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dc.contributor.authorAlbora, Ali Muhittin
dc.contributor.authorUcan, Osman N.
dc.contributor.authorBal, Abdullah
dc.date.accessioned2021-03-05T09:13:03Z
dc.date.available2021-03-05T09:13:03Z
dc.date.issued2007
dc.identifier.citationAlbora A. M. , Bal A., Ucan O. N. , "A new approach for border detection of the Dumluca (Turkey) iron ore area: Wavelet cellular neural networks", PURE AND APPLIED GEOPHYSICS, cilt.164, ss.199-215, 2007
dc.identifier.issn0033-4553
dc.identifier.otherav_9cd0afc4-6fa7-49a4-be49-ad2e6fabc710
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/105364
dc.identifier.urihttps://doi.org/10.1007/s00024-006-0156-5
dc.description.abstractAnomaly analysis is used for various geophysics applications such as determination of geophysical structure's location and border detections. Besides the classical geophysical techniques, artificial intelligence based image processing algorithms have been found attractive for geophysical anomaly analysis. Recently, cellular neural networks (CNN) have been applied to geophysical data and satisfactory results are reported. CNN provides fast and parallel computational capability for geophysical image processing applications due to its filtering structure. The behavior of CNN is defined by two template matrices that are adjusted by a properly supervised learning algorithm. After training stage for geophysical data, Bouguer anomaly maps can be processed and analyzed sequentially. In this paper, CNN learning and processing capability have been improved, combining Wavelet functions and backpropagation learning algorithms. The new architecture is denoted as Wavelet-Cellular Neural networks (Wave-CNN) and it is employed to analyze Bouguer anomaly maps which are important to extract useful information in geophysics. At first, Wave-CNN performance is tested on synthetic geophysical data, which are created by a computer environment. Then, Bouguer anomaly maps of the Dumluca iron ore field have been analyzed and results are reported in comparison to real drilling results.
dc.language.isoeng
dc.subjectJeofizik Mühendisliği
dc.subjectMühendislik ve Teknoloji
dc.subjectJEOKİMYA VE JEOFİZİK
dc.subjectYerbilimleri
dc.subjectTemel Bilimler (SCI)
dc.titleA new approach for border detection of the Dumluca (Turkey) iron ore area: Wavelet cellular neural networks
dc.typeMakale
dc.relation.journalPURE AND APPLIED GEOPHYSICS
dc.contributor.department, ,
dc.identifier.volume164
dc.identifier.issue1
dc.identifier.startpage199
dc.identifier.endpage215
dc.contributor.firstauthorID30905


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