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{'doi': '10.1190/geo2020-0933.1', 'member_id': '186', 'member': 'Society of Exploration Geophysicists', 'container-title': 'GEOPHYSICS', 'primary-resource': 'https://library.seg.org/doi/10.1190/geo2020-0933.1', 'tld': 'seg.org', 'clearbit-logo': 'https://logo.clearbit.com/seg.org', 'coaccess': [], 'multiple-resolution': [{'url': 'https://pubs.geoscienceworld.org/geophysics/article/87/1/r93/610167/integrating-deep-neural-networks-with-full', 'tld': 'geoscienceworld.org', 'clearbit-logo': 'https://logo.clearbit.com/geoscienceworld.org'}], 'type': 'JOURNAL ARTICLE', 'published_date': '1 January 2022', 'publication': 'GEOPHYSICS', 'supplementary_ids': '10.1190/geo2020-0933.1', 'title': 'Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification', 'name': None, 'id': None, 'location': None, 'display_doi': 'https://doi.org/10.1190/geo2020-0933.1', 'grant_info': 'Department of Energy Basic Energy Sciences (DE-SC0020445) | Applied Mathematics Program within the Department of Energy (DOE) Office of Advanced Scientific Computing Research (DE-SC0019453)', 'editors': None, 'authors': 'Weiqiang Zhu | Kailai Xu | Eric Darve | Biondo Biondi | Gregory C. Beroza', 'chairs': None}
https://doi.org/10.1190/geo2020-0933.1
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