Seismic data reconstruction via local traveltime operators

Abstract Local traveltime operators effectively characterize the kinematic attributes of wavefronts and have demonstrated their ability to enhance seismic data quality by stacking traces along estimated wavefronts. Designed to fit local wavefronts even when input data are spatially sparse, these operators hold significant potential for reconstructing missing data directly along estimated wavefronts and addressing the data aliasing challenge associated with regularly undersampled seismic datasets. In this paper, we explore the possibility of using local traveltime operators to reconstruct prestack seismic data. Our approach estimates local traveltime operators through a global optimization solver to generate local wavefronts from spatially sparse seismic datasets. Missing traces are then reconstructed along these wavefronts using a linear–bilinear interpolation scheme. The validity of this method is illustrated through both synthetic and field data examples.