WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction
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.
Cite
@article{arxiv.2405.10327,
title = {WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction},
author = {Ziyi Yin and Rafael Orozco and Felix J. Herrmann},
journal= {arXiv preprint arXiv:2405.10327},
year = {2024}
}