Interbed demultiple using Marchenko redatuming on 3D field data of the Santos basin

Staring, Myrna; Wapenaar, C.P.A.

doi:10.22564/16cisbgf2019.245

Cited by 8 publications

(6 citation statements)

References 4 publications

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“…The quality of the results depends on the input field data preprocessing. Authors as Peng et al (2019); Staring and Wapenaar (2019); and others, discuss the sensitivity of the Marchenko method due to deconvolution, sampling, acquisition geometry, and surface related multiples. Brackenhoff et al (2019) show in numerical examples the importance of the dense and regular sampling of sources and receivers in the Marchenko method.…”

Section: Introductionmentioning

confidence: 99%

Pre-processing methodology to apply the Marchenko method in conventional marine datasets

2021

Proceeding of the 17th International Congress of the Brazilian Geophysical

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Recently the application of the Marchenko method in marine field data has been of great interest in the oil industry. This method allows us to calculate the up-and downgoing Green's functions at a focal point in depth without artifacts coming from the overburden, and build images with high resolution without the influence of internal multiples. However, to run the Marchenko scheme correctly in field data it is necessary to fulfill the theoretical premises that this method requires in a pre-processing stage. The main premises are elevation statics, regularization, deconvolution, reciprocity, and removal of free-surface multiples. In this work, we present and discuss the steps elevation statics, interpolation, and reciprocity in order to successfully apply the Marchenko scheme in a marine seismic datasets.

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Section: Introductionmentioning

confidence: 99%

Pre-processing methodology to apply the Marchenko method in conventional marine datasets

2021

Proceeding of the 17th International Congress of the Brazilian Geophysical

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“…This requires regularly sampled and collocated sources and receivers in order to retrieve proper, uncontaminated responses. On the contrary, non-perfect geometries can have a significant effect on the Marchenko results Staring and Wapenaar, 2019). Most authors, therefore, assume ideal acquisition geometries when using the Marchenko method, avoiding the limitations arising from imperfect sampling.…”

Section: Introductionmentioning

confidence: 99%

Adaptation of the iterative Marchenko scheme for imperfectly sampled data

van IJsseldijk,

Wapenaar

2020

Preprint

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The Marchenko method retrieves the responses to virtual sources in the Earth's subsurface from reflection data at the surface, accounting for all orders of multiple reflections. The method is based on two integral representations for focusing-and Green's functions. In discretized form, these integrals are represented by finite summations over the acquisition geometry. Consequently, the method requires ideal geometries of regularly sampled and co-located sources and receivers. Recently new representations were derived, which handle imperfectly sampled data. These new representations use point-spread functions (PSFs) that reconstruct results as if they were acquired using a perfect geometry. Here, the iterative Marchenko scheme is adapted, using these new representations, to account for imperfect sampling. This new methodology is tested on a 2D numerical example. The results show clear improvement between the proposed scheme and the standard iterative scheme. By removing the requirement for perfect geometries, the Marchenko method can be more widely applied to field data.

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“…However, strict requirements on the acquisition geometry obstruct more wide-spread use of the method. Non-perfect geometries can significantly distort the results [4,9]. Therefore, most authors tacitly assume regularly sampled and collocated sources and receivers.…”

Section: Introductionmentioning

confidence: 99%

Correcting for imperfectly sampled data in the iterative Marchenko scheme

IJsseldijk¹,

Wapenaar²

2020

Preprint

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The Marchenko method retrieves the responses to virtual sources in the subsurface, accounting for all orders of multiples. The method is based on two integral representations for focusing and Green's functions. In discretized form these integrals are represented by finite summations over the acquisition geometry. Consequently, the method requires ideal geometries of regularly sampled and co-located sources and receivers. However, a recent study showed that this restriction can, in theory, be relaxed by deconvolving the irregularlysampled results with certain point spread functions (PSFs).The results are then reconstructed as if they were acquired using a perfect geometry. Here, the iterative Marchenko scheme is adapted in order to include these PSFs; thus, showing how imperfect sampling can be accounted for in practical situations. Next, the new methodology is tested on a 2D numerical example. The results show clear improvement between the proposed scheme and the standard iterative scheme. By removing the requirement for perfect geometries the Marchenko method can be more widely applied to field data.

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Interbed demultiple using Marchenko redatuming on 3D field data of the Santos basin

Cited by 8 publications

References 4 publications

Pre-processing methodology to apply the Marchenko method in conventional marine datasets

Pre-processing methodology to apply the Marchenko method in conventional marine datasets

Adaptation of the iterative Marchenko scheme for imperfectly sampled data

Correcting for imperfectly sampled data in the iterative Marchenko scheme

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