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.