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