A new approach to the structural features of the Aegean Sea: Cellular neural network

Abstract

In this study, structural features in the Aegean Sea were investigated by application of Cellular Neural Network (CNN) and Cross-Correlation methods to the gravity anomaly map. CNN is a stochastic image processing technique, which is based on template optimization using neighbourhood relationships of pixels, and probabilistic properties of two-Dimensional (2-D) input data. The performance of CNN can be evaluated by various interesting real applications in geophysics such as edge detection, data enhancement and separation of regional/residual potential anomaly maps. In this study, CNN is used in edge detection of geological bodies closer to the surface, which are masked by other structures with various depths and dimensions. CNN was first tested for (prismatic) synthetic examples and satisfactory results were obtained. Subsequently, CNN/Cross-Correlation maps and bathymetric features were evaluated together to obtain a new structural map for most of the Aegean Sea. In our structural map, the locations of the faults and basins are generally in accordance with the previous maps from restricted areas based on seismic data. In the southern and southeastern parts of the Aegean Sea, E–W trending faults cut NE–SW trending basins and faults, similar to on-shore Western Anatolia. Also, in the western, central and northern parts of the Aegean Sea, all of these structures are truncated by NE-trending faults.