{"ID":5438707,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T07:34:59.203171219Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31288","arxiv_id":"2606.31288","title":"Probabilistic Inversion with Flow Matching","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.","short_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...","url_abs":"https://arxiv.org/abs/2606.31288","url_pdf":"https://arxiv.org/pdf/2606.31288v1","authors":"[\"Baldur Paulwitz\",\"Stefan Buske\"]","published":"2026-06-30T08:04:17Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"math.PR\",\"physics.geo-ph\"]","methods":"[]","has_code":false}