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Evaluating and Improving the ElarmS Earthquake Early Warning Algorithm


Earthquake Alarm Systems, or ElarmS, is a network-based earthquake early warning (EEW) system developed in California. This dissertation concerns the transition of ElarmS from research prototype to production-grade code. We test ElarmS' performance for large earthquakes with an extensive dataset of large events from Japan. Using the Japanese dataset and results, we develop a statistical error model for the magnitude, location, and ground motion prediction algorithms. We adapt ElarmS to run continuously throughout the state and analyze system latencies due to various seismic networks and instruments. We then rewrite ElarmS completely in newer, more efficient code, while redesigning the association and alert algorithms for improved performance. Finally, we apply an Artificial Neural Network (ANN) at the end of system processing, to detect and block false alerts. The ANN allows ElarmS to send earlier, faster alert messages, when fewer stations have contributed data to an event estimate. ElarmS now sends realtime alert messages to the California Integrated Seismic Network's ShakeAlert EEW system.

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