This paper presents findings from ongoing research that explores the ability to use Micro-Electromechanical Systems (MEMS)-based technologies and various digital communication protocols for earthquake early warning (EEW). The paper proposes a step-by-step guide to developing a unique EEW network architecture driven by a Software-Defined Wide Area Network (SD-WAN)-based hole-punching technology consisting of MEMS-based, low-cost accelerometers hosted by the general public. In contrast with most centralised cloud-based approaches, a node-level decentralised data-processing is used to generate warnings with the support of a modified Propagation of Local Undamped Motion (PLUM)-based EEW algorithm. With several hypothetical earthquake scenarios, experiments were conducted to evaluate the system latencies of the proposed decentralised EEW architecture and its performance was compared with traditional centralised EEW architecture. The results from sixty simulations show that the SD-WAN-based hole-punching architecture supported by the Transmission Control Protocol (TCP) creates the optimum alerting conditions. Furthermore, the results provide clear evidence to show that the decentralised EEW system architecture can outperform the centralised EEW architecture and can save valuable seconds when generating EEW, leading to a longer warning time for the end-user. This paper contributes to the EEW literature by proposing a novel EEW network architecture.

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.

This study addresses the critical question of predicting the amplitude of S-waves during earthquakes in Aotearoa New Zealand (NZ), a highly earthquake-prone region, for implementing an Earthquake Early Warning System (EEWS). This research uses ground motion parameters from a comprehensive dataset comprising historical earthquakes in the Canterbury region of NZ. It explores the potential to estimate the damaging S-wave amplitude before it arrives, primarily focusing on the initial P-wave signals. The study establishes nine linear regression relationships between P-wave and S-wave amplitudes, employing three parameters: peak ground acceleration, peak ground velocity, and peak ground displacement. Each relationship’s performance is evaluated through correlation coefficient (R), coefficient of determination (R²), root mean square error (RMSE), and 5-fold Cross-validation RMSE, aiming to identify the most predictive empirical model for the Canterbury context. Results using a weighted scoring approach indicate that the relationship involving P-wave Peak Ground Velocity (Pv) within a 3-second window strongly correlates with S-wave Peak Ground Acceleration (PGA), highlighting its potential for EEWS. The selected empirical relationship is subsequently applied to establish a P-wave amplitude (Pv) threshold for the Canterbury region as a case study from which an EEWS could benefit. The study also suggests future research exploring complex machine learning models for predicting S-wave amplitude and expanding the analysis with more datasets from different regions of NZ.

Can an effective earthquake early warning (EEW) system be implemented at a low cost in earthquake-prone regions? This study addresses this question by evaluating the feasibility of a low-cost, community-engaged EEW system in the Greater Wellington region of New Zealand. The system utilizes decentralized processing to run two algorithms: (1) NZ-PLUM, an adaptation of the propagation of local undamped motion approach tailored to New Zealand’s seismic context, and (2) NZ-PLUM-P, an enhanced version that incorporates P-wave detection for faster alerts. NZ-PLUM triggers alerts based on horizontal acceleration thresholds that are more sensitive to S waves, whereas NZ-PLUM-P offers a longer warning window by detecting the initial P waves. The system’s effectiveness is evaluated through an analysis of ambient noise in the community-hosted network, where 24 out of 27 stations exhibit low-to-medium noise levels. A false alert analysis confirms zero false alerts with both algorithms at an alerting threshold of Modified Mercalli intensity 5.0. The performance is further assessed using data from three recent local earthquakes: an M 3.9 event on 23 May, an M 4.1 event on 3 June, and an M 4.0 event on 6 July 2024. The results show that NZ-PLUM-P significantly improves warning times compared to NZ-PLUM, with average gains of 2.6, 1.25, and 6.4 s across five target points. These findings demonstrate the feasibility of implementing a low-cost, community-driven network capable of delivering effective EEWs. This approach presents a promising, cost-effective alternative for developing or enhancing EEW systems in earthquake-prone regions. Future research should focus on expanding the algorithms across the entire network, incorporating site amplification factors to refine intensity predictions, and increasing community engagement to further reduce noise and enhance system reliability.