SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior

2026-03-31

AI Summary

This work addresses the challenge of generating geologically plausible and statistically consistent high-resolution velocity models from incomplete observations, such as sparse well logs and migrated seismic images. To this end, the authors propose SAGE, a multimodal generative framework that learns a surrogate posterior distribution of velocity models conditioned on both well data and seismic images. During inference, SAGE requires only the seismic image to produce full-resolution velocity fields, implicitly incorporating well information through the learned generative prior. SAGE is the first method capable of generating geologically realistic and statistically consistent velocity models using solely migrated seismic images. Validated on both synthetic and field datasets, it accurately captures complex subsurface structures and provides high-quality, diverse velocity realizations suitable for downstream inversion tasks.

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.

Problem

• subsurface velocity modeling
• incomplete observations
• geostatistical generation
• seismic imaging
• proxy posterior

Innovation

• proxy posterior
• velocity model generation
• generative networks
• seismic imaging
• data-efficient learning