Earthquake source characterization by machine learning algorithms applied to acoustic signals.

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.