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dc.contributor.authorHashemian, A. 
dc.contributor.authorGarcia, D.
dc.contributor.authorRivera, J.A.
dc.contributor.authorPardo, D. 
dc.date.accessioned2021-06-21T06:02:08Z
dc.date.available2021-06-21T06:02:08Z
dc.date.issued2021-06
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1299
dc.description.abstractBorehole resistivity measurements are routinely inverted in real-time during geosteering operations. The inversion process can be efficiently performed with the help of advanced artificial intelligence algorithms such as deep learning. These methods require a massive dataset that relates multiple Earth models with the corresponding borehole resistivity measurements. In here, we propose to use an advanced numerical method —refined isogeometric analysis (rIGA)— to perform rapid and accurate 2.5D simulations and generate databases when considering arbitrary 2D Earth models. Numerical results show that we can generate a meaningful synthetic database composed of 100,000 Earth models with the corresponding measurements in 56 hours using a workstation equipped with two CPUs.en_US
dc.description.sponsorshipEuropean POCTEFA 2014–2020 Project PIXIL (EFA362/19); The grant ‘‘Artificial Intelligence in BCAM number EXP. 2019/00432en_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.subjectGeosteeringen_US
dc.subjectBorehole resistivity measurementsen_US
dc.subjectRefined isogeometric analysisen_US
dc.subject2.5D numerical simulationen_US
dc.subjectDeep learning inversionen_US
dc.titleMassive Database Generation for 2.5D Borehole Electromagnetic Measurements using Refined Isogeometric Analysisen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1016/j.cageo.2021.104808
dc.relation.publisherversionhttps://doi.org/10.1016/j.cageo.2021.104808en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/777778en_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDES/2PE/PID2019-108111RB-I00en_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/publishedVersionen_US
dc.journal.titleComputers & Geosciencesen_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