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Automatic organofacies identification by means of Machine Learning on Raman spectra

dc.contributor.authorVergara Sassarini, Natalia A.
dc.contributor.authorSchito, Andrea
dc.contributor.authorGasparrini, Marta
dc.contributor.authorMichel, Pauline
dc.contributor.authorCorrado, Sveva
dc.date.accessioned2023-05-25T13:36:01Z
dc.date.available2023-05-25T13:36:01Z
dc.date.issued2023-04-15
dc.identifier.citationVergara Sassarini , N A , Schito , A , Gasparrini , M , Michel , P & Corrado , S 2023 , ' Automatic organofacies identification by means of Machine Learning on Raman spectra ' , International Journal Of Coal Geology , vol. 271 , 104237 . https://doi.org/10.1016/j.coal.2023.104237en
dc.identifier.issn0166-5162
dc.identifier.otherPURE: 246413669
dc.identifier.otherPURE UUID: 1f924c4f-6bb9-48fe-a2b3-9ec0f1a41487
dc.identifier.otherScopus: 85153086274
dc.identifier.urihttps://hdl.handle.net/2164/20778
dc.descriptionFunding Information: IFP Energies nouvelles (France) is warmly acknowledgment for kindly providing access to samples, laboratory facilities and unpublished database. Dr. Amalia Spina and Prof. Simonetta Cirilli from the University of Perugia are warmly acknowledged for the high-quality kerogen isolate extraction. This research was funded by: MIUR grants to Roma Tre PhD School in Earth Sciences (XXXIV doctoral cycle, 2018–2021) and IFP Energies nouvelles PhD program. Publisher Copyright: © 2023 The Authorsen
dc.language.isoeng
dc.relation.ispartofInternational Journal Of Coal Geologyen
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)en
dc.subjectCluster analysisen
dc.subjectDispersed organic matteren
dc.subjectMachine learningen
dc.subjectPrincipal component analysisen
dc.subjectRaman spectroscopyen
dc.subjectThermal maturityen
dc.subjectT Technologyen
dc.subjectQE Geologyen
dc.subjectFuel Technologyen
dc.subjectGeologyen
dc.subjectEconomic Geologyen
dc.subjectStratigraphyen
dc.subject.lccTen
dc.subject.lccQEen
dc.titleAutomatic organofacies identification by means of Machine Learning on Raman spectraen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Geology and Geophysicsen
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.1016/j.coal.2023.104237
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85153086274&partnerID=8YFLogxKen
dc.identifier.vol271en


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