Towards 3D near-surface correction without NMO: A rank-based approach

To avoid multiple iterations of normal moveout (NMO) velocity estimation followed by short-wavelength statics estimation usually performed on land data, and to also improve the accuracy and computational efficiency of the latter, a low-rank-based residuals statics (LR-ReS) estimation and correction framework has been recently proposed. The method iteratively promotes the low-rank structure in the midpoint-offset-frequency domain of 2D data as statics-free data can be approximated by low-rank matrices, while data influenced by the weathering layers exhibits slow singular values decay. For 3D data, there exist different options to organize it into 2D matrices to be able to compute the singular value decomposition (SVD) required for low-rank approximation. It is also essential to find an organization that reveals the rank structure. We examine the different organization options. Based on finding a suitable sorting domain, we extend the LR-ReS estimation and correction to 3D data. We demonstrate the performance of the method on simulated data and will show field data results during the presentation.