🤖 AI Summary
This study addresses the challenge of uncertainty quantification in geophysical probabilistic inversion by systematically introducing Flow Matching—a generative artificial intelligence technique—into full-waveform inversion for the first time. The method constructs a continuous transformation path from the prior to the posterior distribution of subsurface velocity models, parameterized by deep neural networks. Within a Bayesian framework, it leverages continuous normalizing flows to enable efficient posterior sampling. The approach demonstrates stable and accurate uncertainty estimation on both a 2D synthetic model and the complex OpenFWI benchmark dataset, significantly extending the applicability of Flow Matching to scientific computing and inverse problem solving.
📝 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.