Print Email Facebook Twitter Repairing GSMP estimated multiples under coarse sampling using deep learning Title Repairing GSMP estimated multiples under coarse sampling using deep learning Author Zhang, D. (Fugro) Verschuur, D.J. (TU Delft Applied Geophysics and Petrophysics) Date 2024 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. To reference this document use: http://resolver.tudelft.nl/uuid:3310b10f-4743-4449-bc9c-90ef2a025ece DOI https://doi.org/10.3997/2214-4609.202410391 Embargo date 2024-12-10 Event 85th EAGE Annual Conference & Exhibition 2024, 2024-06-10 → 2024-06-13, NOVA Spektrum Convention Centre, Oslo, Lillestrøm, Norway 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. Part of collection Institutional Repository Document type conference paper Rights © 2024 D. Zhang, D.J. Verschuur Files file embargo until 2024-12-10