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Seismic demultiple with deep learning

  • Seismic data processing relies on multiples attenuation to improve inversion and interpretation. Radon-based algorithms are often used for multiples and primaries discrimination. Deep learning, based on convolutional neural networks (CNNs), has shown encouraging applications for demultiple that could mitigate Radon-based challenges. In this work, we investigate new strategies to train a CNN forSeismic data processing relies on multiples attenuation to improve inversion and interpretation. Radon-based algorithms are often used for multiples and primaries discrimination. Deep learning, based on convolutional neural networks (CNNs), has shown encouraging applications for demultiple that could mitigate Radon-based challenges. In this work, we investigate new strategies to train a CNN for multiples removal based on different loss functions. We propose combined primaries and multiples labels in the loss for training a CNN to predict primaries, multiples, or both simultaneously. Moreover, we investigate two distinctive training methods for all the strategies: UNet based on minimum absolute error (L1) training, and adversarial training (GAN-UNet). We test the trained models with the different strategies and methods on 400 synthetic data. We found that training to predict multiples, including the primaries …show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/8243
Bibliografische Angaben
Title (English):Seismic demultiple with deep learning
Conference:EAGE Annual Conference & Exhibition (84. : June 5-8, 2023 : Vienna, Austria)
Author:Mario Fernandez, Norman Ettrich, Matthias Delescluse, Alain Rabaute, 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
URL:https://www.earthdoc.org/content/papers/10.3997/2214-4609.2023101170
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Forschung / IMLA - Institute for Machine Learning and Analytics
Institutes:Bibliografie
Tag:Deep Leaning
Formale Angaben
Relevance:Konferenzbeitrag: h5-Index > 30
Open Access: Closed 
Licence (German):License LogoUrheberrechtlich geschützt