Deblending of continuously recorded OBN data by subtraction integrated with a median filter

Zhan, Cheng; Malik, Raheel; Specht, Jim; Liu, Zhaojun; Teixeira, Davide

doi:10.1190/segam2015-5827990.1

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…The denoising step takes advantage of the incoherence of the blending noise to remove it. Examples of denoising methods used include median filtering (Chen, 2014; Gan et al., 2015; Huo et al., 2012; Y. Liu et al., 2009; Zhang et al., 2013; Z. Liu et al., 2014; Zhan et al., 2015), median filtering after the normal moveout (NMO) correction (Baardman & van Borselen, 2012; Chen et al., 2015), filtering in the wavelet domain (Yu et al., 2017) and using prediction‐error filters (Spitz et al., 2008). Recently, deep learning methods have been proposed to tackle the denoising part.…”

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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Section: Introductionmentioning

confidence: 99%

Focal deblending: Marine data processing experiences

Kontakis

Verschuur

2023

Geophysical Prospecting

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“…The flexibility in defining a coherency metric has led to a variety of approaches to deblending. Examples include coherency-based FK filtering (Mahdad et al, 2011), median-based filtering (Gan et al, 2015;Zhan et al, 2015), sparsity-based methods using Radon transforms (Ibrahim and Sacchi, 2013;Haacke et al, 2015), curvelets (Lin and Herrmann, 2009;Wason et al, 2011) and seislets (Chen, 2015). Another approach is to use rank-reduction techniques (Wason et al, 2014;Cheng and Sacchi, 2015).…”

Section: Introductionmentioning

confidence: 99%

Focal Deblending Using Smart Subsets of OBN 5D Data

Kontakis

Verschuur

2017

Proceedings

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SummaryAlthough theoretically straightforward, adapting focal deblending to realistic 5D acquisition scenarios can be challenging in practice. The two main issues that have to be dealt with are insufficiently sampled spatial dimensions and the computational effort needed for the deblending inversion. In order to deal with both issues, we propose dividing the data in 'smart' subsets, specialized for the acquisition type. Then, the deblending problem can be divided into a number of smaller problems that can be solved independently. The focal transform used for the deblending is also redefined to fit the geometry of the subsets. We examine the case of OBN acquisition and test the performance of the proposed scheme on numerically blended field data.

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“…There is a wealth of methods that have been proposed to attack the problem. These range from coherency-based FK filtering (Mahdad et al, 2011), median-based filtering (Huo et al, 2012;Gan et al, 2015;Zhan et al, 2015), to sparsity-based methods using Radon transforms (Ayeni et al, 2011;Haacke et al, 2015;Ibrahim and Sacchi, 2013), curvelets (Lin and Herrmann, 2009;Wason et al, 2011) and seislets (Chen, 2015). Another approach is to use rank-reduction techniques (Wason et al, 2014;Cheng and Sacchi, 2015), exploiting the fact that blending increases the rank of certain data subsets.…”

Section: Introductionmentioning

confidence: 99%

Acquisition Geometry-aware Focal Deblending

Kontakis¹,

Wu²,

Verschuur³

2016

78th EAGE Conference and Exhibition 2016

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SUMMARYThe applicability of a deblending method is directly related to acquisition parameters, such as source and detector locations. We formulate focal deblending in two alternative ways. In the first case, the double focal transform is used, which relies on a well-sampled source and detector dimension. In the second case, the single-sided focal transform is used, which does not depend on a well-sampled source dimension. Comparing the deblending results, we find that although the double focal transform is superior, blending noise can be significantly attenuated using the single-sided focal transform, which allows application to more practical acquisition geometries. By combining with shot repetition, the deblending result can be further improved.

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Deblending of continuously recorded OBN data by subtraction integrated with a median filter

Cited by 8 publications

References 12 publications

Focal deblending: Marine data processing experiences

Focal deblending: Marine data processing experiences

Focal Deblending Using Smart Subsets of OBN 5D Data

Acquisition Geometry-aware Focal Deblending

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