无需额外数据，用自一致性学习修复不规则地震数据。
An Efficient Self-supervised Seismic Data Reconstruction Method Based on Self-Consistency Learning
- 利用地震数据内部成分相关性设计损失函数
- 仅用18.9万参数网络实现高质量重建
- 适合大规模复杂地质勘探场景
地震勘探是地球物理中表征地下结构的关键方法。然而，复杂地表条件常导致测线上的地震接收点分布不均，造成采集数据不规则，影响后续处理与反演。现有基于深度学习的地震数据重建方法多依赖标注数据进行监督训练，尽管部分方法避免了额外数据需求，但缺乏对重建数据的有效约束，导致性能不稳定。本文提出一种轻量级网络结合自监督自一致性学习策略，无需额外数据，通过利用地震数据内部成分间的相关性设计损失函数，仅用188,849个可学习参数优化网络。在两个公开地震数据集上验证，结果表明该方法能实现高质量重建，为大规模、复杂地震勘探任务提供重要价值。
原文摘要 · Abstract (English)
Seismic exploration remains the most critical method for characterizing subsurface structures in geophysics. However, complex surface conditions often cause a non-uniform distribution of seismic receivers along survey lines, leading to irregularly acquired seismic data, which affects subsequent processing and inversion. Prior deep learning-based seismic data reconstruction methods typically rely on datasets for supervised training. While some existing methods avoid extra data, they lack effective constraints on reconstructed data, leading to unstable performance. In this study, we propose a self-supervised self-consistency learning strategy with a lightweight network for seismic data reconstruction. Our method requires no extra datasets, and it leverages inter-component correlations in seismic data to design a loss function, optimizing a network with only 188,849 learnable parameters. Validated on two public seismic datasets, results demonstrate our approach yields high-quality reconstruction, providing significant value for large-scale and complex seismic exploration tasks.
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