WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction
<|reference_start|>WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction: 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.<|reference_end|>
@article{yin2024wiser:,
    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},
    archivePrefix={arXiv},
    eprint={2405.10327},
    primaryClass={physics.geo-ph cs.CE}
}