthquakes, remains a real challenge since most common existing methods in seismology rely on laborious expert supervision. To this end, in this paper, we present a machine learning-enhanced framework based on ensemble learning strategy, EL-Picker, for the automatic identification of seismic P-phase arrivals on continuous and massive waveforms. More specifically, EL-Picker consists of three modules, namely, Trigger, Classifier, and Refiner, and an ensemble learning strategy is exploited to integrate several machine learning classifiers. An evaluation of the aftershocks following the Ms 8.0 Wenchuan earthquake demonstrates that EL-Picker can not only achieve the best identification performance but also identify 120% more seismic P-phase arrivals as complementary data. Meanwhile, experimental results also reveal both the applicability of different machine learning models for waveforms collected from different seismic stations and the regularities of seismic P-phase arrivals that might be neglected during the manual inspection. These findings clearly validate the effectiveness, efficiency, flexibility, and stability of EL-Picker.

Translated title of the contributionEl-Picker: a machine learning-enhanced robust P-phase picker for real-time seismic monitoring
Original languageChinese (Traditional)
Pages (from-to)912-926
Number of pages15
JournalScientia Sinica Informationis
Issue number6
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering (miscellaneous)


  • Ensemble learning
  • Machine learning
  • P-phase picker
  • Real-time seismic monitoring
  • Wenchuan aftershocks


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