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Physics-informed deep learning quantifies propagated uncertainty in seismic structure and hypocenter determination
Ryoichiro Agata1, Kazuya Shiraishi2, Gou Fujie2
1Japan Agency for Marine-Earth Science and Technology, 3173-25, Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, 2360001, Japan. agatar@jamstec.go.jp.
Scientific Reports
|January 13, 2025
Summary
This study introduces a physics-informed deep learning (PIDL) method to quantify seismic velocity uncertainty for more accurate earthquake hypocenter determination. Accounting for this uncertainty significantly improves earthquake source parameter analysis.
Area of Science:
- Geophysics
- Seismology
- Machine Learning
Background:
- Accurate subsurface seismic velocity structure is crucial for earthquake source studies, including hypocenter determination.
- Conventional methods often overlook the uncertainty in seismic velocity models, potentially impacting results.
Purpose of the Study:
- To develop and apply a physics-informed deep learning (PIDL) approach to quantify uncertainty in 2D seismic velocity models.
- To investigate the impact of this uncertainty propagation on hypocenter determination.
Main Methods:
- Utilized neural network ensembles trained on seismic survey data, earthquake observations, and wave propagation physics.
- Implemented a PIDL framework to model seismic velocity structure and quantify associated uncertainties.
Main Results:
- Applied to an earthquake in southwest Japan, the method significantly reduced bias and underestimation in hypocenter determination.
- Enabled quantitative evaluation of focal depth relative to the plate boundary by accounting for uncertainty propagation.
Conclusions:
- The PIDL approach effectively quantifies uncertainty in seismic velocity modeling and its impact on hypocenter determination.
- This methodology shows promise for improving geophysical inverse problems, including earthquake source parameter analysis.
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