Separation of Blended Data by Sparse Inversion Utilizing Surface-related Multiples

Abstract

SUMMARYBlended surveys have recently appeared in production environments. This underlines the need for processing tools that will either process the recorded data directly or perform the separation into single source data (deblending). An inversion technique for the separation of such data is described here. The problem parameterization utilises the surface-related multiples in order to regularise the inversion. In this way, the separation and surface-related multiple elimination are performed in one step. Also, the physical meaning of the model space is exploited during the inversion by formulating the problem as a Basis Pursuit Denoise problem. The method has been applied on a synthetic dataset and it produced promising results.

The utilization of the double focal transformation for sparse data representation and data reconstruction

“…Note that the robustness of this method against inaccuracies in the focal operators is not only utilized for seismic data reconstruction (Kutscha, Verschuur, and Berkhout ) but also for primary estimation (Lopez and Verschuur ) and deblending (Doulgeris, Verschuur, and Blacquiere ; Kontakis and Verschuur ).…”

The Double Focal Transformation and its Application to Seismic Data Reconstruction

“…Even though the focusing in the focal domain is not perfect, still a decent aliasing noise suppression is achieved, which leads to the desired data reconstruction and a small data residual (Figure 3.13e,f). Please note that the robustness of the double focal transformation against inaccuracies in the focal operators is not only utilised for seismic data reconstruction (Kutscha et al, 2010), but also for primary estimation (Lopez and Verschuur, 2013a) and deblending (Doulgeris et al, 2012). The observation that a good aliasing noise suppression is achievable even with an imprecise focal operators is actually not surprising if one considers that also reflector two and three are properly reconstructed.…”

“…In this form equation 7.2 can be used for multiple suppression. Since it has been demonstrated in Doulgeris et al (2012) that multiples are beneficial for the process of separating blended sources, it is reasonable to assume that the process of multiple separation also contributes to seismic data reconstruction. In this way we can consider equation 7.2 as an alternative implementation of the EPSI algorithm (Estimation of primaries by sparse inversion, van Groenestijn and Verschuur, 2009a,b).…”

3D primary estimation by sparse inversion using the focal domain parameterization

“…Lin andHerrmann (2010, 2011) showed that the primary impulse response can also be defined in the curvelet domain, rendering a more efficient parameterization. The L1 implementation of EPSI was used by Doulgeris et al (2012) to achieve joint multiple removal and deblending.…”