arXiv:2606.31288v1 Announce Type: new 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 inv
The continuous evolution of generative AI techniques, like Flow Matching, is leading to new applications in traditionally complex scientific fields, driven by advancements in AI research from prior years.
This development allows for more accurate and efficient probabilistic inversion in geophysics, which is critical for resource exploration and environmental monitoring, potentially accelerating workflows and reducing costs.
The application of generative AI techniques simplifies complex inverse problems in scientific domains, potentially making them more accessible and less computationally intensive for researchers and industry practitioners.
- · Geophysical exploration firms
- · Generative AI researchers
- · Energy sector
- · Academic research institutions
- · Traditional inversion software providers (if slow to adapt)
- · Companies relying on less efficient inversion methods
More precise and faster geophysical models for resource discovery and environmental assessment.
Increased efficiency in oil and gas exploration, carbon capture site selection, and geothermal energy development.
Enhanced understanding of subsurface geology leading to fewer failed ventures and optimized infrastructure planning.
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Read at arXiv cs.LG