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dc.contributor.authorAdler, A
dc.contributor.authorAraya-Polo, M
dc.contributor.authorPoggio, T
dc.date.accessioned2021-12-09T19:30:50Z
dc.date.available2021-12-09T19:30:50Z
dc.date.issued2021-03-01
dc.identifier.urihttps://hdl.handle.net/1721.1/138408
dc.description.abstract© 1991-2012 IEEE. Seismic inversion is a fundamental tool in geophysical analysis, providing a window into Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for hydrocarbon exploration, mining, earthquake analysis, shallow hazard assessment, and other geophysical tasks.en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/MSP.2020.3037429en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceProf. Poggioen_US
dc.titleDeep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflowsen_US
dc.typeArticleen_US
dc.identifier.citationAdler, A, Araya-Polo, M and Poggio, T. 2021. "Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows." IEEE Signal Processing Magazine, 38 (2).
dc.relation.journalIEEE Signal Processing Magazineen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-12-09T19:15:49Z
dspace.orderedauthorsAdler, A; Araya-Polo, M; Poggio, Ten_US
dspace.date.submission2021-12-09T19:15:53Z
mit.journal.volume38en_US
mit.journal.issue2en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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