Automatic recognition of T and teleseismic P waves by statistical analysis of their spectra: An application to continuous records of moored hydrophones
Abstract
A network of moored hydrophones is an effective way of monitoring seismicity of oceanic ridges since it allows detection and localization of underwater events by recording generated T waves. The high cost of ship time necessitates long periods (normally a year) of autonomous functioning of the hydrophones, which results in very large data sets. The preliminary but indispensable part of the data analysis consists of identifying all T wave signals. This process is extremely time consuming if it is done by a human operator who visually examines the entire database. We propose a new method for automatic signal discrimination based on the Gradient Boosted Decision Trees technique that uses the distribution of signal spectral power among different frequency bands as the discriminating characteristic. We have applied this method to automatically identify the types of acoustic signals in data collected by two moored hydrophones in the North Atlantic. We show that the method is capable of efficiently resolving the signals of seismic origin with a small percentage of wrong identifications and missed events: 1.2% and 0.5% for T waves and 14.5% and 2.8% for teleseismic P waves, respectively. In addition, good identification rates for signals of other types (iceberg and ship generated) are obtained. Our results indicate that the method can be successfully applied to automate the analysis of other (not necessarily acoustic) databases provided that enough information is available to describe statistical properties of the signals to be identified.
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