Deep Learning for P-Wave First-Motion Polarity Determination and Its Application in Focal Mechanism Inversion-Reference-Cited by-同舟云学术

Deep Learning for P-Wave First-Motion Polarity Determination and Its Application in Focal Mechanism Inversion

Published:2024 Issue: Volume:62 Page:1-11
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Chen Yangkang¹^ORCID,Saad Omar M.²^ORCID,Savvaidis Alexandros¹^ORCID,Zhang Fangxue³,Chen Yunfeng³^ORCID,Huang Dino¹,Li Huijian⁴^ORCID,Aziz Zanjani Farzaneh⁵^ORCID

Affiliation:

1. Bureau of Economic Geology, The University of Texas at Austin, Austin, TX, USA

2. Seismology Department, National Research Institute of Astronomy and Geophysics (NRIAG), Helwan, Egypt

3. Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou, Zhejiang, China

4. College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

5. Department of Marine Geosciences, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA

Funder

Research funding granted to Texas Seismological Network Project and in part by starting funds from Zhejiang University

zhejiang university

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10542541.pdf?arnumber=10542541

Reference30 articles.

1. 85.12 FOCMEC: FOCal MEChanism determinations

2. A New Method for Determining First-Motion Focal Mechanisms

3. Using S/P Amplitude Ratios to Constrain the Focal Mechanisms of Small Earthquakes

4. Adjoint centroid-moment tensor inversions

5. Advancement in source estimation techniques using broadband regional seismograms