Transfer Learning Enhanced Full Waveform Inversion

• URL: http://arxiv.org/abs/2302.11259v2
• Date: Fri, 1 Dec 2023 13:22:56 GMT
• Title: Transfer Learning Enhanced Full Waveform Inversion
• Authors: Stefan Kollmannsberger, Divya Singh and Leon Herrmann
• Abstract summary: 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.
• Score: 2.3020018305241337
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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