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Ranking Competing geologic interpretations via foundation-model-assisted generative hydrologic inversion — arXiv:2609.20978

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.20978
License (as archived): CC0-1.0
Archived: 2026-10-10T01:44:32.738Z
SHA-256 of the archived text: 7b2fa3519aa5dcf6bb08d31d95856d8b41564d38fdbc0b54f7303db20fb7980f
Stable URL of this page: https://metraipolis.com/documento/dc_4cb216a76dfd

How to cite

Ranking Competing geologic interpretations via foundation-model-assisted generative hydrologic inversion — arXiv:2609.20978 — arXiv (en), https://arxiv.org/abs/2609.20978 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4cb216a76dfd (Metraipolis archive dc_4cb216a76dfd, sha256 7b2fa3519aa5dcf6bb08d31d95856d8b41564d38fdbc0b54f7303db20fb7980f)

BibTeX

@misc{dc4cb216a76dfd,
  title = {Ranking Competing geologic interpretations via foundation-model-assisted generative hydrologic inversion — arXiv:2609.20978},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.20978},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4cb216a76dfd; sha256 7b2fa3519aa5dcf6bb08d31d95856d8b41564d38fdbc0b54f7303db20fb7980f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.20978. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 modeling. For each interpretation, a text-to-image foundation model generates an ensemble of geologic images, and a separately trained variational autoencoder learns an interpretation-specific latent representation. A supervised inverse network maps head observations into this latent space, and the frozen decoder reconstructs an image that is mapped to a log-conductivity field. Steady-state flow simulations predict heads, and the aggregate normalized head error determines the ranking. We evaluate the framework using a synthetic benchmark based on the Johansen Formation with three interpretations of decreasing consistency with the reference geology. Across 595 test cases, the Precise \& Accurate interpretation produces lower normalized errors than Accurate in 58.5\% of cases and Mismatched in 82.5\% of cases. Accurate outperforms Mismatched in 65.5\% of cases. We then compare spatial representations of two published conceptual models of the Culebra Dolomite Member at the Waste Isolation Pilot Plant. The revised representation yields an aggregate normalized error of 7.598, compared with 8.595 for the original, consistent with the documented conceptual-model revision. The framework enables quantitative comparison of competing geological interpretations using available hydraulic observations.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-21 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.20978 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T01:14:16.597Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).