Seismic data interpolation using SVD free Pareto curve based low rank optimization

Seismic data acquisition is cursed by missing data caused by physical and/or budget constraints. Aim of interpolation technique is to spatially transform irregularly acquired data to regularly sampled data while maintaining the events coherency. While transform-domain sparsity promotion has proven to be an effective tool to solve this recovery problem, recent developments in Rank penalizing techniques opens new horizon to improved recovery by exploiting low-rank structure. A major downside of current state of the art techniques is their reliance on the SVD of seismic data structures, which can be prohibitively expensive. Fortunately, recent work allows us to circumvent this problem by working with matrix factorizations. We review a novel approach to rank penalization, and successfully apply it to the seismic interpolation problem by exploiting the low-rank structure of seismic data. Experiments for the recovery of 2D and 3D acquisition support the feasibility and potential of the new approach.