Abstract
Surface-related multiples prediction by inverse scattering series (ISS) is a completely data-driven method, which does not require a priori subsurface velocity information, and has the advantage of being suitable for complex structures compared with model-driven methods based on wave equations. But its computation and memory requirement are substantial. We propose a high-efficiency algorithm for the prediction of surface-related multiples by ISS in the curvelet domain. Firstly, we perform curvelet transform (CT) on shot gathers; secondly, we select few curvelet coefficient matrices for surface-related multiple prediction by ISS; thirdly, we perform inverse CT to the predicted multiples in the curvelet domain; lastly, we use matched filtering and subtraction to obtain primaries. Numerical experiments show that the proposed method not only improves computational efficiency and saves memory, but also achieves the same results compared with conventional ISS.
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This research is funded by the National Natural Science Foundation of China (grant no. 41630964). We appreciate the editors and reviewers for the critical and constructive comments, which significantly improved the quality of this paper.
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Zhang, B., Geng, J. High-Efficiency Prediction of Surface-Related Multiples by Inverse Scattering Series in the Curvelet Domain. Pure Appl. Geophys. 179, 2201–2214 (2022). https://doi.org/10.1007/s00024-022-03033-9
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DOI: https://doi.org/10.1007/s00024-022-03033-9