Internal multiple attenuation for OBN data with overburden/target separation

Pereira, Roberto; Ramzy, Mena; Griscenco, Petre; Huard, Benjamin; Huang, Hui; Cypriano, Luis; Khalil, A.

doi:10.1190/segam2019-3215138.1

Cited by 14 publications

(12 citation statements)

References 11 publications

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“…The latter are then (indirectly) used in an Amundsen et al (2001)-like multidimensional deconvolution (MDD) to remove the effect of overburden-borne reverberations on the target primary reflections. Thus far, the Marchenko equation-based approach had been tested in settings where source signature estimation was not a limiting factor (Staring et al, 2018;Pereira et al, 2018Pereira et al, , 2019. Alternatively, given multicomponent data, Ravasi et al (2016) suggest deconvolving the upgoing and downgoing wavefields, which results in data free from (a) surface-related multiples and (b) the source signature.…”

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confidence: 99%

Robust estimation of primaries by sparse inversion and Marchenko equation-based workflow for multiple suppression in the case of a shallow water layer and a complex overburden: A 2D case study in the Arabian Gulf

Staring

Dukalski²,

Belonosov³

et al. 2021

GEOPHYSICS

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Suppression of surface-related and internal multiples is an outstanding challenge in seismic data processing. The former is particularly difficult in shallow water, whereas the latter is problematic for targets buried under complex, highly scattering overburdens. We propose a two-step, amplitude- and phase-preserving, inversion-based workflow, which addresses these problems. We apply Robust Estimation of Primaries by Sparse Inversion (R-EPSI) to suppress the surface-related multiples and solve for the source wavelet. A significant advantage of the inversion approach of the R-EPSI method is that it does not rely on an adaptive subtraction step that typically limits other de-multiple methods such as SRME. The resulting Green's function is used as input to a Marchenko equation-based approach to predict the complex interference pattern of all overburden-generated internal multiples at once, without a priori subsurface information. In theory, the interbed multiples can be predicted with correct amplitude and phase and, again, no adaptive filters are required. We illustrate this workflow by applying it on an Arabian Gulf field data example. It is crucial that all pre-processing steps are performed in an amplitude preserving way to restrict any impact on the accuracy of the multiple prediction. In practice, some minor inaccuracies in the processing flow may end up as prediction errors that need to be corrected for. Hence, we decided that the use of conservative adaptive filters is necessary to obtain the best results after interbed multiple removal. The obtained results show promising suppression of both surface-related and interbed multiples.

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mentioning

confidence: 99%

Robust estimation of primaries by sparse inversion and Marchenko equation-based workflow for multiple suppression in the case of a shallow water layer and a complex overburden: A 2D case study in the Arabian Gulf

Staring

Dukalski²,

Belonosov³

et al. 2021

GEOPHYSICS

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“…Since the sources and receivers stay at the surface, the output R ddr can be directly compared with the input data R, which is advantageous for quality control. Pereira et al (2019) apply a modified version of this method to suppress first order internal multiples from a 3D deep water OBN dataset. and Staring et al (2021) apply the method to shallow water numerical and field datasets, demonstrating the significance of including higher order terms to suppress internal multiples caused by a complex overburden.…”

Section: Retrieval Of Extrapolated Focusing Functionsmentioning

confidence: 99%

“…To make the Marchenko method less sensitive to the macro model, it was proposed to extrapolate the virtual sources and receivers upward to the acquisition surface van der Neut and Wapenaar, 2016). This led to a class of Marchenko multiple elimination methods, i.e., methods in which the sources and receivers stay at the surface while the internal multiples are eliminated from the data (Zhang et al, 2019a,b;Pereira et al, 2019;Elison et al, 2020;Dukalski and de Vos, 2020;Meles et al, 2020;Staring et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Marchenko redatuming, imaging, and multiple elimination and their mutual relations

Wapenaar

Brackenhoff

Dukalski³

et al. 2021

GEOPHYSICS

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With the Marchenko method it is possible to retrieve Green's functions between virtual sources in the subsurface and receivers at the surface from reflection data at the surface and focusing functions. A macro model of the subsurface is needed to estimate the first arrival; the internal multiples are retrieved entirely from the reflection data. The retrieved Green's functions form the input for redatuming by multidimensional deconvolution (MDD). The redatumed reflection response is free of internal multiples related to the overburden. Alternatively, the redatumed response can be obtained by applying a second focusing function to the retrieved Green's functions. This process is called Marchenko redatuming by double focusing. It is more stable and better suited for an adaptive implementation than Marchenko redatuming by MDD, but it does not eliminate the multiples between the target and the overburden. An attractive efficient alternative is plane-wave Marchenko redatuming, which retrieves the responses to a limited number of plane-wave sources at the redatuming level. In all cases, an image of the subsurface can be obtained from the redatumed data, free of artefacts caused by internal multiples. Another class of Marchenko methods aims at eliminating the internal multiples from the reflection data, while keeping the sources and receivers at the surface. A specific characteristic of this form of multiple elimination is that it predicts and subtracts all orders of internal multiples with the correct amplitude, without needing a macro subsurface model. Like Marchenko redatuming, Marchenko multiple elimination can be implemented as an MDD process, a double dereverberation process, or an efficient plane-wave oriented process. We systematically discuss the different approaches to Marchenko redatuming, imaging and multiple elimination, using a common mathematical framework.

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“…Wapenaar et al (2017) derive the homogeneous Green's function retrieval scheme from the Marchenko equations, where the homogeneous Green's function between any two points inside a medium can be retrieved from the measured single-sided reflection response. Ravasi et al (2016), Jia et al (2018), Staring et al (2018), Pereira et al (2019), andMildner et al (2019) apply the Marchenko method successfully on field data. Sripanich et al (2019) derive a method that can estimate initial focusing functions from data and does not rely on a velocity model for mildly varying media.…”

Section: Introductionmentioning

confidence: 99%

Implementation of the Marchenko multiple elimination algorithm

et al. 2021

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The Marchenko multiple elimination and transmission compensation schemes retrieve primary reflections in the two-way traveltime domain without model information or using adaptive subtraction. Both schemes are derived from projected Marchenko equations and similar to each other, but use different time-domain truncation operators. The Marchenko multiple elimination scheme retrieves a new dataset without internal multiple reflections. The transmission compensated Marchenko multiple elimination scheme does the same and additionally compensates for transmission losses in the primary reflections. Both schemes can be solved with an iterative algorithm based on a Neumann series. At each iteration, a convolution or correlation between the projected focusing function and the measured reflection response are performed and after each convolution or correlation, a truncation in the time domain is applied. After convergence, the resulting projected focusing function is used for retrieving the transmission compensated primary reflections and the projected Green’s function is used for the physical primary reflections. We demonstrate that internal multiples are removed by using time-windowed input data that only contain primary reflections. We evaluate both schemes in detail and develop an iterative implementation that reproduces the presented numerical examples. The software is part of our open-source suite of programs and fits into the Seismic Unix software suite of the Colorado School of Mines.

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Internal multiple attenuation for OBN data with overburden/target separation

Cited by 14 publications

References 11 publications

Robust estimation of primaries by sparse inversion and Marchenko equation-based workflow for multiple suppression in the case of a shallow water layer and a complex overburden: A 2D case study in the Arabian Gulf

Robust estimation of primaries by sparse inversion and Marchenko equation-based workflow for multiple suppression in the case of a shallow water layer and a complex overburden: A 2D case study in the Arabian Gulf

Marchenko redatuming, imaging, and multiple elimination and their mutual relations

Implementation of the Marchenko multiple elimination algorithm

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