نمایش مختصر رکورد

dc.contributor.authorOkhovvat, Hamid Rezaen_US
dc.contributor.authorRiahi, Mohammad Alien_US
dc.contributor.authorAkbari Dehkharghani, Afshinen_US
dc.date.accessioned1402-06-13T19:13:42Zfa_IR
dc.date.accessioned2023-09-04T19:13:42Z
dc.date.available1402-06-13T19:13:42Zfa_IR
dc.date.available2023-09-04T19:13:42Z
dc.date.issued2023-01-01en_US
dc.date.issued1401-10-11fa_IR
dc.date.submitted2022-09-04en_US
dc.date.submitted1401-06-13fa_IR
dc.identifier.citationOkhovvat, Hamid Reza, Riahi, Mohammad Ali, Akbari Dehkharghani, Afshin. (2023). Kernel Principal Component Analysis (KPCA) in Electrical Facies Classification. Iranian Journal of Oil and Gas Science and Technology, 12(1)doi: 10.22050/ijogst.2023.360469.1653en_US
dc.identifier.issn2345-2412
dc.identifier.issn2345-2420
dc.identifier.urihttps://dx.doi.org/10.22050/ijogst.2023.360469.1653
dc.identifier.urihttps://ijogst.put.ac.ir/article_170275.html
dc.identifier.urihttps://iranjournals.nlai.ir/handle/123456789/1033390
dc.description.abstractIn this study, in order to facies classification, the kernel principal component analysis (KPCA) feature extraction method is used to extract new features from the measured well-logs. After applying the Principal Component Analysis (PCA), and KPCA feature extraction approaches, the classification was made using three powerful classifiers: Multilayer Perceptron Neural Network (MLP), Support Vector Machine (SVM), and Random Forest (RF). Finally, the predicted results for the test data that were not included in the training process were evaluated with the F1 score criterion.The PCA method did not show a significant effect on the classification performance due to the nonlinear structure of the facies. Our results show that the KPCA improves the performance of facies classification. Compared with the conventional approach based on well-log data, our new approach improves the classification accuracy for each classifier algorithm. In the RF results, the classification accuracy has increased by about 6% while using the KPCA feature extraction approach, increasing from 52% to 58% compared to the original well-log data.en_US
dc.languageEnglish
dc.language.isoen_US
dc.publisherPetroleum University of Technologyen_US
dc.relation.ispartofIranian Journal of Oil and Gas Science and Technologyen_US
dc.relation.isversionofhttps://dx.doi.org/10.22050/ijogst.2023.360469.1653
dc.subjectElectro-Faciesen_US
dc.subjectKernel Principal Component Analysisen_US
dc.subjectSVMen_US
dc.subjectRandom foresten_US
dc.subjectWire-line logsen_US
dc.subjectPetroleum Engineering – Explorationen_US
dc.titleKernel Principal Component Analysis (KPCA) in Electrical Facies Classificationen_US
dc.typeTexten_US
dc.typeResearch Paperen_US
dc.contributor.departmentPetroleum, Mining and Materials Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran,en_US
dc.contributor.departmentTehran Universityen_US
dc.contributor.departmentPetroleum, Mining, and Materials Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran,en_US
dc.citation.volume12
dc.citation.issue1
nlai.contributor.orcid0000-0002-3827-4467


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