Please use this identifier to cite or link to this item:
http://hdl.handle.net/2122/4056
DC Field | Value | Language |
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dc.contributor.authorall | Muhittin Albora, A.; Geophysical Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey | en |
dc.contributor.authorall | Nuri Uçan, O.; Electrical and Electronics Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey | en |
dc.contributor.authorall | Aydogan, D.; Geophysical Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey | en |
dc.date.accessioned | 2008-09-12T08:16:55Z | en |
dc.date.available | 2008-09-12T08:16:55Z | en |
dc.date.issued | 2007-10 | en |
dc.identifier.uri | http://hdl.handle.net/2122/4056 | en |
dc.description.abstract | In this paper, to separate regional-residual anomaly maps and to detect borders of buried geological bodies, we applied the Cellular Neural Network (CNN) approach to gravity and magnetic anomaly maps. CNN is a stochastic image processing technique, based optimization of templates, which imply relationships of neighborhood pixels in 2-Dimensional (2D) potential anomalies. Here, CNN performance in geophysics, tested by various synthetic examples and the results are compared to classical methods such as boundary analysis and second vertical derivatives. After we obtained satisfactory results in synthetic models, we applied CNN to Bouguer anomaly map of Konya-Beysehir Region, which has complex tectonic structure with various fault combinations. We evaluated CNN outputs and 2D/3D models, which are constructed using forward and inversion methods. Then we presented a new tectonic structure of Konya-Beysehir Region. We have denoted (F1, F2, …, F7) and (Konya1, Konya2) faults according to our evaluations of CNN outputs. Thus, we have concluded that CNN is a compromising stochastic image processing technique in geophysics. | en |
dc.language.iso | English | en |
dc.relation.ispartof | Annals of Geophysics | en |
dc.relation.ispartofseries | 5/50 (2007) | en |
dc.subject | Cellular Neural Network (CNN) | en |
dc.subject | forward and inverse modeling | en |
dc.subject | tectonic modeling | en |
dc.subject | boundary analysis | en |
dc.subject | Central Turkey | en |
dc.title | Tectonic modeling of Konya-Beysehir Region (Turkey) using cellular neural networks | en |
dc.type | article | en |
dc.type.QualityControl | Peer-reviewed | en |
dc.subject.INGV | 04. Solid Earth::04.07. Tectonophysics::04.07.07. Tectonics | en |
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dc.description.journalType | JCR Journal | en |
dc.description.fulltext | open | en |
dc.contributor.author | Muhittin Albora, A. | en |
dc.contributor.author | Nuri Uçan, O. | en |
dc.contributor.author | Aydogan, D. | en |
dc.contributor.department | Geophysical Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey | en |
dc.contributor.department | Electrical and Electronics Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey | en |
dc.contributor.department | Geophysical Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey | en |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | Istanbul University, Engineering Faculty, Geophysical Department, Avcilar, Istanbul, Turkey | - |
crisitem.author.dept | Electrical and Electronics Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey | - |
crisitem.author.dept | Geophysical Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey | - |
crisitem.classification.parent | 04. Solid Earth | - |
Appears in Collections: | Annals of Geophysics |
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