Improvements from incorporating machine learning algorithms into near real-time operational post-processing - Gabrielle Tepp; Ellen Yu; Aparna Bhaskaran; Ryan Tam; Weiqiang Zhu; Zackary Newman; Erika Jaski; Nick Scheckel (Scientific Reports)
Abstract During regional seismic monitoring, data is automatically analyzed in real-time to identify events and provide initial locations and magnitudes. Monitoring networks may apply automatic post-processing to small events (M < 3) to add and refine picks and improve the event before analyst review. Recently, machine learning algorithms, particularly for phase picking, have matured enough for use in regional monitoring systems. The Southern California Seismic Network has implemented the deep-learning picker PhaseNet in our event post-processing, resulting in about 2–3 times as many picks, particularly S phases, with slightly better pick accuracy than the previous STA/LTA picker (relative to analyst picks). These improvements have led to better epicenter accuracy. We have also developed an automatic post-processing pipeline (ST-Proc) for sub-network triggers, which are collections of nearby phase picks that the real-time system could not associate into an event. ST-Proc uses PhaseNet to find phase picks and the machine learning algorithm GaMMA to associate events. This pipeline is capable of correctly detecting events in 65–70% of triggers containing events with a low false event rate around 5%. Additionally, the GaMMA-determined epicenters are generally accurate (within a few kilometers of the final). Both pipelines have helped to reduce analyst workload and streamline event processing.