自注意力摘要网络用于从共成像点道集构建地下速度模型

中文摘要 AI 辅助

共成像点道集（CIGs）通过反射体聚焦和剩余动校正包含关于速度模型误差的物理上有意义的信息，但在常规成像工作流程中，它们通常仅用作诊断工具。在这项工作中，我们提出了一种多尺度自注意力摘要网络，将高维3D CIG体映射为紧凑的条件嵌入，用于概率性地下速度反演。这些学习到的嵌入保留了偏移相关的运动学结构和空间相干性，同时减少了由背景速度不匹配引起的变异性。以这些摘要嵌入为条件，一个流匹配模型学习从高斯源分布到合理速度场后验分布的传输。数值实验表明，与直接以原始CIGs为条件相比，所提出的摘要网络改善了后验速度推断。特别是，多尺度注意力设计对背景模型不匹配提供了更大的鲁棒性，产生了更准确的后验重建和更低的预测不确定性。

英文摘要

Common-image gathers (CIGs) contain physically meaningful information about velocity-model errors through reflector focusing and residual moveout, but in conventional imaging workflows they are typically used only as diagnostic tools. In this work, we propose a multiscale self-attention summary network that maps high-dimensional 3D CIG volumes into compact conditioning embeddings for probabilistic subsurface velocity inversion. These learned embeddings preserve offset-dependent kinematic structure and spatial coherence while reducing variability caused by background-velocity mismatch. Conditioned on these summary embeddings, a flow-matching model learns a transport from a Gaussian source distribution to the posterior distribution of plausible velocity fields. Numerical experiments show that, compared with direct conditioning on raw CIGs, the proposed summary network improves posterior velocity inference. In particular, the multiscale attention design provides greater robustness to background-model mismatch, yielding more accurate posterior reconstructions and lower predictive uncertainty.

发表机构

• Georgia Institute of Technology（佐治亚理工学院）

机构由 AI 辅助整理，请以论文原文为准。