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

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...

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Vydáno v:New Zealand journal of geology and geophysics Ročník 68; číslo 1; s. 135 - 150
Hlavní autoři: Chandrakumar, Chanthujan, Tan, Marion Lara, Holden, Caroline, Stephens, Max T., Prasanna, Raj
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 02.01.2025
Taylor & Francis Ltd
Témata:
Algorithms
Case studies
citizen seismology
Early warning systems
earthquake detection
earthquake detection algorithms
Earthquake early warning (EEW)
earthquake resilience
Earthquakes
Emergency communications systems
false detection
Ground motion
low-cost seismometers
missed detection
Motion sensors
P waves
Performance enhancement
Seismic activity
Seismology
User experience
warning systems
Wavelet analysis
Wavelet transforms
ISSN:0028-8306, 1175-8791
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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.
AbstractList 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.
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.
Author Holden, Caroline
Stephens, Max T.
Prasanna, Raj
Tan, Marion Lara
Chandrakumar, Chanthujan
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  givenname: Raj
  surname: Prasanna
  fullname: Prasanna, Raj
  organization: Massey University
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Snippet Can a P-wave detection algorithm enhance the performance of an Earthquake Early Warning System (EEWS), particularly in community-engaged networks of low-cost...
ABSTRACT Can a P‐wave detection algorithm enhance the performance of an Earthquake Early Warning System (EEWS), particularly in community‐engaged networks of...
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StartPage 135
SubjectTerms Algorithms
Case studies
citizen seismology
Early warning systems
earthquake detection
earthquake detection algorithms
Earthquake early warning (EEW)
earthquake resilience
Earthquakes
Emergency communications systems
false detection
Ground motion
low-cost seismometers
missed detection
Motion sensors
P waves
Performance enhancement
Seismic activity
Seismology
User experience
warning systems
Wavelet analysis
Wavelet transforms
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Title 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
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