Earthquake source characterization by machine learning algorithms applied to acoustic signals

Abstract Underwater seismic events generate acoustic radiation (such as acoustic-gravity waves), that carries information about the source and can travel long distances before dissipating. Effective early warning, emergency response, and information dissemination for earthquakes and tsunamis require...

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Autores principales:	Bernabe Gomez, Usama Kadri
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d8cd87fd4ff14799aee525815a78f39b
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spelling	oai:doaj.org-article:d8cd87fd4ff14799aee525815a78f39b2021-12-05T12:15:14ZEarthquake source characterization by machine learning algorithms applied to acoustic signals10.1038/s41598-021-02483-w2045-2322https://doaj.org/article/d8cd87fd4ff14799aee525815a78f39b2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02483-whttps://doaj.org/toc/2045-2322Abstract Underwater seismic events generate acoustic radiation (such as acoustic-gravity waves), that carries information about the source and can travel long distances before dissipating. Effective early warning, emergency response, and information dissemination for earthquakes and tsunamis require a rapid characterisation of the fault properties: geometry and dynamics. In this work, we analysed hydrophone recordings of 201 earthquakes, located in the Pacific and the Indian Ocean, by employing acoustic signal processing and classification methods. The analysis allows identifying the type of earthquake (i.e. slip type, magnitude) and provides near real-time estimation of the effective properties of the fault dynamics and geometry. The results were compared against values reported by the Harvard Global Centroid Moment Tensor catalog (gCMT), revealing statistical significance between the extracted acoustic properties used to feed machine learning algorithms and the predicted slip and magnitude values.Bernabe GomezUsama KadriNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bernabe Gomez Usama Kadri Earthquake source characterization by machine learning algorithms applied to acoustic signals
description	Abstract Underwater seismic events generate acoustic radiation (such as acoustic-gravity waves), that carries information about the source and can travel long distances before dissipating. Effective early warning, emergency response, and information dissemination for earthquakes and tsunamis require a rapid characterisation of the fault properties: geometry and dynamics. In this work, we analysed hydrophone recordings of 201 earthquakes, located in the Pacific and the Indian Ocean, by employing acoustic signal processing and classification methods. The analysis allows identifying the type of earthquake (i.e. slip type, magnitude) and provides near real-time estimation of the effective properties of the fault dynamics and geometry. The results were compared against values reported by the Harvard Global Centroid Moment Tensor catalog (gCMT), revealing statistical significance between the extracted acoustic properties used to feed machine learning algorithms and the predicted slip and magnitude values.
format	article
author	Bernabe Gomez Usama Kadri
author_facet	Bernabe Gomez Usama Kadri
author_sort	Bernabe Gomez
title	Earthquake source characterization by machine learning algorithms applied to acoustic signals
title_short	Earthquake source characterization by machine learning algorithms applied to acoustic signals
title_full	Earthquake source characterization by machine learning algorithms applied to acoustic signals
title_fullStr	Earthquake source characterization by machine learning algorithms applied to acoustic signals
title_full_unstemmed	Earthquake source characterization by machine learning algorithms applied to acoustic signals
title_sort	earthquake source characterization by machine learning algorithms applied to acoustic signals
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d8cd87fd4ff14799aee525815a78f39b
work_keys_str_mv	AT bernabegomez earthquakesourcecharacterizationbymachinelearningalgorithmsappliedtoacousticsignals AT usamakadri earthquakesourcecharacterizationbymachinelearningalgorithmsappliedtoacousticsignals
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Earthquake source characterization by machine learning algorithms applied to acoustic signals

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