Data-driven control over short-period internal multiples in media with a horizontally layered overburden


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Date

2020-01-11

Publication Type

Journal Article

ETH Bibliography

yes

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Abstract

Short-period internal multiples, resulting from closely spaced interfaces, may interfere with their generating (bandlimited) primaries, and hence they pose a long-standing challenge in their prediction and removal. A recently proposed method based on the Marchenko equation enables removal of the entire overburden-related scattering by means of calculating an inverse transmission response. However, the method relies on time windowing and can thus be inexact in the presence of short-period internal scattering. In this work, we present a detailed analysis of the impact of band-limitation on the Marchenko method. We show the influence of an incorrect first guess, and that adding multidimensional energy conservation and a minimum phase principle may be used to correctly account for both long- and short-period internal multiple scattering. The proposed method can currently only be solved for media with a laterally invariant overburden, since a multidimensional minimum phase condition is not well understood for truly 2-D and 3-D media. We demonstrate the virtue of the proposed scheme with a complex acoustic numerical model that is based on sonic log measurements in the Middle East. The results suggest not only that the conventional scheme can be robust in this setting, but that the ‘augmented’ Marchenko method is superior, as the latter produces a structural image identical to one where the finely layered overburden is missing. This is the first demonstration of a data-driven method to account for short-period internal multiples beyond 1-D.

Publication status

published

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Volume

221 (2)

Pages / Article No.

769 - 787

Publisher

Oxford University Press

Event

Edition / version

Methods

Software

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Subject

Organisational unit

03953 - Robertsson, Johan / Robertsson, Johan check_circle

Notes

Funding

641943 - Waves and Wave-Based Imaging in Virtual and Experimental Environments (SBFI)

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