{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T18:35:43Z","timestamp":1789929343152,"version":"4.0.1"},"reference-count":60,"publisher":"Society of Exploration Geophysicists","issue":"6","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41974131"],"award-info":[{"award-number":["41974131"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41774135"],"award-info":[{"award-number":["41774135"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41974131"],"award-info":[{"award-number":["41974131"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41774135"],"award-info":[{"award-number":["41774135"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["library.seg.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Seismic vertical resolution is critical for accurately identifying subsurface structures and reservoir properties. Improving the vertical resolution of vintage seismic data with strongly supervised deep learning is challenging due to scarce or costly labels. To remedy the label-lacking problem, we develop a weakly supervised deep-learning method to improve vintage seismic data with poor resolution by extrapolating from nearby high-resolution seismic data. Our method uses a cycle generative adversarial network with an improved identity loss function. In addition, we contribute a pseudo-3D training data construction strategy that reduces discontinuity artifacts caused by accessing 3D field data with a 2D network. We determine the feasibility of our method on 2D synthetic data and achieve results comparable to the classic time-varying spectrum whitening method on field poststack migration data while effectively recovering more high-frequency information.<\/jats:p>","DOI":"10.1190\/geo2023-0006.1","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T09:46:50Z","timestamp":1689155210000},"page":"V445-V458","update-policy":"https:\/\/doi.org\/10.1190\/crossmark-policy","source":"Crossref","is-referenced-by-count":31,"title":["Improving vertical resolution of vintage seismic data by a weakly supervised method based on cycle generative adversarial network"],"prefix":"10.1190","volume":"88","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5553-2379","authenticated-orcid":false,"given":"Dawei","family":"Liu","sequence":"first","affiliation":[{"name":"Xi\u2019an Jiaotong University 1 , School of Information and Communications Engineering, Xi\u2019an, China and , Department of Physics, Edmonton, Alberta, Canada. 409791715@qq.com"},{"name":"University of Alberta 1 , School of Information and Communications Engineering, Xi\u2019an, China and , Department of Physics, Edmonton, Alberta, Canada. 409791715@qq.com"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenli","family":"Niu","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University 2 , School of Information and Communications Engineering, Xi\u2019an, China. 3101434366@qq.com"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5396-985X","authenticated-orcid":false,"given":"Xiaokai","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University 2 , School of Information and Communications Engineering, Xi\u2019an, China. xkwang@xjtu.edu.cn"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6654-6661","authenticated-orcid":false,"given":"Mauricio D.","family":"Sacchi","sequence":"additional","affiliation":[{"name":"University of Alberta 3 , Department of Physics, Edmonton, Alberta, Canada. msacchi@ualberta.ca"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2686-067X","authenticated-orcid":false,"given":"Wenchao","family":"Chen","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University 2 , School of Information and Communications Engineering, Xi\u2019an, China. wencchen@xjtu.edu.cn (corresponding author)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Daqing Oilfield Company Ltd. 4 , the Exploration and Development Research Institute, Daqing, China. wangcheng88@petrochina.com.cn"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"186","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"issue":"3","key":"2025121211490591000_r1","doi-asserted-by":"crossref","first-page":"SE175","DOI":"10.1190\/INT-2018-0249.1","article-title":"A machine-learning benchmark for facies classification","volume":"7","author":"Alaudah","year":"2019","journal-title":"Interpretation"},{"key":"2025121211490591000_r2","first-page":"26","article-title":"Spectral whitening of seismic data","volume":"2","author":"Bian","year":"1986","journal-title":"Geophysical Prospecting for Petroleum"},{"key":"2025121211490591000_r3","doi-asserted-by":"crossref","unstructured":"Chen\n              D.\n            \n            \n              Gao\n              J.\n            \n            \n              Hou\n              Y.\n            \n            \n              Gao\n              Z.\n            \n          , 2019, High resolution inversion of seismic wavelet and reflectivity using iterative deep neural networks: 89th Annual International Meeting, SEG, Expanded Abstracts, 2538\u20132542, doi:http:\/\/dx.doi.org\/10.1190\/segam2019-3216178.1.","DOI":"10.1190\/segam2019-3216178.1"},{"issue":"3","key":"2025121211490591000_r4","doi-asserted-by":"crossref","first-page":"R265","DOI":"10.1190\/geo2020-0034.1","article-title":"Optimization-inspired deep learning high-resolution inversion for seismic data","volume":"86","author":"Chen","year":"2021","journal-title":"Geophysics","ISSN":"https:\/\/id.crossref.org\/issn\/0016-8033","issn-type":"print"},{"key":"2025121211490591000_r5","doi-asserted-by":"crossref","unstructured":"Choi\n              Y.\n            \n            \n              Seol\n              S. 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