Earthquakes are natural events that are interesting to analyze in order to gain knowledge about its causes, occurrences and properties; specifically, is important to know the epicenter location that can be determined using the times of arrival of the P and S phases present in the seismogram. Different methods have been applied for seismic detection. The best results have been obtained when multi-station threedimensional data was used; a reliable phase detection technique using one-dimensional data does not exist already. Time-Frequency Representations (TFRs) seems to be an alternative tool to process the seismic trace and to detect the P and S waves because the seismogram is a non-stationary signal. However, the usefulness of TFR analysis depends largely on the transformation kernel. In this work we proposed a TFR-based algorithm using two types of kernel : a) Line Dirac-delta kernel and b) The magnitude of the ambiguity function of a low noise P wave. Experiments with real seismograms were performed. The results shown that not only is possible to detect the P and the S phases, also the technique provides frequency characterization that can be used to distinguish between both waves even in cases where the seismogram is very noisy.
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