Accurate removal of surface-related multiples remains a challenge in shallow-water cases. One reason is that the success of surface-related multiple estimation (SRME)-related algorithms is sensitive to the quality of the near-offset reconstruction. When it comes to a larger missing gap and a shallower water bottom, the state-of-the-art near-offset gap construction method — the parabolic Radon transform — fails to provide reliable recovery of the shallow reflections due to the limited information from the data and highly curved events at near offsets with strong lateral amplitude variations. Therefore, we have developed a novel workflow that first deploys a deep-learning-based reconstruction of the shallow reflections and then uses the reconstructed data as the input for the subsequent surface multiple removal. In particular, we use a convolutional neural network architecture — U-net that was developed from convolutional autoencoders with extra direct skip connections between different levels of encoders and the corresponding decoders. Instead of using field data directly in network training, the training set is carefully synthesized based on the prior water-layer information of the field data; thus, a fully sampled field data set, which is difficult to obtain, is not needed for training in our workflow. An inversion-based approach — closed-loop SRME — is used for the surface multiple removal, in which the primaries are directly estimated via full-waveform inversion in a data-driven manner. Finally, the effectiveness of our workflow is determined based on 2D North Sea field data in a shallow-water scenario (92.5 m water depth) with a relatively large minimum offset (150 m).

中文翻译：

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仅使用合成数据训练深层网络：基于深度学习的近偏移量重构，用于对浅水域数据进行（闭环）与地面相关的多次估计

在浅水情况下，准确去除与表面相关的倍数仍然是一个挑战。原因之一是与表面相关的多重估计（SRME）相关算法的成功对近偏移重建的质量很敏感。当涉及到更大的缺失间隙和浅水底部时，由于来自反射波的有限信息，最新的近偏移间隙构造方法（抛物线Radon变换）无法提供可靠的浅反射恢复。数据和高度弯曲的事件在接近偏移处具有强烈的横向振幅变化。因此，我们开发了一种新颖的工作流程，该工作流程首先部署基于深度学习的浅反射重建，然后将重建的数据用作后续多次曲面去除的输入。特别是，我们使用了卷积神经网络体系结构—由卷积自动编码器开发的U-net，在不同级别的编码器和相应的解码器之间具有额外的直接跳过连接。代替直接在网络训练中使用现场数据，训练集是基于现场数据的先前水层信息精心合成的；因此，在我们的工作流程中进行培训不需要困难的完整采样现场数据集。基于反演的方法-闭环SRME-用于表面多重去除，其中通过数据驱动方式通过全波形反演直接估计原色。最后，我们的工作流程的有效性是根据浅水情况下的2D北海现场数据确定的（92。