Deep Learning for Picking Seismic Arrival Times

Jian Wang, Zhuowei Xiao, Chang Liu, Dapeng Zhao, Zhenxing Yao

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

Arrival times of seismic phases contribute substantially to the study of the inner working of the Earth. Despite great advances in seismic data collection, the usage of seismic arrival times is still insufficient because of the overload manual picking tasks for human experts. In this work we employ a deep-learning method (PickNet) to automatically pick much more P and S wave arrival times of local earthquakes with a picking accuracy close to that by human experts, which can be used directly to determine seismic tomography. A large number of high-quality seismic arrival times obtained with the deep-learning model may contribute greatly to improve our understanding of the Earth's interior structure.

Original languageEnglish
Pages (from-to)6612-6624
Number of pages13
JournalJournal of Geophysical Research: Solid Earth
Volume124
Issue number7
DOIs
Publication statusPublished - 2019

Keywords

  • arrival times
  • deep learning
  • seismic tomography

ASJC Scopus subject areas

  • Geophysics
  • Geochemistry and Petrology
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science

Fingerprint Dive into the research topics of 'Deep Learning for Picking Seismic Arrival Times'. Together they form a unique fingerprint.

Cite this