Using a Deep Neural Network and Transfer Learning to Bridge Scales for Seismic Phase Picking-Reference-Cited by-同舟云学术

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

Published:2020-08-17 Issue:16 Volume:47 Page:
ISSN:0094-8276
Container-title:Geophysical Research Letters
language:en
Short-container-title:Geophys. Res. Lett.

Author:

Chai Chengping¹^ORCID,Maceira Monica¹²^ORCID,Santos‐Villalobos Hector J.¹,Venkatakrishnan Singanallur V.¹,Schoenball Martin³^ORCID,Zhu Weiqiang⁴,Beroza Gregory C.⁴^ORCID,Thurber Clifford⁵^ORCID,

Affiliation:

1. Oak Ridge National Laboratory Oak Ridge TN USA

2. Department of Physics and Astronomy University of Tennessee, Knoxville Knoxville TN USA

3. Lawrence Berkeley National Laboratory Berkeley CA USA

4. Department of Geophysics Stanford University Stanford CA USA

5. Department of Geoscience University of Wisconsin‐Madison Madison WI USA

Funder

U.S. Department of Energy

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GL088651

Reference32 articles.

1. Akazawa T.(2004).A technique for automatic detection of onset time of P‐ and S‐phases in strong motion records. In13th World Conference on Earthquake Engineering(p. 786).

2. Automatic earthquake recognition and timing from single traces;Allen R. V.;Bulletin of the Seismological Society of America,1978

3. ObsPy: A Python Toolbox for Seismology

4. Estimating subglacial structure using P-wave receiver functions

5. Inverting interpolated receiver functions with surface wave dispersion and gravity: Application to the western U.S. and adjacent Canada and Mexico

Cited by 91 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning for real-time P-wave detection: A case study in Indonesia's earthquake early warning system;Applied Computing and Geosciences;2024-12

2. Deep transfer learning for P-wave arrival identification and automatic seismic source location in underground mines;International Journal of Rock Mechanics and Mining Sciences;2024-10

3. Combining deep neural network and spatio-temporal clustering to automatically assess rockburst and seismic hazard – Case study from Marcel coal mine in Upper Silesian Basin, Poland;Computers & Geosciences;2024-10

4. A comprehensive review of model compression techniques in machine learning;Applied Intelligence;2024-09-02

5. Application of sparse S transform network with knowledge distillation in seismic attenuation delineation;Petroleum Science;2024-08