Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization

Abstract

We used a novel iterative estimation scheme for separation of blended seismic data from simultaneous sources. The scheme is based on an augmented estimation problem that can be solved by iteratively constraining the deblended data using shaping regularization in the seislet domain. We formulated the forward modeling operator in the commonreceiver domain, in which two sources were assumed to be blended using a random time-shift dithering approach. The nonlinear shaping-regularization framework offered some freedom in designing a shaping operator to constrain the model in an underdetermined inverse problem. We designed the backward operator and the shaping operator for the shaping-regularization framework. The backward operator can be optimally chosen as half of the identity operator in the two-source case, and the shaping operator can be chosen as coherency-promoting operator. The high performance deblending effect of the iterative framework was tested on three numerically blended synthetic data sets and one numerically blended field data set. Compared with alternative f-k domain thresholding and f-x predictive filtering, seislet-domain soft thresholding exhibits the most robust behavior.

A periodically varying code for improving deblending of simultaneous sources in marine acquisition

“…The proposed iterative blending noise estimation and subtraction strategy was also used by Mahdad et al (2011), but with a slightly different implementation formulation. The coherency-promoting filter was also called the shaping operator in Fomel (2007) and Chen et al (2014a). There are two main differences between our approach and the published methods for simultaneous sources separation.…”

“…Inversion methods treat the separation problem as an estimation problem that aims at estimating the desired unknown unblended data by iteratively inverting the forward blending equation. Because of the illposed property of such estimation problems, a regularization term is usually required (Doulgeris et al, 2012;Chen et al, 2014a;Qu et al, 2014) to obtain a well-behaved and stable model estimation. According to the regularization constraints, there are several different iterative approaches.…”

“…According to the regularization constraints, there are several different iterative approaches. Most of the successful regularizations (Akerberg et al, 2008;Abma et al, 2010;Chen et al, 2014a;Cheng and Sacchi, 2015) are based on the sparsity constraint in some sparse transform domains. Compared with the filtering methods, deblending via inversion methods usually leads to better separation results (van Borselen et al, 2012).…”

An open-source Matlab code package for improved rank-reduction 3D seismic data denoising and reconstruction

“…However, the predictive filtering method can only be applied to regularly sampled seismic data. The second type is a transformed domain method (Candès et al, 2006a;Chen et al, 2014b;Gan et al, 2015b;Liu et al, 2015), which is based on compressive sensing theory (Candès et al, 2006b;Donoho, 2006) to achieve a successful recovery using highly incomplete available data (Sacchi et al, 1998;Wang, 2003;Chen et al, 2014a). Compressive sensing (CS) is a relatively new paradigm (Candès et al, 2006b;Donoho, 2006;Donoho et al, 2006;Gan et al, 2016;Liu et al, 2016) in signal processing that has recently received a lot of attention.…”

Enhancing seismic reflections using empirical mode decomposition in the flattened domain

“…Modern exploration for hydrocarbon requires more advanced techniques for each step of seismic acquisition, processing, and interpretation (Chen et al, 2014a;Qu et al, 2015). The key step that connects geophysics and geology is interpretation.…”