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In this paper, we propose a method for automatic enhancement and identification of partial curvilinear structures. The accurate identification of line structures in geophysical images plays an important role in geophysical interpretation and the detection of subsurface structures. The method was applied on geophysical images in an effort to recognize the linear patterns of subsurface architectural structures that exist in archaeological sites. To our knowledge, the problem of identification of curvilinear structures in geophysical images for archaeological sites is faced for the first time. The method efficiently combines a rotation- and scale-invariant filter and a pixel-labeling method, providing a robust enhancement and detection of mostly line structures in 2-D grayscale images, respectively. Experimental results on real and synthetic images and comparison with existing methods in the literature indicated the reliable performance of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a method for the automatic identification of P-phase arrival based on the distribution of local maxima (LM) in earthquake seismograms. The method efficiently combines energy and frequency characteristics of the LM distribution (LMD). The P detection is mainly based on the energy of a seismic event in the case the earthquake has higher amplitude than seismic background noise. Otherwise, it is based on the frequency of LM. Thus, the method provides robust detection of P-phase arrival in any quality type of seismic data. Moreover, it uses two sequential sliding signal windows yielding very high accuracy on the P-phase estimation. A hierarchical P-phase detection algorithm dramatically reduces the computational cost, making possible a real-time implementation. Experimental results from a large database of more than 80 low, medium, and high signal-to-noise ratio seismic events and comparison with existing methods in the literature indicate the reliable performance of the proposed scheme. [ABSTRACT FROM AUTHOR]