Learning velocity model for complex media with deep convolutional neural networks

Stankevich, A.; Nechepurenko, I.; Shevchenko, A.; Gremyachikh, L.; Ustyuzhanin, A.; Vasyukov, A.

doi:10.48550/arxiv.2110.08626

Cited by 1 publication

(1 citation statement)

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“…The obtained material profile was subsequently employed as the initial input for the subsequent FWI process. Stankevich et al 39 addressed the challenge of acquiring velocity models for complex media by employing CNNs. Their approach solely relies on boundary measurements, with a specific focus on the variations commonly encountered in seismic exploration.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network for identifying effective seismic force at a DRM layer for rapid reconstruction of SH ground motions

Maharjan,

Guidio,

Jeong

2023

Earthq Engng Struct Dyn

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We introduce a novel data‐informed convolutional neural network (CNN) approach that utilizes sparse ground motion measurements to accurately identify effective seismic forces in a truncated two‐dimensional (2D) domain. Namely, this paper presents the first prototype of a CNN capable of inferring domain reduction method (DRM) forces, equivalent to incident waves, across all nodes in the DRM layer. It achieves this from sparse measurement data in a multidimensional setting, even in the presence of incoherent incident waves. The method is applied to shear (SH) waves propagating into a domain truncated by a wave‐absorbing boundary condition (WABC). By effectively training the CNN using input‐layer features (surface sensor measurements) and output‐layer features (effective forces at a DRM layer), we achieve significant reductions in processing time compared to PDE‐constrained optimization methods. The numerical experiments demonstrate the method's effectiveness and robustness in identifying effective forces, equivalent to incoherent incident waves, at a DRM layer.

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Section: Introductionmentioning

confidence: 99%