Point‐spread functions for interferometric imaging

Neut, Joost van der; Wapenaar, Kees

doi:10.1111/1365-2478.12221

Cited by 12 publications

(2 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…). Complex propagation in the overburden can blur the image (van der Neut and Wapenaar ; Thomson, Kitchenside and Fletcher ) additionally to the relatively weak sub‐salt reflections. Further image artefacts are potentially caused by multiple scattering due to the strong contrasts in the overburden.…”

Section: Introductionmentioning

confidence: 99%

Source wavelet correction for practical Marchenko imaging: A sub‐salt field‐data example from the Gulf of Mexico

Mildner

Broggini

Filho

et al. 2019

Geophysical Prospecting

View full text Add to dashboard Cite

Imaging a target zone below a salt body can be challenging because large velocity contrasts in the overburden between the salt and surrounding sediments generate internal multiples, which interfere with primary reflections from the target level in the imaging process. This can lead to an erroneous interpretation of reflections in the sub‐salt area if multiples are misinterpreted as primaries. The Marchenko redatuming method may enable imaging of the sub‐salt target area where the effect of the multiply‐scattering overburden is removed. This is achieved by creating a redatumed reflection response where virtual sources and receivers are located below the overburden using a macromodel of the velocity field and the surface reflection data. The accuracy of the redatumed data and the associated internal multiple removal, however, depends on the accurate knowledge of the source wavelet of the acquired reflection data. For the first time, we propose a method which can accurately and reliably correct the amplitudes of the reflection response in field data as required by the Marchenko method. Our method operates by iteratively and automatically updating the source function so as to cancel the most artefact energy in the focusing functions, which are also generated by the Marchenko method. We demonstrate the method on a synthetic dataset and successfully apply it to a field dataset acquired in a deep‐water salt environment in the Gulf of Mexico. After the successful source wavelet estimation for the field dataset, we create sub‐salt target‐oriented images with Marchenko redatumed data. Marchenko images using the proposed source wavelet estimation show clear improvements, such as increased continuity of reflectors, compared to surface‐based images and to conventional Marchenko images computed without the inverted source wavelet. Our improvements are corroborated by evidence in the literature and our own synthetic results.

show abstract

Section: Introductionmentioning

confidence: 99%

Source wavelet correction for practical Marchenko imaging: A sub‐salt field‐data example from the Gulf of Mexico

Mildner

Broggini

Filho

et al. 2019

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…The classical correlation integral representation (Wapenaar and Fokkema, 2006) is replaced by a convolution integral representation, which is subsequently inverted by multidimensional deconvolution (Wapenaar and van der Neut, 2010). The point-spread function plays a central role in this approach (Van der Neut and Wapenaar, 2015). Here we introduce a variant of this approach, leading to summation representations for the Marchenko method, including point-spread functions.…”

Section: Introductionmentioning

confidence: 99%

Representations for the Marchenko Method for imperfectly sampled data

Wapenaar

IJsseldijk

2019

SEG Technical Program Expanded Abstracts 2019

Self Cite

View full text Add to dashboard Cite

The Marchenko method is based on two integral representations for focusing functions and Green's functions. In practice the integrals are replaced by finite summations. This works well for regularly sampled data, but the quality of the results degrade in case of imperfect sampling. We reformulate the integral representations into summation representations which properly account for imperfectly sampled data and we illustrate these representations with numerical examples. We indicate how these representations may be used to modify the Marchenko method to account for imperfect sampling.

show abstract