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arXiv · 2405.10327

WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction

Ziyi Yin·Rafael Orozco·Felix J. Herrmann
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Abstract

We present a semi-amortized variational inference framework designed for computationally feasible uncertainty quantification in 2D full-waveform inversion to explore the multimodal posterior distribution without dimensionality reduction. The framework is called WISER, short for full-Waveform variational Inference via Subsurface Extensions with Refinements. WISER leverages the power of generative artificial intelligence to perform approximate amortized inference that is low-cost albeit showing an amortization gap. This gap is closed through non-amortized refinements that make frugal use of acoustic wave physics. Case studies illustrate that WISER is capable of full-resolution, computationally feasible, and reliable uncertainty estimates of velocity models and imaged reflectivities.

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Ziyi Yin, Rafael Orozco, Felix J. Herrmann. 2024-05-03. WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction. https://doi.org/10.1190/geo2024-0483.1

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