Virtual acoustics in inhomogeneous media with single-sided access

Wapenaar, Kees; Brackenhoff, Joeri; Thorbecke, Jan; Neut, Joost van der; Slob, Evert; Verschuur, Eric

doi:10.1038/s41598-018-20924-x

Cited by 18 publications

(18 citation statements)

References 44 publications

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“…The spatial dimensions have been scaled by the factor 20 000. A more detailed description of the physical modelling tank and the 3-D acquisition system can be found in Koek et al (1995), Blacquiere et al (1999) and Wapenaar et al (2018). The recording data from the acquisition line is selected as a 2-D experiment to test the performance of the MME scheme.…”

Section: E X a M P L Ementioning

confidence: 99%

Marchenko multiple elimination of a laboratory example

Zhang

Slob

2020

Geophysical Journal International

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SUMMARY The Marchenko multiple elimination (MME) scheme is derived from the coupled Marchenko equations. It is proposed for filtering primary reflections with two-way traveltime from the measured acoustic data. The measured acoustic reflection data are used as its own filter and no model information or adaptive subtraction is required to apply the method. The data obtained after MME are better suited for velocity model construction and artefact-free migration than the measured data. We apply the MME scheme to a measured laboratory data set to evaluate the success of the method. The results suggest that the MME scheme can be the appropriate choice when high-quality pre-processing is performed successfully.

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Section: E X a M P L Ementioning

confidence: 99%

Marchenko multiple elimination of a laboratory example

Zhang

Slob

2020

Geophysical Journal International

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“…A recent theoretical advance in this field is so-called 'Marchenko source-receiver redatuming'. These methods use receiver-redatumed Green's functions calculated using Marchenko methods to also redatum the source to a second arbitrary subsurface location (Wapenaar et al, 2016;Singh and Snieder, 2017;Wapenaar et al, 2018). In this article we propose that the receiver redatumed signals can be replaced with a measured VSP signal.…”

Section: Introductionmentioning

confidence: 99%

Imaging vertical structures using Marchenko methods with vertical seismic-profile data

2020

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Marchenko methods use seismic data acquired at or near the surface of the earth to estimate seismic signals as if the receiver (now a virtual receiver) was at an arbitrary point inside the subsurface of the earth. This process is called redatuming, and it is central to subsurface imaging. Marchenko methods estimate the multiply scattered components of these redatumed signals, which is not the case for most other redatuming techniques that are based on single-scattering assumptions. As a result, images created using Marchenko redatumed signals contain a reduction in the artifacts that usually contaminate migrated seismic images due to improper handling of internal multiples. We exploit recent theoretical advances that enable virtual sources and virtual receivers to be placed at arbitrary points inside the subsurface as a means to incorporate vertical seismic profile (VSP) data into Marchenko methods. The advantage of including this type of data is that the additional acquisition boundary increases subsurface illumination, which in turn enables vertical interfaces and steeply dipping structures to be imaged. We develop this methodology using two synthetic data sets. The first is created using a simple variable density but constant velocity subsurface model. In this example, we find that our newly devised VSP Marchenko imaging methodology enables imaging of horizontal and vertical structures and that optimal results are achieved by combining these images with those created using standard Marchenko imaging. A second example demonstrates that the method can be applied to more realistic subsurface structures, in this case a modified version of the Marmousi 2 model. We determine the applicability of the methods to image fault structures with the final imaging result containing reduced contamination due to internal multiples and an improvement in the imaging of fault structures when compared to other standard imaging methods alone.

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“…However, theory exists for Marchenko methods using elastic data (da Costa Filho et al, 2014, 2015Wapenaar, 2014; and data containing surface-related multiples (Singh et al, 2015(Singh et al, , 2017. There are also limited examples of applications to real data (Ravasi et al, 2016;Jia et al, 2018;Wapenaar et al, 2018). For an entirely nonmathematical introduction to Marchenko methods, we refer readers to van der Neut et al (2015c), or for an introduction to 1D Marchenko methods, see Cui et al (2018); for a thorough introduction to the more sophisticated mathematical aspects of the Marchenko methods, see Slob et al (2014b), ), or van der Neut et al (2015b.…”

Section: Introductionmentioning

confidence: 99%

An introduction to Marchenko methods for imaging

Lomas

Curtis

2019

GEOPHYSICS

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Geoscientists often have little information about earth’s subsurface heterogeneities prior to mapping them using seismic or other geophysical data. Marchenko methods are a set of novel, data-driven techniques that help us to project surface seismic data to points in the subsurface, to form seismograms as though they had been created at each point. In so doing, Marchenko methods account for many of the complex, multiply reflected seismic wave interactions that take place in the real earth’s subsurface. The resulting seismograms are the information required to create subsurface images that are more accurate than standard methods. Our aim is to introduce these concepts with the minimum amount of mathematics required to understand how the Marchenko method can iterate to a solution and to provide a well-commented, easily editable MATLAB code package for demonstration and training purposes. Green’s function estimation using the Marchenko method is first illustrated for a constant velocity, variable density, 1D medium, with results that indicate a near-perfect match when compared with true, synthetically modeled solutions. Similar quality results are shown for variable velocity, 2D Green’s function estimation. Finally, we determine how these estimates could be used to create images of the subsurface, which, when compared with standard methods, contain reduced contamination due to multiple-related artifacts. Our code package includes the 2D data set required to reconstruct the relevant figures that we present, and it allows for experimentation with the implementation of the Marchenko method and the application of Marchenko imaging.

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Virtual acoustics in inhomogeneous media with single-sided access

Cited by 18 publications

References 44 publications

Marchenko multiple elimination of a laboratory example

Marchenko multiple elimination of a laboratory example

Imaging vertical structures using Marchenko methods with vertical seismic-profile data

An introduction to Marchenko methods for imaging

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