A comparative study of multidimensional Fourier reconstruction of 2D prestack seismic data

Abstract

Multidimensional regularization and interpolation methods are an essential component of seismic data processing. Fourier reconstruction algorithms are efficient and straightforward to implement. Minimum Weighted Norm Interpolation (MWNI), Anti-Leakage Fourier Transform (ALFT), and Matching Pursuit Fourier Interpolation (MPFI) are examples of classical Fourier reconstructions techniques for prestack seismic data. MWNI estimates the Fourier coefficients that synthesize spatial data via a regularized inversion method. The MPFI and ALFT algorithms belong to the family of greedy algorithms, which determine Fourier coefficients iteratively. We provide an overview of the three Fourier based reconstruction algorithms mentioned above and compare them to access their strengths and weakness for processing prestack 2D land data. We conduct tests that highlight the capacity of Fourier techniques to reconstruct sparse seismic data and low-quality noisy land seismic data. We pay particular attention to adopting Fourier interpolation for data preconditioning before prestack time migration. We present a processing flow that can be utilized for new and vintage 2D onshore datasets, where preconditioning is essential to obtain high-quality migrated sections. In addition to synthetic examples, we present a real data study that corresponds to a vintage line from the Tacutu Basin, Northern Brazil, which was reprocessed and preconditioned with Fourier interpolation to improve prestack time migration images.