Deep Learning for Deep Earthquakes in Oceans: Insights From Obs Observations of the Tonga Subduction Zone
Applications of machine learning in seismology have greatly improved our capability of detecting earthquakes in large seismic data archives. Most of these efforts have been focused on continental shallow earthquakes, but here we introduce an integrated deep-learning-based workflow to detect deep earthquakes recorded by a temporary array of ocean-bottom seismographs (OBSs) and land-based stations in the Tonga subduction zone. We develop a new phase picker, PhaseNet-TF, to detect and pick P-,S-, and PS-wave arrivals in the time-frequency domain. The frequency-domain information is critical for analyzing OBS data, particularly the horizontal components, because they are contaminated by signals of ocean-bottom currents and other noise sources in certain frequency bands. PhaseNet-TF shows a much better performance in picking S waves compared to its predecessor PhaseNet. The predicted phases are associated using an improved Gaussian Mixture Model Associator GaMMA-1D and then relocated with a double-difference package teletomoDD. We further enhance the model performance with a semi-supervised learning approach by iteratively refining labeled data and retraining PhaseNet-TF. This approach effectively suppresses false picks and significantly improves the detection of small earthquakes. The new catalog of Tonga deep earthquakes contains more than 10 times more events compared to the reference catalog that was analyzed manually. This deep-learning-enhanced catalog reveals Tonga seismicity in unprecedented detail, and better defines the lateral extent of the double-seismic zone at intermediate depths and the location of 4 large deep-focus earthquakes relative to background seismicity. The newly picked arrivals offer new potentials for deciphering deep earthquake mechanisms, refining tomographic models, and understanding subduction processes.
Session: Seismology in the Oceans: Pacific Hemisphere and Beyond - II
Type: Oral
Room: Tubughnenq’ 3
Date: 5/2/2024
Presentation Time: 11:15 AM (local time)
Presenting Author: Songqiao Wei
Student Presenter: No
Additional Authors
Songqiao Wei Presenting Author Corresponding Author swei@msu.edu Michigan State University |
Ziyi Xi xiziyi@msu.edu Michigan State University |
Weiqiang Zhu zhuwq@berkeley.edu University of California, Berkeley |
Gregory Beroza beroza@stanford.edu Stanford University |
Yaqi Jie jieyaqi@msu.edu Michigan State University |
Nooshin Saloor nooshinsaloor2014@u.northwestern.edu Michigan State University |
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Deep Learning for Deep Earthquakes in Oceans: Insights From Obs Observations of the Tonga Subduction Zone
Category
Seismology in the Oceans: Pacific Hemisphere and Beyond
Description