Accurate removal of surface-related multiples remains a challenge in shallow-water cases. One reason is that the success of the 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 — parabolic Radon transform (PRT) — fails to provide a 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 propose a novel workflow, which first deploys a deep-learning(DL)-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, which 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 dataset, which is hard to obtain, is not needed for training in the proposed workflow. An inversion-based approach — closed-loop surface-related multiple estimation (CL-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 the proposed workflow is demonstrated based on a 2D North Sea field data in a shallow-water scenario (92.5 m water depth) with a relatively large minimum offset (150 m).
Integrated receiver deghosting and closed-loop surface multiple elimination