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arXiv · 2203.01799

Coupling Deep Learning with Full Waveform Inversion

Wen Ding·Kui Ren·Lu Zhang
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Abstract

Full waveform inversion (FWI) aims to reconstruct unknown physical coefficients in wave equations using the wavefield data generated from multiple incoming sources. In this work, we propose an offline-online computational strategy for coupling classical least-squares-based computational inversion with modern learning-based approaches for FWI to achieve advantages that can not be achieved with only one of the components. \RED{In brief, we develop an offline learning strategy to construct a robust approximation of the inverse operator through weighted optimization and utilize it to design a new objective function for approximate online inversion with new datasets. The approximate online inversion then serves as a warm start for the true online inversion.} We demonstrate through numerical simulations that our coupling strategy improves the computational efficiency of FWI with reliable offline training on moderate computational resources (in terms of both the size of the training dataset and the computational cost needed).

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Wen Ding, Kui Ren, Lu Zhang. 2022-03-03. Coupling Deep Learning with Full Waveform Inversion. https://arxiv.org/abs/2203.01799

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