日時
2023年1月23日(月)10:30 - 11:30 (JST)
講演者
  • 上田 修功 (理化学研究所 革新知能統合研究センター (AIP) 副センター長)
会場
  • via Zoom
言語
英語
ホスト
Tetsuo Hatsuda

The movement and deformation of the Earth’s crust and upper mantle provide critical insights into the evolution of earthquake processes and future earthquake potentials. Crustal deformation can be modeled by dislocation models that represent earthquake faults in the crust as defects in a continuum medium. In this study, we propose a physics-informed deep learning approach to model crustal deformation due to earthquakes. Neural networks can represent continuous displacement fields in arbitrary geometrical structures and mechanical properties of rocks by incorporating governing equations and boundary conditions into a loss function. The polar coordinate system is introduced to accurately model the displacement discontinuity on a fault as a boundary condition. We illustrate the validity and usefulness of this approach through example problems with strike-slip faults. This approach has a potential advantage over conventional approaches in that it could be straightforwardly extended to high dimensional, anelastic, nonlinear, and inverse problems.

Reference

  1. Tomohisa Okazaki, Takeo Ito, Kazuro Hirahara & Naonori Ueda, Physics-informed deep learning approach for modeling crustal deformation, Nature Communications, vol. 13, Article number: 7092 (2022), doi: 10.1038/s41467-022-34922-1

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