Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/4056
Authors: Muhittin Albora, A.* 
Nuri Uçan, O.* 
Aydogan, D.* 
Title: Tectonic modeling of Konya-Beysehir Region (Turkey) using cellular neural networks
Issue Date: Oct-2007
Series/Report no.: 5/50 (2007)
URI: http://hdl.handle.net/2122/4056
Keywords: Cellular Neural Network (CNN)
forward and inverse modeling
tectonic modeling
boundary analysis
Central Turkey
Subject Classification04. Solid Earth::04.07. Tectonophysics::04.07.07. Tectonics 
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.
Appears in Collections:Annals of Geophysics

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