从远到近传播先验：一种自监督扩散框架用于逐步恢复拖曳线阵数据的近偏移
Propagating the prior from far to near offset: A self-supervised diffusion framework for progressively recovering near-offsets of towed-streamer data
AI总结：
本文提出一种自监督扩散框架，通过递归外推从远到近恢复拖曳线阵数据的近偏移轨迹，无需地面真实数据，提升地震数据处理效果。
摘要：
在海洋拖曳线阵地震采集中，最近的水听器通常距离源点约200米，导致近偏移轨迹缺失，这会降级关键处理流程，如表面相关多重消除、速度分析和全波形反演。现有重建方法，如变换域插值，往往会产生运动学不一致性和振幅失真，而监督深度学习方法需要完整的地面真实近偏移数据，这些数据在实际采集场景中不可用。为了解决这些限制，我们提出了一种自监督扩散框架，可以在不需近偏移参考数据的情况下重建缺失的近偏移轨迹。我们的方法利用重叠补丁提取，通过从可用的远偏移部分中单轨迹位移来训练一个条件扩散模型，该模型学习控制事件曲率、振幅变化和波let特征的偏移依赖统计模式。在推理过程中，我们逐轨迹递归外推从最近记录的偏移向零偏移方向，逐步将学习到的先验信息从远偏移传播到近偏移。生成式公式进一步通过集合采样提供不确定性估计，在验证数据不存在时量化预测置信度。受控验证实验在合成和实地数据集上显示了比传统抛物线Radon变换基线显著的性能提升。在实际近偏移缺口上的操作部署展示了实用性，其中地面真实验证是不可能的。值得注意的是，重建的波形在仅训练于远偏移观测的情况下仍能保持真实的振幅-偏移趋势，且不确定性图准确识别了具有挑战性的外推区域。
英文摘要：
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.
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。
