Deblending of simulated simultaneous sources using an iterative approach: An experiment with variable-depth streamer data

Peng, Chao; Liu, Bin; Khalil, A.; Poole, Gordon

doi:10.1190/segam2013-0214.1

Cited by 21 publications

(10 citation statements)

References 2 publications

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“…Table 1. Quality of source separation Q S and performance I for the iterative projected gradient method for different projection operators: rank reduction (proposed method), f -x deconvolution (Peng and Liu, 2013), and f -k thresholding (Abma et al, 2010). These results correspond to the comparison tests portrayed in Figure 2.…”

Section: Examples Comparison Of Projection Operatorssupporting

confidence: 62%

“…where ν denotes iteration number and λ ½ν is the step size (Goldstein, 1964;Levitin and Polyak, 1966;Bertsekas, 1976). Similar iteration strategies have been used to deblend simultaneous sources by Cheng and Sacchi (2013), Peng and Liu (2013), and Chen et al (2014).…”

Section: Source Separation Via Projected Gradients Methodsmentioning

confidence: 99%

“…In addition to source separation via the proposed iterative rank reduction approach, we also test the iterative f-x deconvolution method and the iterative f-k thresholding algorithm. In other words, we replace the rank-reduction operator P in equation 14 by an f-x deconvolution operator (Peng and Liu, 2013) and by the f-k hardthresholding operator (Abma et al, 2010;Chen et al, 2014). Figure 2.…”

Section: Examples Comparison Of Projection Operatorsmentioning

confidence: 99%

“…For this reason, additional processing steps are often required to eliminate the interferences. One strategy entails converting source separation into an incoherent noise removal problem in common receiver gathers (Spitz et al, 2011;Ibrahim and Sacchi, 2013) or common offset gathers (Huo et al, 2009;Maraschini et al, 2012;Peng and Liu, 2013). For instance, one can adopt coherent pass operators including median filters, f-x prediction filters, and Radon transforms to suppress the incoherent interferences.…”

Section: Introductionmentioning

confidence: 99%

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Separation and reconstruction of simultaneous source data via iterative rank reduction

2015

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We have developed a rank-reduction algorithm based on singular spectrum analysis (SSA) that is capable of suppressing the interferences generated by simultaneous source acquisition. We evaluated an inversion scheme that minimizes the misfit between predicted and observed blended data in t-x domain subject to a low-rank constraint that is applied to data in the f-x domain. In particular, we developed an iterative algorithm by adopting the projected gradient method with the SSA filter acting as the projection operator. This method entails extracting small patches of data from a common receiver gather and organizing the spatial data at a given monochromatic frequency into a Hankel matrix. For the ideal unblended data, Hankel matrices extracted from the data are of low rank. The incoherent interferences in common-receiver domain caused by simultaneously fired shots increase the rank of the aforementioned Hankel matrices. Therefore, rank-reduction filtering is an effective way to annihilate source interferences while preserving the unblended signal. Through tests with synthetic examples, we found that the interference can be effectively suppressed by the proposed method. In addition, we found that the proposed algorithm can be modified to simultaneously cope with deblending and data recovery. A real survey acquired in the Gulf of Mexico was used to mimic a simultaneous-source acquisition with missing shot locations. The algorithm was able to recover the missing shot gathers from the blended acquisition with an improvement of the signal quality of approximately 12 dB.

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Section: Examples Comparison Of Projection Operatorssupporting

confidence: 62%

Section: Source Separation Via Projected Gradients Methodsmentioning

confidence: 99%

Section: Examples Comparison Of Projection Operatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Separation and reconstruction of simultaneous source data via iterative rank reduction

2015

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“…Various deblending methods have been developed to separate the interferences for simultaneous shooting data (Abma et al, 2010, Peng et al, 2013, Kontakis and Verschuur, 2014, Cheng and Sacchi, 2015, Haacke et al, 2015, Abma et al, 2015. Most of them make use of coherency enhancement in proper domains, iteratively modelling cross-talk noise and subtracting it from the data.…”

Section: Introductionmentioning

confidence: 99%

Advanced Deblending Scheme for Independent Simultaneous Source Data

Wang

Chen

et al. 2016

ASEG Extended Abstracts

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Independent Simultaneous Source (ISS®) technology is an attractive way to provide high acquisition production rates and affordable higher density seismic imaging. In this paper, we propose an advanced deblending scheme to address the source separation requirement of ISS® data. The deblending scheme includes a) 3D low-rank decomposition based iterative noise modelling, and subtraction, b) signal-to-noise ratio map guided residual denoise and c) erratic noise attenuation. The application is tested on actual shallow water OBC dataset, which was pseudo-blended using a realistic dual vessel acquisition plan. The result shows that this advanced deblending scheme can successfully recover individual source responses. The deblended data have a minimal difference compared with the actual unblended data.

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Iterative Deblending of Simultaneous-Source Seismic Data via a Robust Singular Spectrum Analysis Filter

Lin

Bahia

Sacchi

2022

IEEE Trans. Geosci. Remote Sensing

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Deblending of simulated simultaneous sources using an iterative approach: An experiment with variable-depth streamer data

Cited by 21 publications

References 2 publications

Separation and reconstruction of simultaneous source data via iterative rank reduction

Separation and reconstruction of simultaneous source data via iterative rank reduction

Advanced Deblending Scheme for Independent Simultaneous Source Data

Iterative Deblending of Simultaneous-Source Seismic Data via a Robust Singular Spectrum Analysis Filter

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