{'doi': '10.1190/geo2020-0886.1', 'member_id': '186', 'member': 'Society of Exploration Geophysicists', 'container-title': 'GEOPHYSICS', 'primary-resource': 'https://library.seg.org/doi/10.1190/geo2020-0886.1', 'tld': 'seg.org', 'clearbit-logo': 'https://logo.clearbit.com/seg.org', 'coaccess': [], 'multiple-resolution': [{'url': 'https://pubs.geoscienceworld.org/geophysics/article/86/6/m185/608024/data-driven-s-wave-velocity-prediction-method-via', 'tld': 'geoscienceworld.org', 'clearbit-logo': 'https://logo.clearbit.com/geoscienceworld.org'}], 'type': 'JOURNAL ARTICLE', 'published_date': '1 November 2021', 'publication': 'GEOPHYSICS', 'supplementary_ids': '10.1190/geo2020-0886.1', 'title': 'Data-driven S-wave velocity prediction method via a deep-learning-based deep convolutional gated recurrent unit fusion network', 'name': None, 'id': None, 'location': None, 'display_doi': 'https://doi.org/10.1190/geo2020-0886.1', 'grant_info': 'National Natural Science Foundation of China (41430323,41774192,41974160,42030812,42042046)', 'editors': None, 'authors': 'Jun Wang | Junxing Cao', 'chairs': None}