WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909416317517824 |
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| author | Yin, Ziyi Orozco, Rafael Herrmann, Felix J. |
| author_facet | Yin, Ziyi Orozco, Rafael Herrmann, Felix J. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_10327 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction Yin, Ziyi Orozco, Rafael Herrmann, Felix J. Geophysics Computational Engineering, Finance, and Science 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. |
| title | WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction |
| topic | Geophysics Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2405.10327 |