Transfer Learning Enhanced Full Waveform Inversion
Transfer Learning Enhanced Full Waveform Inversion
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Publisher
Ithaca: Cornell University Library, arXiv.org
Journal title
Language
English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Scope and Contents
Contents
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 t...
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Full title
Transfer Learning Enhanced Full Waveform Inversion
Authors, Artists and Contributors
Author / Creator
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TN_cdi_proquest_journals_2779271684
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2779271684
Other Identifiers
E-ISSN
2331-8422
DOI
10.48550/arxiv.2302.11259
