Earthquake P-wave detection is the first and most critical step in Earthquake Early Warning (EEW), as detection speed determines how much time is available before strong shaking arrives, while detection accuracy governs the reliability of the warning. Conventional P-Wave detection methods such as Short-Term Average over Long-Term Average (STA/LTA) often struggle in complex noise environments and exhibit higher detection latency. Deep learning offers a more robust alternative by learning complex waveform characteristics directly from data, enabling faster and more reliable detections. However, the high computational cost of deep models limits their use on low-cost, resource-constrained edge devices in decentralized EEW networks. Here we present a lightweight Convolutional Neural Network optimized for real-time P- and S-wave detection on edge hardware using only two seconds of seismic input waveforms. Trained on approximately 89 K waveform segments recorded by strong-motion sensors across New Zealand, the model achieves an overall accuracy of 97.12% and correctly identifies 98% of P-wave segments. With ~ 38 K trainable parameters and sub-7-millisecond inference time on Raspberry Pi 5 hardware, the model demonstrates efficient operation under edge-computing constraints. Its ability to generalize to high-magnitude events highlights its strong potential for integration into distributed EEW systems.
Ravishan et al. (2026) studied this question.