[论文解读] Propagating the prior from far to near offset: A self-supervised diffusion framework for progressively recovering near-offsets of towed-streamer data

Shijun Cheng, Tariq Alkhalifah|arXiv (Cornell University)|Feb 2, 2026

Seismic Imaging and Inversion Techniques被引用 0

一句话总结

作者提出一个自监督扩散框架，通过从远偏移数据递归外推，重建拖曳音管道海上地震数据中缺失的近偏移道线，并通过集合采样提供不确定性估计。

ABSTRACT

In marine towed-streamer seismic acquisition, the nearest hydrophone is often two hundred meter away from the source resulting in missing near-offset traces, which degrades critical processing workflows such as surface-related multiple elimination, velocity analysis, and full-waveform inversion. Existing reconstruction methods, like transform-domain interpolation, often produce kinematic inconsistencies and amplitude distortions, while supervised deep learning approaches require complete ground-truth near-offset data that are unavailable in realistic acquisition scenarios. To address these limitations, we propose a self-supervised diffusion-based framework that reconstructs missing near-offset traces without requiring near-offset reference data. Our method leverages overlapping patch extraction with single-trace shifts from the available far-offset section to train a conditional diffusion model, which learns offset-dependent statistical patterns governing event curvature, amplitude variation, and wavelet characteristics. At inference, we perform trace-by-trace recursive extrapolation from the nearest recorded offset toward zero offset, progressively propagating learned prior information from far to near offsets. The generative formulation further provides uncertainty estimates via ensemble sampling, quantifying prediction confidence where validation data are absent. Controlled validation experiments on synthetic and field datasets show substantial performance gains over conventional parabolic Radon transform baselines. Operational deployment on actual near-offset gaps demonstrates practical viability where ground-truth validation is impossible. Notably, the reconstructed waveforms preserve realistic amplitude-versus-offset trends despite training exclusively on far-offset observations, and uncertainty maps accurately identify challenging extrapolation regions.

研究动机与目标

• 强调近偏移数据对于处理步骤的重要性，如 SRME、速度分析和 FWI。
• 开发一个不依赖近偏移地面真实数据来训练的自监督框架。
• 利用远偏移冗余学习偏移相关的地震事件模式。
• 实现从远到近逐道线的递归外推，同时保持运动学和振幅。

提出的方法

• 使用条件扩散模型，在给定条件补丁 y 的情况下，预测干净目标补丁 x0 来自嘈杂输入 xt。
• 用相邻的重叠补丁进行训练，偏移一个道以学习 p(x0|y)，无需近偏移地面真值。
• 在 DDPM 中采用 x0 预测以提升重建质量。
• 从最近记录的偏移量向零偏移逐道线进行递归推理，使用 DDIM 采样。
• 通过来自多次扩散 realizations 的集合采样提供不确定性估计。

实验结果

研究问题

• RQ1 自监督扩散模型是否能仅使用远偏移数据学习偏移相关的地震模式？
• RQ2 从远到近偏移的递归外推是否能在保留运动学和振幅方面接近真实地震数据？
• RQ3 来自扩散集合的不确定性映射是否能识别近偏移重建中具有挑战性的外推区域？
• RQ4 与基于变换的基线（如抛物线 Radon 变换）在合成数据和实测数据中的对比表现如何？

主要发现

• 该方法在合成数据上优于抛物线 Radon 变换基线的重建保真度。
• 重建的 F-K 谱接近真实谱，相较基线减少伪迹。
• 从远偏移到近偏移的道线波形保持良好振幅与相位一致性。
• 不确定性映射能有效识别外推难度更高的区域，帮助后续处理决策。
• 实测数据结果验证了在真实近偏移缺口场景下的实用性。