Automatic recognition of T and teleseismic P waves by statistical analysis of their spectra: An application to continuous records of moored hydrophones

• Published in: Journal of Geophysical Research: Solid Earth
• Main Authors: Sukhovich, Alexey, Irisson, Jean-Olivier, Perrot, Julie, Nolet, Guust
• Other Authors: Domaines Océaniques (LDO), Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Observatoire des Sciences de l'Univers-Institut d'écologie et environnement-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Géoazur (GEOAZUR 6526), Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2014
• Subjects: Data analysis; algorithms and implementation; Data sets; Neural networks; fuzzy logic; machine learning; Ocean observatories and experiments; Ocean observing systems; [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph]; [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph]; [SDE.MCG]Environmental Sciences/Global Changes; North Atlantic
• Online Access: https://insu.hal.science/insu-01058954; https://insu.hal.science/insu-01058954/document; https://insu.hal.science/insu-01058954/file/jgr-Sukowich.pdf; https://doi.org/10.1002/2013JB010936

International audience 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.