An iterative SVD method for deblending: theory and examples

Maraschini, Margherita; Dyer, Richard; Stevens, K.; Bird, Dave; King, Simon

doi:10.1190/segam2012-0675.1

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…They do, however, require sorting the elements of the solution into a tensor that exhibits the low‐rank property. A popular choice is to form a Hankel matrix out of monochromatic frequency slices of data in the frequency‐space domain (Cheng & Sacchi, 2013, 2015; Maraschini et al., 2012). Another approach is to sort the data based on mid‐point/half offset coordinates and construct a hierarchically semi‐separable matrix from frequency slices (Wason et al., 2014; Kumar et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Focal deblending: Marine data processing experiences

Kontakis

Verschuur

2023

Geophysical Prospecting

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In contrast to conventional acquisition practices, simultaneous source acquisition allows for overlapping wavefields to be recorded. Relaxing the shot schedule in this manner has certain advantages, such as allowing for faster acquisition and/or denser shot sampling. This flexibility usually comes at the cost of an extra step in the processing workflow, where the wavefields are deblended, that is, separated. An inversion‐type algorithm for deblending, based on the focal transform, is investigated. The focal transform uses an approximate velocity model to focus seismic data. The combination of focusing with sparsity constraints is used to suppress blending noise in the deblended wavefield.The focal transform can be defined in different ways to better match the spatial sampling of different types of marine surveys. To avoid solving a large inverse problem, involving a large part of the survey simultaneously, the input data can be split into sub‐sets that are processed independently. We discuss the formation of such sub‐sets for ocean bottom node and streamer‐type acquisitions. Two deblending experiments are then carried out. The first is on numerically blended ocean bottom node field data. The second is on field‐blended towed streamer data with a challenging signal overlap. The latter experiment is repeated using curvelet‐based deblending for comparison purposes, showing the virtues of the focal deblending process. Several challenges of basing deblending around the focal transform are discussed as well as some suggestions for improved implementations.

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