A comparison of deep learning paradigms for seismic data interpolation
- Seismic data has often missing traces due to technical acquisition or economical constraints. A compete dataset is crucial in several processing and inversion techniques. Deep learning algorithms, based on convolutional neural networks (CNNs), have shown alternative solutions that overcome limitation of traditional interpolation methods e.g. data regularity, linearity assumption, etc. There areSeismic data has often missing traces due to technical acquisition or economical constraints. A compete dataset is crucial in several processing and inversion techniques. Deep learning algorithms, based on convolutional neural networks (CNNs), have shown alternative solutions that overcome limitation of traditional interpolation methods e.g. data regularity, linearity assumption, etc. There are two different paradigms of CNN methods for seismic interpolation. The first one, so-called deep prior interpolation (DPI), trains a CNN to map random noise to a complete seismic image using only the decimated image itself. The second one, referred as standard deep learning method, trains a CNN to map a decimated seismic image into a complete one using a dataset of complete and artificially decimated images. Within this research, we systematically compare the performance of both methods for different quantities of regular and irregular missing traces using 4 datasets. We evaluate the results of both methods using 5 well-known metrics. We found that DPI method performs better than the standard method if the percentage of missing traces is low (10%) and otherwise if the level of decimation is high (50%).…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/6450 | Bibliografische Angaben |
Title (English): | A comparison of deep learning paradigms for seismic data interpolation |
Conference: | EAGE Digitalization Conference and Exhibition (2. : March 23-25, 2022 : Vienna, Austria) |
Author: | Mario Fernadez, Ricard Durall Lopez, Norman Ettrich, Matthias Delescluse, Alain Rabaute, Janis KeuperStaff MemberORCiDGND |
Year of Publication: | 2022 |
Creating Corporation: | European Association of Geoscientists & Engineers |
First Page: | 1 |
Last Page: | 5 |
Parent Title (English): | Second EAGE Digitalization Conference and Exhibition |
Volume: | 2022 |
URL: | https://www.earthdoc.org/content/papers/10.3997/2214-4609.202239028 |
Language: | English | Inhaltliche Informationen |
Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
Institutes: | Bibliografie |
Tag: | Geophysik | Formale Angaben |
Relevance: | Konferenzbeitrag: h5-Index < 30 |
Open Access: | Closed |
Licence (German): | Urheberrechtlich geschützt |