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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.show moreshow less

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Metadaten
Document Type:Conference Proceeding
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):License LogoUrheberrechtlich geschützt