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dc.contributor.authorRivera, J.A 
dc.contributor.authorShahriari, M
dc.contributor.authorPardo, D. 
dc.contributor.authorOmella, J.
dc.contributor.authorTorres-Verdín, C.
dc.date.accessioned2022-03-07T14:22:40Z
dc.date.available2022-03-07T14:22:40Z
dc.date.issued2021-11-01
dc.identifier.issn2214-4609
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1439
dc.description.abstractWe propose the use of a Deep Learning (DL) algorithm for the real-time inversion of electromagnetic measurements acquired during geosteering operations. Moreover, we show that when the DL algorithm is equipped with a properly designed two-step loss function without regularization, it is possible to recover an uncertainty quantification map by analyzing certain cross-plots. We illustrate these ideas with a synthetic example based on piecewise 1D earth models. The resulting uncertainty quantification map could be used to design better measurement acquisition systems for geosteering operations.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.titleUncertainty Quantification on the Inversion of Geosteering Measurements using Deep Learningen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.identifier.doi10.3997/2214-4609.2021624005en_US
dc.relation.publisherversionhttps://www.earthdoc.org/content/papers/10.3997/2214-4609.2021624005en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/777778en_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.title3rd EAGE/SPE Geosteering Workshopen_US
dc.volume.number2021en_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España