Using a Deep Neural Network and Transfer Learning to Bridge Scales for Seismic Phase Picking

Chengping Chai, Monica Maceira, Hector J. Santos-Villalobos, Singanallur V. Venkatakrishnan, Martin Schoenball, Weiqiang Zhu, Gregory C. Beroza, Clifford Thurber

Research output: Contribution to journalArticlepeer-review

96 Scopus citations

Abstract

The important task of tracking seismic activity requires both sensitive detection and accurate earthquake location. Approximate earthquake locations can be estimated promptly and automatically; however, accurate locations depend on precise seismic phase picking, which is a laborious and time-consuming task. We adapted a deep neural network (DNN) phase picker trained on local seismic data to mesoscale hydraulic fracturing experiments. We designed a novel workflow, transfer learning-aided double-difference tomography, to overcome the 3 orders of magnitude difference in both spatial and temporal scales between our data and data used to train the original DNN. Only 3,500 seismograms (0.45% of the original DNN data) were needed to retrain the original DNN model successfully. The phase picks obtained with transfer-learned model are at least as accurate as the analyst's and lead to improved event locations. Moreover, the effort required for picking once the DNN is trained is a small fraction of the analyst's.

Original languageEnglish
Article numbere2020GL088651
JournalGeophysical Research Letters
Volume47
Issue number16
DOIs
StatePublished - Aug 28 2020

Funding

This material was based upon the work supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), Office of Technology Development, Geothermal Technologies Office, under Award DE-AC05-00OR22725. The U.S. Government retains, and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes. The research supporting this work took place in whole or in part at the Sanford Underground Research Facility in Lead, South Dakota. The assistance of the Sanford Underground Research Facility and its personnel in providing physical access and general logistical and technical support is acknowledged. We thank Haijiang Zhang for sharing the tomoDD package. We thank Yarom Polsky, Amir Ziabari, and Derek Rose for their helpful discussions. We acknowledge the comments and suggestions from Jacob Hinkle, Jeffrey Johnson, and Philip Bingham. The authors also thank the Editor Germán Prieto, the Associate Editor Victor Tsai, and two anonymous reviews for their positive and constructive comments and suggestions that helped improve the quality of this article. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Government. This material was based upon the work supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), Office of Technology Development, Geothermal Technologies Office, under Award DE‐AC05‐00OR22725. The U.S. Government retains, and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid‐up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes. The research supporting this work took place in whole or in part at the Sanford Underground Research Facility in Lead, South Dakota. The assistance of the Sanford Underground Research Facility and its personnel in providing physical access and general logistical and technical support is acknowledged. We thank Haijiang Zhang for sharing the tomoDD package. We thank Yarom Polsky, Amir Ziabari, and Derek Rose for their helpful discussions. We acknowledge the comments and suggestions from Jacob Hinkle, Jeffrey Johnson, and Philip Bingham. The authors also thank the Editor Germán Prieto, the Associate Editor Victor Tsai, and two anonymous reviews for their positive and constructive comments and suggestions that helped improve the quality of this article. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Government.

Keywords

  • geothermal
  • machine learning
  • neural network
  • seismic phase picking
  • seismic tomography
  • transfer learning

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