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.

翻译：在海洋拖缆地震采集中，最近的水听器通常距离震源两百米，导致近偏移距道缺失，这降低了关键处理流程（如表面相关多次波消除、速度分析和全波形反演）的质量。现有的重建方法（如变换域插值）常产生运动学不一致性和振幅畸变，而监督式深度学习方法需要完整的近偏移距真实数据，这在现实采集场景中无法获得。为应对这些局限，我们提出了一种自监督扩散框架，无需近偏移距参考数据即可重建缺失的近偏移距道。该方法通过从可用远偏移距剖面中提取具有单道偏移的重叠块来训练条件扩散模型，从而学习控制事件曲率、振幅变化和小波特征的偏移距相关统计模式。在推理阶段，我们从最近记录偏移距向零偏移距逐道递归外推，逐步将学习到的先验信息从远偏移距传播至近偏移距。该生成式框架进一步通过集成采样提供不确定性估计，在缺乏验证数据的情况下量化预测置信度。在合成和现场数据集上的受控验证实验表明，相较于传统抛物线Radon变换基线方法，本方法取得了显著的性能提升。在实际近偏移距缺口上的操作部署证明了该方法在无法进行真实数据验证的场景下具有实用可行性。值得注意的是，尽管仅使用远偏移距观测数据进行训练，重建的波形仍保持了真实的振幅随偏移距变化趋势，且不确定性图能准确识别具有挑战性的外推区域。