Journal ArticleDOI
Adaptive overburden elimination with the multidimensional Marchenko equation
Joost van der Neut,Kees Wapenaar +1 more
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TLDR
In this article, an adaptive substitution of the multidimensional Marchenko equation has been introduced to integrate internal multiple reflections in the seismic imaging process, without the need of a macro velocity model of the subsurface.Abstract:
Iterative substitution of the multidimensional Marchenko equation has been introduced recently to integrate internal multiple reflections in the seismic imaging process. In so-called Marchenko imaging, a macro velocity model of the subsurface is required to meet this objective. The model is used to back-propagate the data during the first iteration and to truncate integrals in time during all successive iterations. In case of an erroneous model, the image will be blurred (akin to conventional imaging) and artifacts may arise from inaccurate integral truncations. However, the scheme is still successful in removing artifacts from internal multiple reflections. Inspired by these observations, we rewrote the Marchenko equation, such that it can be applied early in a processing flow, without the need of a macro velocity model. Instead, we have required an estimate of the two-way traveltime surface of a selected horizon in the subsurface. We have introduced an approximation, such that adaptive subtracti...read more
Citations
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Journal ArticleDOI
Source-receiver Marchenko redatuming on field data using an adaptive double-focusing method
TL;DR: An adaptive double-focusing method that is specifically designed for the field-data application of source-receiver Marchenko redatuming and is less sensitive to imperfections in the data and a sparse acquisition geometry than the MDD method.
Journal ArticleDOI
Implementation of the Marchenko method
TL;DR: In this paper, the authors proposed an adaptive implementation of the Marchenko method based on a Neumann series, which is considered to be the conventional scheme and evaluated this algorithm in detail and developed an implementation that reproduces their examples.
Journal ArticleDOI
Transmission compensated primary reflection retrieval in the data domain and consequences for imaging
TL;DR: A scheme that retrieves primary reflections in the two-way traveltime domain by filtering the data is developed, and it is found that the presence of refracted and scattered waves generates artifacts in the retrieved data.
Journal ArticleDOI
Marchenko inversion in a strong scattering regime including surface-related multiples
Marcin Dukalski,Koos de Vos +1 more
Journal ArticleDOI
Marchenko scheme based internal multiple reflection elimination in acoustic wavefield
Lele Zhang,Myrna Staring +1 more
TL;DR: This paper applies a projected Marchenko scheme to a 2D numerical example to illustrate the procedure of this method and show how the internal multiple reflection eliminated data set can be retrieved and the migration image is improved.
References
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Journal ArticleDOI
An inverse-scattering series method for attenuating multiples in seismic reflection data
TL;DR: In this paper, a multidimensional multiple-attenuation method is presented that does not require any subsurface information for either surface or internal multiples. But it does not consider the relationship between forward and inverse scattering.
Book
Seismic applications of acoustic reciprocity
TL;DR: In this paper, the reciprocity theorem was chosen as the central theme of the seismic wave theory, and the seismic experiment was formulated as terme of a geological system response to a known source function.
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Wave‐equation datuming before stack
TL;DR: In this article, the authors describe the extension to unstacked seismic data of a computationally efficient form of the Kirchhoff integral published several years ago, and their applications include velocity-replacement datum corrections and multilayer forward modeling.
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Marchenko imagingMarchenko imaging
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Seismic reflector imaging using internal multiples with Marchenko-type equations
TL;DR: In this article, a filter is computed from the measured reflection response and does not require a background model, and the filter is a focusing wavefield that focuses inside a layered medium and removes all internal multiples between the surface and the focus depth.