SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior

TL;DR

SAGE is a novel AI framework that generates realistic subsurface velocity models from limited seismic data, improving geological plausibility and supporting seismic inversion workflows.

Contribution

It introduces a proxy posterior learning approach that effectively synthesizes velocity models from sparse observations, advancing data-efficient seismic imaging methods.

Findings

Successfully validated on synthetic and real datasets.

Generates geologically plausible velocity realizations.

Supports downstream inversion workflows.

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 to capture complex subsurface variability under limited observational constraints. Furthermore, samples drawn from the learned proxy distribution can be leveraged to train downstream networks, supporting inversion workflows. Overall, SAGE provides a scalable and data-efficient pathway toward learning geological proxy posterior for seismic imaging and inversion. Repo link: https://github.com/slimgroup/SAGE.