Transfer Learning Enhanced Full Waveform Inversion

Stefan Kollmannsberger, Divya Singh, Leon Herrmann
2/22/2023
cs.LGphysics.comp-phphysics.geo-ph

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Abstract

We propose a way to favorably employ neural networks in the field of non-destructive testing using Full Waveform Inversion (FWI). The presented methodology discretizes the unknown material distribution in the domain with a neural network within an adjoint optimization. To further increase efficiency of the FWI, pretrained neural networks are used to provide a good starting point for the inversion. This reduces the number of iterations in the Full Waveform Inversion for specific, yet generalizable settings.

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Cite this paper

@article{kollmannsberger2023transfer,
  title  = {Transfer Learning Enhanced Full Waveform Inversion},
  author = {Stefan Kollmannsberger and Divya Singh and Leon Herrmann},
  year   = {2023},
  eprint = {2302.11259},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.11259v2}
}

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