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Deep Diffusion Models for Multiple Removal

  • Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing. These effects mainly comprise coherent artefacts such as multiples, non-coherent signals such as electrical noise, and loss of signal information at the receivers that leads to incomplete traces. In this work, we employ a generative solution, since it can explicitly modelSeismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing. These effects mainly comprise coherent artefacts such as multiples, non-coherent signals such as electrical noise, and loss of signal information at the receivers that leads to incomplete traces. In this work, we employ a generative solution, since it can explicitly model complex data distributions and hence, yield to a better decision-making process. In particular, we introduce diffusion models for multiple removal. To that end, we run experiments on synthetic and on real data, and we compare the deep diffusion performance with standard algorithms. We believe that our pioneer study not only demonstrates the capability of diffusion models, but also opens the door to future research to integrate generative models in seismic workflows.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/8242
Bibliografische Angaben
Title (English):Deep Diffusion Models for Multiple Removal
Conference:EAGE Annual Conference & Exhibition (84. : June 5-8, 2023 : Vienna, Austria)
Author:Ricard Durall Lopez, Ammar Ghanim, Mario Fernandez, Norman Ettrich, Janis KeuperStaff MemberORCiDGND
Year of Publication:2023
Creating Corporation:European Association of Geoscientists & Engineers
First Page:1
Last Page:5
Parent Title (English):84th EAGE Annual Conference & Exhibition
Volume:2023
URL:https://www.earthdoc.org/content/papers/10.3997/2214-4609.202310387
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Forschung / IMLA - Institute for Machine Learning and Analytics
Collections of the Offenburg University:Bibliografie
Tag:Deep Leaning
Formale Angaben
Relevance for "Jahresbericht über Forschungsleistungen":Konferenzbeitrag: h5-Index > 30
Open Access: Closed 
Licence (German):License LogoUrheberrechtlich geschützt