Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/180406
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dc.contributor.advisorMarcuello Pascual, Alejandro-
dc.contributor.authorNocelo Sampedro, Paula-
dc.date.accessioned2021-10-05T15:24:45Z-
dc.date.available2021-10-05T15:24:45Z-
dc.date.issued2021-02-
dc.identifier.urihttp://hdl.handle.net/2445/180406-
dc.descriptionTreballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2021, Tutor: Alejandro Marcuello Pascualca
dc.description.abstractThis report is based on magnetotelluric (MT) data from the electromagnetic (EM) Earth field. The main objective is to analyze, reduce, and asses EM noise, coming from man-made technologies. To evaluate how to denoise MT data, a code is developed in Python, which uses the K-Means technique from machine learning. In the end, it is obtained a preliminary method to identify noise situations.ca
dc.format.extent5 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Nocelo, 2021-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceTreballs Finals de Grau (TFG) - Física-
dc.subject.classificationProspecció magnetotel·lúricacat
dc.subject.classificationAprenentatge automàticcat
dc.subject.classificationTreballs de fi de graucat
dc.subject.otherMagnetotelluric prospectingeng
dc.subject.otherMachine learningeng
dc.subject.otherBachelor's theseseng
dc.titleProcessing of Magnetotelluric Data Using Machine Learning Techniqueseng
dc.typeinfo:eu-repo/semantics/bachelorThesisca
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
Appears in Collections:Treballs Finals de Grau (TFG) - Física

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