Elimination of multiples from acoustic reflection data

Conference Paper (2019)
Author(s)

Evert Slob (TU Delft - Applied Geophysics and Petrophysics)

Lele Zhang (TU Delft - Applied Geophysics and Petrophysics)

Research Group
Applied Geophysics and Petrophysics
Copyright
© 2019 E.C. Slob, L. Zhang
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Publication Year
2019
Language
English
Copyright
© 2019 E.C. Slob, L. Zhang
Research Group
Applied Geophysics and Petrophysics
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
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Abstract

Elimination of multiples from acoustic reflection data is important to reduce the effect of their presence in velocity model building and subsequent imaging. Many processing schemes assume only primary reflection events are present in the data. Free-surface multiple elimination is an established technology, but internal multiple elimination is under development. We show that new data-driven processing methods have led to a robust multiple elimination scheme. This scheme removes free-surface and internal multiples contemporarily but can also eliminate internal multiples after free-surface multiples elimination. For each recording time instant, the method computes two filters using only the measured reflection response and an estimate of the source time signature. Once the filters are computed, they are used to filter the data up to that time instant. The result is that multiples related to reflectors with a two-way travel time less than the chosen time instant are removed from the data. This removes possible overlap with the primary reflection from the first deeper reflector. This event can be taken and stored in a new dataset. Repeating the procedure for all recording times produces the desired primaries only dataset. A numerical and a field data example show the effectiveness of the method.

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