3:30 PM - 4:00 PM
[SCG48-12] Mapping seismic data to slow slip using machine learning
★Invited Papers
Keywords:Tremor, Slow earthquake, Machine learning, Deep learning
In a parallel approach, we show that tremor can be accurately detected on a single seismic station by a deep convolutional neural network. Seismic tremor is posited to provide information on the physical state of the slowly slipping portion of the megathrust, and a single station tremor detector gives a direct access to the tremor signal and its physical characteristics. A deep learning model trained to detect tremor in Cascadia recognizes catalogued tremor in other subduction zones, arguing for the universality of tremor and slow slip characteristics. The detection rate of a deep learning model trained to recognize tremor in Cascadia and applied on waveforms from Northern Chile shows strong correlation with surface displacement rate, arguing for episodic tremor and slip detection where slow earthquakes and associated tremor had not been seen so far, presumably taking place too deep to have been detected using conventional multi-station methods.