Estimation of primaries by sparse inversion using dual‐sensor data

Baardman, Rolf; Verschuur, D.J.; Borselen, R.G. van; Frijlink, Martijn; Hegge, Rob

doi:10.1190/1.3513569

Cited by 16 publications

(11 citation statements)

References 13 publications

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“…By applying reciprocity, a split-spread data set is obtained. Note that the same data set has also been used by Baardman et al (2010). In this data set, we have chosen sources to have a 1:5 irregular undersampling ratio in the sources.…”

Section: Coarsely Sampled Datamentioning

confidence: 99%

“…Although successful in many scenarios (Savels et al, 2011), the EPSI algorithm also brought some limitations. Due to the parameterization of the primary responses with spikes and wavelets, the algorithm showed difficulties in handling complexities in the subsurface such as wave dispersion, making refinements to the original algorithm a necessity (Baardman et al, 2010;Lin and Herrmann, 2011;Jumah and Herrmann, 2014). The challenges for EPSI were even larger in 3D, in which the coarse sampling required a method for combined multiple elimination and data interpolation.…”

Section: Introductionmentioning

confidence: 99%

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Closed-loop surface-related multiple elimination and its application to simultaneous data reconstruction

López

Verschuur

2015

GEOPHYSICS

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Surface-related multiple elimination (SRME) is one of the most commonly used methods for suppressing surface multiples. However, to obtain an accurate surface multiple estimation, dense source and receiver sampling is required. The traditional approach to this problem is performing data interpolation prior to multiple estimation. Although appropriate in many cases, this methodology fails when big data gaps are present or when relevant information is not recovered, e.g., near-offset data in shallow-water environments. We have developed a solution in which multiple estimation was performed simultaneously with data reconstruction, such that data reconstruction helped obtain better multiple estimates and in which the physical primary-multiple relationship helped constrain the data interpolation. To accomplish this, we proposed to extend the recently introduced closed-loop SRME (CL-SRME) algorithm to account for primary estimation in the case of coarsely sampled data. We achieved this by introducing a focal-domain parameterization of the primaries in a sparsity-promoting CL-SRME method. Results proved that the method was capable of reliably estimating primaries data in case of shallow water and with large undersampling factors.

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Section: Coarsely Sampled Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Closed-loop surface-related multiple elimination and its application to simultaneous data reconstruction

López

Verschuur

2015

GEOPHYSICS

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“…In practice, we also estimate Q before the EPSI operator is made. The effectiveness of EPSI has been repeatedly demonstrated (van Groenestijn and Verschuur, 2009b;Baardman et al, 2010) for the purpose of primary estimation and near-offset data reconstruction on real data. In this abstract, we take this success a step further by integrating EPSI into migration.…”

Section: Robust Epsimentioning

confidence: 99%

Migration with surface‐related multiples from incomplete seismic data

Lin

Herrmann

2011

SEG Technical Program Expanded Abstracts 2011

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SUMMARYSeismic acquisition is confined by limited aperture that leads to finite illumination, which, together with other factors, hinders imaging of subsurface objects in complex geological settings such as salt structures. Conventional processing, including surface-related multiple elimination, further reduces the amount of information we can get from seismic data. With the growing consensus that multiples carry valuable information that is missing from primaries, we are motivated to exploit the extra illumination provided by multiples to image the subsurface. In earlier research, we proposed such a method by combining primary estimation and sparsity-promoting migration to invert for model perturbations directly from the total up-going wavefield. In this abstract, we focus on a particular case. By exploiting the extra illumination from surface-related multiples, we mitigate the effects caused by migrating from incomplete data with missing sources and missing near-offsets.

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“…Despite the fact that EPSI is a relative new technique, there are already some successful applications (van Groenestijn and Verschuur, 2009b;Lin and Herrmann, 2010;Baardman et al, 2010) to primary estimation and near-offset data reconstruction.…”

Section: Mapping Wavefields Back To the Green's Function By Epsimentioning

confidence: 99%

Sparsity-promoting Migration with Surface-related Multiples

Tu¹,

Lin²,

Herrmann³

2011

73rd EAGE Conference and Exhibition Incorporating SPE EUROPEC 2011

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Multiples, especially the surface-related multiples, form a significant part of the total up-going wavefield. If not properly dealt with, they can lead to false reflectors in the final image. So conventionally practitioners remove them prior to migration. Recently research has revealed that multiples can actually provide extra illumination so different methods are proposed to address the issue that how to use multiples in seismic imaging, but with various kinds of limitations. In this abstract, we combine primary estimation and sparsity-promoting migration into one convex-optimization process to include information from multiples. Synthetic examples show that multiples do make active contributions to seismic migration. Also by this combination, we can benefit from better recoveries of the Green's function by using sparsity-promoting algorithms since reflectivity is sparser than the Green's function.

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Estimation of primaries by sparse inversion using dual‐sensor data

Cited by 16 publications

References 13 publications

Closed-loop surface-related multiple elimination and its application to simultaneous data reconstruction

Closed-loop surface-related multiple elimination and its application to simultaneous data reconstruction

Migration with surface‐related multiples from incomplete seismic data

Sparsity-promoting Migration with Surface-related Multiples

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