[2604.00307] SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior

Computer Science > Machine Learning arXiv:2604.00307 (cs) [Submitted on 31 Mar 2026] Title:SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior Authors:Huseyin Tuna Erdinc, Ipsita Bhar, Rafael Orozco, Thales Souza, Felix J. Herrmann View a PDF of the paper titled SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior, by Huseyin Tuna Erdinc and 4 other authors View PDF HTML (experimental) Abstract:Recent advances in generative networks have enabled new approaches to subsurface velocity model synthesis, offering a compelling alternative to traditional methods such as Full Waveform Inversion. However, these approaches predominantly rely on the availability of large-scale datasets of high-quality, geologically realistic subsurface velocity models, which are often difficult to obtain in practice. We introduce SAGE, a novel framework for statistically consistent proxy velocity generation from incomplete observations, specifically sparse well logs and migrated seismic images. During training, SAGE learns a proxy posterior over velocity models conditioned on both modalities (wells and seismic); at inference, it produces full-resolution velocity fields conditioned solely on migrated images, with well information implicitly encoded in the learned distribution. This enables the generation of geologically plausible and statistically accurate velocity realizations. We validate SAGE on both synthetic and field datasets, demonstrating its ability ...