Non-surface-consistent short-wavelength statics correction for dense and subsampled data; A rankbased approach

Most short-wavelength statics-correction methods are based on the surface-consistency assumption depending on locations of sources and receivers at the surface. Even though this assumption may work in practice, it is not the most accurate solution since raypaths in the near-surface are not strictly vertical. Existing non-surface-consistent residual statics correction methods require constructing pilot traces or picking horizons, which make them prone to errors. We propose a low rank-based residual statics correction framework (LR-Res) that estimates non-surface-consistent statics from monochromatic frequency slices in the midpointoffset transform domain in an iterative and multi-scale fashion. When land seismic data contains gaps, an additional layer of complexity arises because interpolating the data suffers as residual statics break the data's continuity. To reconstruct densely-sampled data, we utilize ideas from our proposed LR-Res framework to jointly correct for short-wavelength statics and interpolate the data. We demonstrate the performance of our proposed methods in accurately correcting for non-surface-consistent, short-wavelength statics of densely sampled data, as well as reconstructing densely sampled data from subsampled data affected by short-wavelength statics, which we also compare with conventional methods.