标题： 自监督基于表面的相关多次波抑制与多维卷积 显示英文标题

标题： Self-supervised surface-related multiple suppression with multidimensional convolution

摘要： 表面相关的多次波在地震数据处理中构成了显著挑战，通常会掩盖一次反射并降低成像质量。传统方法依赖于计算成本高昂的算法、对地下模型的先验知识或准确的子波估计，而监督学习方法需要干净的标签，这在真实数据中是不切实际的。因此，我们提出了一种用于表面相关多次波抑制的自监督学习框架，利用多维卷积从观测数据中生成多次波，并采用两阶段训练策略，包括预热和迭代数据细化阶段，从而使网络学会去除多次波。该框架通过使用增强的多次波输入和伪标签来迭代地优化预测，从而消除了对标记数据的需求。数值实例表明，所提出的方法能有效地抑制表面相关的多次波，同时保留一次反射。迁移结果证实了其减少伪影和提高成像质量的能力。 显示英文摘要

摘要： Surface-related multiples pose significant challenges in seismic data processing, often obscuring primary reflections and reducing imaging quality. Traditional methods rely on computationally expensive algorithms, the prior knowledge of subsurface model, or accurate wavelet estimation, while supervised learning approaches require clean labels, which are impractical for real data. Thus, we propose a self-supervised learning framework for surface-related multiple suppression, leveraging multi-dimensional convolution to generate multiples from the observed data and a two-stage training strategy comprising a warm-up and an iterative data refinement stage, so the network learns to remove the multiples. The framework eliminates the need for labeled data by iteratively refining predictions using multiples augmented inputs and pseudo-labels. Numerical examples demonstrate that the proposed method effectively suppresses surface-related multiples while preserving primary reflections. Migration results confirm its ability to reduce artifacts and improve imaging quality.