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Istanbul University, Engineering Faculty, Geophysical Department, Avcilar, Istanbul, Turkey
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- PublicationOpen AccessA new approach for residual gravity anomaly profile interpretations: Forced Neural Network (FNN)(2006-12)
; ; ; ;Osman, O.; Istanbul Commerce University, Eminonu, Istanbul, Turkey ;Muhittin Albora, A.; Istanbul University, Engineering Faculty, Geophysical Department, Avcilar, Istanbul, Turkey ;Ucan, O. N.; Istanbul University, Engineering Faculty, Electrical & Electronics Dept, Avcilar, Istanbul, Turkey; ; This paper presents a new approach for interpretation of residual gravity anomaly profiles, assuming horizontal cylinders as source. The new method, called Forced Neural Network (FNN), is introduced to determine the underground structure parameters which cause the anomalies. New technologies are improved to detect the borders of geological bodies in a reliable way. In a first phase one neuron is used to model the system and a back propagation algorithm is applied to find the density difference. In a second phase, density differences are quantified and a mean square error is computed. This process is iterated until the mean square error is small enough. After obtaining reliable results in the case of synthetic data, to simulate real data, the real case of the Gulf of Mexico gravity anomaly map, which has the form of anticline structure, is examined. Gravity anomaly values from a cross section of this real case, result to be very close to those obtained with the proposed method.606 998 - PublicationOpen AccessTectonic modeling of Konya-Beysehir Region (Turkey) using cellular neural networks(2007-10)
; ; ; ;Muhittin Albora, A.; Geophysical Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey ;Nuri Uçan, O.; Electrical and Electronics Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey ;Aydogan, D.; Geophysical Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey; ; 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.216 392