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.…
Document Type: | Conference Proceeding |
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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): | Urheberrechtlich geschützt |