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

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

Signal perception and belief updating

This paper introduces a theory of signal perception to study how people update their beliefs. By allowing perceived signals to deviate from actual signals, we identify the probability that people miss or misread signals, giving indices of conservatism and confirmatory bias. In an experiment, we elicited perceived signals from choices and obtained a structural estimation of the indices. The subjects were conservative and acted as if they missed 65% of the signals they received. Also they exhibited confirmatory bias by misreading 17% of the signals contradicting their prior beliefs

A cross-section of expected stock returns on the Istanbul stock exchange

[No abstract available