SAGE：基于代理后验的子表面人工智能驱动地质统计提取
SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior
AI总结：
SAGE通过稀疏井测和迁移地震图像生成地质合理的子表面速度模型，利用代理后验分布提高效率和准确性，适用于地震成像和反演。
摘要：
近期生成网络的进步为子表面速度模型合成提供了新方法，但传统方法如全波形反演仍占主导地位。本文提出SAGE框架，通过不完整观测生成统计一致的代理速度模型，利用井测和地震数据训练代理后验分布，在推理阶段仅需迁移地震图像即可生成高分辨率速度场。通过合成和实地数据验证，SAGE能有效捕捉复杂地下变异性，并支持后续网络训练。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
