Elastic internal multiple analysis and attenuation using Marchenko and interferometric methods

Carlos Alberto Da Costa Filho, Giovanni Angelo Meles, Andrew Curtis

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Conventional seismic processing aims to create data that contain only primary reflections, whereas real seismic recordings also contain multiples. As such, it is desirable to predict, identify, and attenuate multiples in seismic data. This task is more difficult in elastic (solid) media because mode conversions create families of internal multiples not present in the acoustic case. We have developed a method to predict prestack internal multiples in general elastic media based on the Marchenko method and convolutional interferometry. It can be used to identify multiples directly in prestack data or migrated sections, as well as to attenuate internal multiples by adaptively subtracting them from the original data set. We developed the method on two synthetic data sets, the first composed of horizontal density layers and constant velocities, and the second containing horizontal and vertical density and velocity variations. The full-elastic method is computationally expensive and ideally uses data components that are not usually recorded. We therefore tested an acoustic approximation to the method on the synthetic elastic data from the second model and find that although the spatial resolution of the resulting image is reduced by this approximation, it provides images with relatively fewer artifacts. We conclude that in most cases where cost is a factor and we are willing to sacrifice some resolution, it may be sufficient to apply the acoustic version of this demultiple method.
Original languageEnglish
Pages (from-to)Q1-Q12
JournalGeophysics
Volume82
Issue number2
Early online date11 Jan 2017
DOIs
Publication statusPublished - 1 Mar 2017

Fingerprint

Dive into the research topics of 'Elastic internal multiple analysis and attenuation using Marchenko and interferometric methods'. Together they form a unique fingerprint.

Cite this