Neural Eikonal solver: Improving accuracy of physics-informed neural networks for solving eikonal equation in case of caustics

Grubas, Serafim I.; Duchkov, Anton A.; Loginov, G.

doi:10.1016/j.jcp.2022.111789

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…PINNs serve as mesh-free numerical solvers that are applicable to complex geometries and exhibit promising performances in various scientific fields. In seismology, PINNs have been applied to travel time calculation [19][20][21] , wave propagation [22][23][24] , and crustal deformation 15 .…”

Section: Introductionmentioning

confidence: 99%

Fault Geometry Invariance for Physics-Informed Crustal Deformation Learning

Okazaki,

Hirahara,

Ueda

2023

Preprint

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Earthquake-induced crustal deformation provides valuable insights into the mechanisms of tectonic processes. Dislocation models offer a fundamental framework for comprehending such deformation, and two-dimensional antiplane dislocations are used to describe strike-slip faults. Previous earthquake deformation analyses observed that antiplane dislocations due to uniform fault slips are influenced predominantly by fault tips. In this study, we state a general principle of fault geometry invariance in antiplane dislocations and exploit its theoretical consequence to define dislocation potentials that enable a streamlined crustal deformation analysis. To demonstrate the benefits of this theory, we construct a rapid numerical solver for crustal deformation caused by variable fault slip scenarios using physics-informed neural networks, whose mesh-free property is suitable for modeling dislocation potentials. We anticipate that fault geometry invariance and the dislocation potential will further the analysis of antiplane crustal deformation, particularly for uncertainty quantification and inversion analysis regarding unknown fault geometries in realistic crustal structures.

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