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This work presents novel pattern recognition techniques applied on bathymetric data from two large areas in Eastern Mediterranean. Our objectives are as follows: (a) to demonstrate the efficiency of this methodology, (b) to highlight the quick and accurate detection of both hydrocarbon related tectonic lineaments and salt structures affecting seafloor morphology, and (c) to reveal new structural data in areas poised for hydrocarbon exploration. In our work, we first apply a multiple filtering and sequential skeletonization scheme inspired by the hysterisis thresholding technique. In a second stage, we categorize each linear and curvilinear segment on the seafloor skeleton (medial axis) based on the strength of detection as well as the length, direction, and spatial distribution. Finally, we compare the seafloor skeleton with ground truth data. As shown in this paper, the automatic extraction of the bathymetric skeleton allows the interpretation of the most prominent seafloor morphological features. We focus on the competent tracing of tectonic lineaments, as well as the effective distinction between seafloor features associated with shallow evaporite movements and those related to intense tectonic activity. The proposed scheme has low computational demand and decreases the cost of the marine research because it facilitates the selection of targets prior to data acquisition.

remote sensing

Automatic Pattern Recognition of Tectonic Lineaments in Seafloor Morphology to Contribute in the Structural Analysis of Potentially Hydrocarbon-Rich Areas