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.
Highlights
Underwater seismic events generate acoustic radiation, that carries information about the source and can travel long distances before dissipating
machine learning (ML) algorithms were applied to the acoustic signal properties to estimate two main characteristics of the studied tectonic events: slip type and moment magnitude
Two classification approaches were taken to identify the slip type associated with the tectonic events that generated the acoustic signals composing the dataset: binary and multi-class
Summary
Underwater seismic events generate acoustic radiation (such as acoustic-gravity waves), that carries information about the source and can travel long distances before dissipating. Underwater seismic events can produce very long compression-type waves, known as acoustic-gravity waves (AGWs), that propagate in the water layer travelling long distances with almost no attenuation[1] and can be recorded by distant hydrophones This property of AGWs allows them to carry information on the sound source[1,2]. Feature vectors serve as input to ML algorithms that perform classification of the slip type (existence of significant vertical motion component) and assessment of the magnitude of the event This information is used to feed an inverse problem model for acoustic waves that calculates the effective geometry and dynamics of the fault[3]
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