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Ranking Competing geologic interpretations via foundation-model-assisted generative hydrologic inversion

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

High-consequence subsurface decisions often rely on sparse data that permit competing geological interpretations. Determining consistency of these interpretations with the available observations remains challenging. We present a workflow that addresses this challenge by translating competing geologic interpretations into alternative priors and ranking them according to their consistency with hydraulic-head observations. A key step in this workflow is exploiting the broad knowledge of image-generation foundation models to transform nuanced geologic interpretations into data ready for computer m

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