Repairing GSMP estimated multiples under coarse sampling using deep learning

D. Zhang, E. Verschuur

Research output: Contribution to conferencePaperpeer-review

Abstract

The data-driven surface-related multiple elimination (SRME)-type approach requires fully sampled sources and receivers during the multidimensional convolution process. Otherwise, the estimated multiples will be aliased. Compared to expensive reconstruction processes before prediction, dealiasing on the estimated multiples from limited sources might provide a potential easier solution in a 2D scenario, where deep learning (DL)-based methods suit well for this highly non-linear problem. Unfortunately, DL-based multiple dealising will not function well for 3D data due to extremely coarse sampling in either source or receiver side. Thus, data interpolation/reconstruction is the only option, though the performance might not be desired. Generalized surface multiple prediction (GSMP) is the most used on-the-fly interpolation approach in 3D. Still, GSMP accuracy heavily relies on the existing traces. When fed with coarsely sampled recorded data only, GSMP tends to generate multiples with low amplitude and distorted phase, especially for small offsets. We propose a U-Net framework to repair GSMP estimated multiples such that the amplitude loss and distorted phase can be restored. In this way, the strong non-linear mapping power from DL can help repair the GSMP estimated multiples.
Original languageEnglish
Number of pages5
DOIs
Publication statusPublished - 2024
Event85th EAGE Annual Conference & Exhibition 2024: Technology and talent for a secure and sustainable energy future - NOVA Spektrum Convention Centre, Oslo, Lillestrøm, Norway
Duration: 10 Jun 202413 Jun 2024
https://eageannual.org/eage-annual-2024

Conference

Conference85th EAGE Annual Conference & Exhibition 2024
Abbreviated titleEAGE Annual 2024
Country/TerritoryNorway
CityOslo, Lillestrøm
Period10/06/2413/06/24
Internet address

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.

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