Performance analysis of P‐wave detection algorithms for a community‐engaged earthquake early warning system – a case study of the 2022 M5.8 Cook Strait earthquake
Chanthujan Chandrakumar, Marion Lara Tan, Caroline Holden, Max T. Stephens, Raj Prasanna
Abstract
ABSTRACT Can a P‐wave detection algorithm enhance the performance of an Earthquake Early Warning System (EEWS), particularly in community‐engaged networks of low‐cost ground motion sensors susceptible to noise? If so, what P‐wave detection algorithm would perform the best? This study analyses the performance of four different P‐wave detection algorithms using a community‐engaged Earthquake Early Warning (EEW) network. The ground motion data from a 48‐hour time window around a M5.8 earthquake on 22 September 2022 were used as the basis for this case study, where false and missed detections were analysed for each P‐wave detection algorithm. The results indicate that a wavelet transformation‐based P‐wave picker is the most suitable algorithm for detecting an earthquake with minimal missed and false detections for a community‐engaged EEWS. Our results show that a citizen seismology‐based EEWS is capable of detecting events of interest to EEW when selecting an appropriate earthquake detection algorithm. The study also suggests future research areas for community‐engaged EEWSs, including dynamically changing P‐wave detection thresholds and improving citizen seismologists’ user experience and involvement.