Exploring the Use of the EPIC Earthquake Early Warning Algorithm in Alaska

Earthquake early warning (EEW) systems are vital for mitigating seismic risk. Earthquake Point-Source Integrated Code (EPIC), an algorithm used in the ShakeAlert EEW program, has successfully characterized the shallow, crustal earthquakes that dominate the seismicity on the U.S. West Coast. To address performance blind spots and evaluate the feasibility of expanding EEW to complex tectonic environments, this study tests the EPIC algorithm using a dataset of 530 Alaska moderate-to-large-magnitude earthquakes (M 4.5 to 8.2) with hypocenter depths ranging from 1 to 212 km. We utilize real seismic waveforms and metadata to evaluate EPIC’s performance in simulated real-time replays. Performance is measured based on detection success, epicenter accuracy, and magnitude estimation. EPIC successfully detected and matched 345 of the 530 events, with the highest reliability in high station density on-land regions like Southcentral Alaska. The average initial location error was 28.4 km, improving to 22.9 km at final alert. A significant performance gap was identified for deep events: magnitude estimates for deep earthquakes (depths >100 km) showed an initial average underestimate of 0.71 magnitude units, compared with only 0.18 for shallow events. This error is largely attributed to EPIC’s fixed 8 km depth assumption, which creates a mismatch in station distance calculations for deep events at near-field stations. These findings highlight that incorporating a hypocenter depth correction factor could significantly improve the accuracy of magnitude and location estimates in areas where deep earthquakes are expected. Testing of different maximum station distance thresholds could also improve detection in data-sparse regions. Ideally, this is paired with studies of how this change affects simultaneous data-quality checks, like EPIC’s teleseismic waveform flag. The results also set a baseline performance for EPIC that can be used when expanding EEW performance to Alaska. The openly available Alaska test suite also provides a robust benchmark for future algorithm development.