Physics-informed deep learning approach for modeling crustal deformation.

ABSTRACT: 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 u

SUBMITTER: Okazaki T 

PROVIDER: S-EPMC9675849 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature