AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
[Submitted on 17 Sep 2026] Title:Generative inversion for early ranking of competing geologic interpretations View a PDF of the paper titled Generative inversion for early ranking of competing geologic interpretations, by Harun Ur Rashid and 1 other authors View PDF HTML (experimental) Abstract:High-consequence subsurface decisions are often made under severe data scarcity. Experts may arrive at competing interpretations of the same subsurface system, yet early in a project there is rarely a practical way to determine which one is most realistic. This uncertainty can persist until several wells are drilled, often costing millions of dollars. Existing approaches for evaluating geologic interpretations rely either on subjective judgment or on dense data that are rarely available in early-stage investigations. We present a workflow that addresses this challenge by translating competing geologic interpretations into alternative spatial priors and ranking them according to their consistency with hydraulic-head observations. For each interpretation, a text-to-image foundation model generates an ensemble of 1600 geologic images, and a separately trained variational autoencoder provides an interpretation-specific latent representation. A supervised inverse network maps the head observations into this latent space, and the frozen decoder produces an image that is mapped to a log-conductivity field. Steady-state flow simulation then provides predicted heads, and the resulting mismatch is converted into a Gaussian-form compatibility score. We evaluate the framework using a synthetic benchmark based on the Johansen Formation and three interpretations of decreasing consistency with the reference representation. Across 925 test cases, the mean head RMSE increases from 0.197 for the Precise \& Accurate interpretation to 0.227 for the Accurate interpretation and 0.280 for the Mismatched interpretation. We subsequently apply the workflow to two published conceptual models of the Culebra Dolomite Member at the Waste Isolation Pilot Plant. The revised model receives a compatibility weight of 0.991, compared with 0.009 for the original model, consistent with the independent evidence. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.20978 [cs.LG] (or arXiv:2609.20978v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.20978 arXiv-issued DOI via DataCite (pending registration) Submission history From: Harun Ur Rashid [view email] [v1] Thu, 17 Sep 2026 18:31:52 UTC (4,224 KB) Full-text links: Access Paper: View a PDF of the paper titled Generative inversion for early ranking of competing geologic interpretations, by Harun Ur Rashid and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)