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dc.contributor.authorUÇAN, Osman Muri
dc.contributor.authorAlbora, Ali Muhittin
dc.date.accessioned2021-03-04T14:11:48Z
dc.date.available2021-03-04T14:11:48Z
dc.date.issued2006
dc.identifier.citationAlbora A. M. , UÇAN O. M. , "Separation of magnetic-field data using the differential Markov random-field (DMRF) approach", GEOPHYSICS, cilt.71, sa.2, 2006
dc.identifier.issn0016-8033
dc.identifier.otherav_80474267-6538-44d9-b029-f1f2e53b50b9
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/87514
dc.identifier.urihttps://doi.org/10.1190/1.2187773
dc.description.abstractIn this paper, the differential Markov random-field (DMRF) method is introduced and applied to the magnetic anomaly separation problem, in which residual anomalies are separated from a regional field. The DMRF method is all unsupervised statistical model-based learning approach that does not require prior knowledge. A data-adaptive program, based on the evaluation of noise and superimposed effects of various geologic structures, is presented by considering a statistical maximum a posteriori (MAP) criterion. The aim of our method is to capture the intrinsic properties of geologic structures and then to identify and hence understand the behavior of the observed magnetic-anomaly map. The magnetic-anomaly map is modeled using a 2D matrix. In the DMRF approach, each pixel of the matrix is evaluated considering neighboring pixels. In synthetic models, anomalies of magnetic dipoles are tested for different depths, orientation angles, and lengths.
dc.language.isoeng
dc.subjectYerbilimleri
dc.subjectMühendislik ve Teknoloji
dc.subjectJeofizik Mühendisliği
dc.subjectTemel Bilimler (SCI)
dc.subjectJEOKİMYA VE JEOFİZİK
dc.titleSeparation of magnetic-field data using the differential Markov random-field (DMRF) approach
dc.typeMakale
dc.relation.journalGEOPHYSICS
dc.contributor.departmentİstanbul Üniversitesi , ,
dc.identifier.volume71
dc.identifier.issue2
dc.contributor.firstauthorID30897


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