Earthquake source characterization by machine learning algorithms applied to acoustic signals

Gomez, Bernabe and Kadri, Usama ORCID: https://orcid.org/0000-0002-5441-1812 2021. Earthquake source characterization by machine learning algorithms applied to acoustic signals. Scientific Reports 11 , 23062. 10.1038/s41598-021-02483-w

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Additional Information:	This article is licensed under a Creative Commons Attribution 4.0 International License
Publisher:	Nature Research
ISSN:	2045-2322
Funders:	EPSRC
Date of First Compliant Deposit:	17 November 2021
Date of Acceptance:	17 November 2021
Last Modified:	10 May 2023 20:50
URI:	https://orca.cardiff.ac.uk/id/eprint/145568

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