High Quality Separation of Simultaneous Sources by Sparse Inversion

Abma, Ray; Manning, Ted; Tanis, Mehmet C.; Yu, Jingbo; Foster, Mark

doi:10.3997/2214-4609.201400611

Cited by 64 publications

(38 citation statements)

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“…Many methods have been used to separate blended data (e.g. Lynn et al, 1987;Beasley et al, 1998;Ikelle, 2007;Akerberg et al, 2008;Dragoset et al, 2009;Krebs et al, 2009;Abma et al, 2010;Mahdad et al, 2011;Huo et al, 2012;Beasley et al, 2012;Wason and Herrmann, 2012). Two types of source blending scenarios commonly occur: (a) multiple boats fire simultaneously with shots from the different sources blended together, and (b) shots from the same source boat interfere with each other (self interference).…”

Section: Introductionmentioning

confidence: 98%

Joint source deblending and reconstruction for seismic data

Mosher

Morley

et al. 2013

SEG Technical Program Expanded Abstracts 2013

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The goal of simultaneous shooting is to acquire better seismic data more quickly at lower total cost. Effective source deblending techniques provide us with one of the tools for accomplishing this goal. The use of compressive sensing theory gives us another tool by helping to increase the effective spatial bandwidth of our acquired data. Seismic surveys designed to collect both optimally sampled and blended data can reduce acquisition costs and significantly improve image quality. In this paper, we consider a joint deblending and reconstruction problem using the framework of a synthesis-based basis pursuit denoising model. The combination of a "deblending" operator together with a "restriction" operator leads to a joint inversion in which the data are both deblended and reconstructed at regular sampling intervals. Our inversion model can be further constrained by down-weighting the evanescent portion of the wavefield. We illustrate our method using both synthetic and real data examples simulating continuous-time recording under ocean bottom node (OBN) settings.

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Section: Introductionmentioning

confidence: 98%

Joint source deblending and reconstruction for seismic data

Mosher

Morley

et al. 2013

SEG Technical Program Expanded Abstracts 2013

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Chen

et al. 2016

GEOPHYSICS

104

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We have designed a periodically varying code that can avoid the problem of the local coherency and make the interference distribute uniformly in a given range; hence, it was better at suppressing incoherent interference (blending noise) and preserving coherent useful signals compared with a random dithering code. We have also devised a new form of the iterative method to remove interference generated from the simultaneous source acquisition. In each iteration, we have estimated the interference using the blending operator following the proposed formula and then subtracted the interference from the pseudodeblended data.To further eliminate the incoherent interference and constrain the inversion, the data were then transformed to an auxiliary sparse domain for applying a thresholding operator. During the iterations, the threshold was decreased from the largest value to zero following an exponential function. The exponentially decreasing threshold aimed to gradually pass the deblended data to a more acceptable model subspace. Two numerically blended synthetic data sets and one numerically blended practical field data set from an ocean bottom cable were used to demonstrate the usefulness of our proposed method and the better performance of the periodically varying code over the traditional random dithering code.

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“…Note that this means that our method belongs to the class of inversion type of methods. Other examples of such methods are Abma and Yan (2009) and Abma et al (2010).…”

Section: General Frameworkmentioning

confidence: 99%

Separation of blended data by iterative estimation and subtraction of blending interference noise

2011

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Seismic acquisition is a trade-off between economy and quality. In conventional acquisition the time intervals between successive records are large enough to avoid interference in time.To obtain an efficient survey, the spatial source sampling is therefore often (too) large. However, in blending, or simultaneous acquisition, temporal overlap between shot records is allowed. This additional degree of freedom in survey design significantly improves the quality or the economics or both. Deblending is the procedure of recovering the data as if they were acquired in the conventional, unblended way. A simple least-squares procedure, however, does not remove the interference due to other sources, or blending noise. Fortunately, the character of this noise is different in different domains, e.g., it is coherent in the common source domain, but incoherent in the common receiver domain. This property is used to obtain a considerable improvement. We propose to estimate the blending noise and subtract it from the blended data. The estimate does not need to be perfect because our procedure is iterative. Starting with the least-squares deblended data, the estimate of the blending noise is obtained via the following steps: sort the data to a domain where the blending noise is incoherent; apply a noise suppression filter; apply a threshold to remove the remaining noise, ending up with (part of) the signal; compute an estimate of the blending noise from this signal. At each iteration, the threshold can be lowered and more of the signal is recovered. Promising results were obtained with a simple implementation of this method for both impulsive and vibratory sources. Undoubtedly, in the future algorithms will be developed for the direct processing of blended data. However, currently a highquality deblending procedure is an important step allowing the application of contemporary processing flows.

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High Quality Separation of Simultaneous Sources by Sparse Inversion

Cited by 64 publications

References 0 publications

Joint source deblending and reconstruction for seismic data

Joint source deblending and reconstruction for seismic data

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

Separation of blended data by iterative estimation and subtraction of blending interference noise

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