Deep learning-based dealiasing for estimated surface-related multiples from limited sources

The main prediction engine in surface-related multiple elimination (SRME) is the multidimensional convolution process, where data sampling plays an essential role for accurate surface multiple prediction. Therefore, fully sampled sources and receivers are preferred. If especially the source sampling is far from ideal, the estimated multiples will suffer from the severe aliasing effect. Consequently, this can lead to poorly estimated primaries. Interpolation of coarsely sampled sources is not a trivial task and computation intensive. Dealiasing on the estimated multiples from limited sources might provide a potential solution. In theory, this dealiasing problem is highly non-linear, which suits well for deep learning (DL)-based methods. Therefore, we propose a U-Net-based approach to dealiase the estimated surface multiples from limited sources. Applications on two subsets of the field data demonstrate the effective performance of the proposed method.