Tectonic modeling of Konya-Beysehir Region (Turkey) using cellular neural networks

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A. Muhittin Albora
O. Nuri Uçan
D. Aydogan


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.

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How to Cite
Muhittin Albora, A., Nuri Uçan, O. and Aydogan, D. (2007) “Tectonic modeling of Konya-Beysehir Region (Turkey) using cellular neural networks”, Annals of Geophysics, 50(5). doi: 10.4401/ag-3060.