Least-Squares Gaussian Beam Transform for Deblending Distance-Separated Simultaneous Sources

Abstract

Simultaneous-source acquisition saves enormous acquisition costs and greatly enhances the quality of seismic data. However, it brings challenges to subsequent data processing due to the intense interferences of neighbor sources. Deblending is one of the methods to solve the problem of crosstalk noise. The deblending applied in the shot domain does not require the random scheduling that is used in the conventional simultaneous-source acquisition, so it has more flexibility. However, because the records of different sources show continuous traces in common shot gathers, they have the same characteristics and are difficult to separate directly. In this article, we use the least-squares Gaussian beam transform (LSGBT) to separate the useful seismic signals from crosstalk noise in the distance-separated simultaneous-sourcing (DSSS) survey and propose a novel deblending framework based on the LSGBT in the shot domain. Unlike most state-of-the-art Gaussian beam approaches that construct Gaussian beams in the frequency domain, the LSGBT constructs time-domain Gaussian beams, which can be considered as functions of amplitude, position, dip field, and so on. The essence of the proposed algorithm is that the blended data can be decomposed into the Gaussian beams that represent different dip-angle components. Thus, the single-source seismic records can be reconstructed in terms of a carefully selected combination of dip-oriented Gaussian beams. Two synthetic examples and one field data example show that the iterative deblending scheme based on LSGBT obtains better performance than the conventional frequency-wavenumber-based methods.