Physics-informed deep learning quantifies propagated uncertainty in seismic structure and hypocenter determination

DOI DOI PDF Web Site 研究データあり 被引用文献2件 参考文献89件 オープンアクセス

書誌事項

公開日
2025-01-13
資源種別
journal article
権利情報
  • https://creativecommons.org/licenses/by-nc-nd/4.0
  • https://creativecommons.org/licenses/by-nc-nd/4.0
DOI
  • 10.1038/s41598-024-84995-9
  • 10.48550/arxiv.2402.08854
公開者
Springer Science and Business Media LLC

説明

Subsurface seismic velocity structure is essential for earthquake source studies, including hypocenter determination. Conventional hypocenter determination methods ignore the inherent uncertainty in seismic velocity structure models, and the impact of this oversight has not been thoroughly investigated. Here, we address this issue by employing a physics-informed deep learning (PIDL) approach that quantifies uncertainty in seismic velocity structure modeling and its propagation to hypocenter determination by introducing neural network ensembles trained on active seismic survey data, earthquake observation data, and the physical equation of wavefront movement. An analysis of an earthquake in southwest Japan using our method revealed that accounting for such uncertainty propagation significantly reduced the bias and uncertainty underestimation in the hypocenter determination, enabling quantitative evaluation of the focal depth relative to the plate boundary. Our results highlight the potential of PIDL for various geophysical inverse problems, such as investigating earthquake source parameters, which inherently suffer from uncertainty propagation.

収録刊行物

  • Scientific Reports

    Scientific Reports 15 (1), 2025-01-13

    Springer Science and Business Media LLC

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