{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T20:08:52Z","timestamp":1774382932311,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T00:00:00Z","timestamp":1672617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Science and Technology of Sinopec","award":["P21071-3"],"award-info":[{"award-number":["P21071-3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep-learning-based seismic data interpretation has received extensive attention and focus in recent years. Research has shown that training data play a key role in the process of intelligent seismic interpretation. At present, the main methods used to obtain training data are synthesizing seismic data and manually labeling the real data. However, synthetic data have certain feature differences from real data, and the manual labeling of data is time-consuming and subjective. These factors limit the application of deep learning algorithms in seismic data interpretation. To obtain realistic seismic training data, we propose label-to-data networks based on cycle-consistent adversarial networks in this work. These networks take random labels and unlabeled real seismic data as input and generate synthetic seismic data that match the random labels and have similar features to the real seismic data. Quantitative analysis of the generated data demonstrate the effectiveness of the proposed methods. Meanwhile, test results on different data indicate that the generated data are reliable and can be applied for seismic fault detection.<\/jats:p>","DOI":"10.3390\/rs15010265","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T04:17:38Z","timestamp":1672633058000},"page":"265","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Generating Paired Seismic Training Data with Cycle-Consistent Adversarial Networks"],"prefix":"10.3390","volume":"15","author":[{"given":"Zheng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zhe","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Jiankun","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Hanming","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Haiying","family":"Li","sequence":"additional","affiliation":[{"name":"Sinopec Northwest China Oilfield Company, Urumuqi 830011, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1190\/1.1437077","article-title":"3-D seismic discontinuity for faults and stratigraphic features: The coherence cube","volume":"14","author":"Bahorich","year":"1995","journal-title":"Lead. 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