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Probabilistic Inversion with Flow Matching

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Computer Science > Machine Learning

arXiv:2606.31288 (cs)
[Submitted on 30 Jun 2026]

Title:Probabilistic Inversion with Flow Matching

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Abstract:We demonstrate the application of Flow Matching, a technique originating from generative Artificial Intelligence, to probabilistic inversion in geophysical settings, such as seismic Full-Waveform inversion. We adapt the well-established mathematical theory of Flow Matching from generative Artificial Intelligence to the context of probabilistic inversion. We evaluate the approach with two case studies: a simple 2D velocity model to illustrate the general features of the method, and the OpenFWI dataset to show its capabilities for probabilistic inversion of more complex seismic velocity models.
Subjects: Machine Learning (cs.LG); Probability (math.PR); Geophysics (physics.geo-ph)
Cite as: arXiv:2606.31288 [cs.LG]
  (or arXiv:2606.31288v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2606.31288
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Baldur Paulwitz [view email]
[v1] Tue, 30 Jun 2026 08:04:17 UTC (18,290 KB)
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