Synthesizing seismic diffractions using a generative adversarial network
- Diffracted waves carry high resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. Because of the low signal-to-noise ratio of diffracted waves, it is challenging to preserve them during processing and to identify them in the final data. It is, therefore, a traditional approach to pick manually the diffractions. However, such taskDiffracted waves carry high resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. Because of the low signal-to-noise ratio of diffracted waves, it is challenging to preserve them during processing and to identify them in the final data. It is, therefore, a traditional approach to pick manually the diffractions. However, such task is tedious and often prohibitive, thus, current attention is given to domain adaptation. Those methods aim to transfer knowledge from a labeled domain to train the model, and then infer on the real unlabeled data. In this regard, it is common practice to create a synthetic labeled training dataset, followed by testing on unlabeled real data. Unfortunately, such procedure may fail due to the existing gap between the synthetic and the real distribution since quite often synthetic data oversimplifies the problem, and consequently the transfer learning becomes a hard and non-trivial procedure. Furthermore, deep neural networks are characterized by their high sensitivity towards cross-domain distribution shift. In this work, we present deep learning model that builds a bridge between both distributions creating a semi-synthetic datatset that fills in the gap between synthetic and real domains. More specifically, our proposal is a feed-forward, fully convolutional neural network for imageto-image translation that allows to insert synthetic diffractions while preserving the original reflection signal. A series of experiments validate that our approach produces convincing seismic data containing the desired synthetic diffractions.…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/4406 | Bibliografische Angaben |
Title (English): | Synthesizing seismic diffractions using a generative adversarial network |
Conference: | SEG 2020, 11-16 October 2020, Online |
Author: | Janis KeuperStaff MemberORCiDGND, Valentin Tschannen, Ricard Durall Lopez, Franz-Josef Pfreundt |
Year of Publication: | 2020 |
Creating Corporation: | Society of Exploration Geophysicists |
First Page: | 1491 |
Last Page: | 1495 |
Parent Title (English): | SEG Technical Program Expanded Abstracts 2020 |
ISSN: | 1052-3812 (Print) |
ISSN: | 1949-4645 (Online) |
DOI: | https://doi.org/10.1190/segam2020-3415521.1 |
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 | Formale Angaben |
Open Access: | Closed Access |
Licence (German): | Urheberrechtlich geschützt |