Fast Progressive Sparse Tau-P Transform for Regularization of Spatially Aliased Seismic Data

Abstract

Summary We present a new data regularization algorithm that combines a sparse Tau-P transform with low-rank optimization. Various issues, such as aliasing, efficiency and weak events, have been addressed to ensure the success of the algorithm. With both 3D synthetic and field data examples, we demonstrate that the technique is effective for regularization of strongly spatially-aliased seismic data while remaining cost effective.