Deep Learning for Real-Time P-Wave Detection: A Case Study in Indonesia's Earthquake Early Warning System

Abstract

Detecting seismic events in real-time for prompt alerts and responses is a challenging task that requires accurately capturing P-wave arrivals. This task becomes even more challenging in regions like Indonesia, where widely spaced seismic stations exist. The wide station spacing makes associating the seismic signals with specific even more difficult. This paper proposes a novel deep learning-based model with three convolutional layers, enriched with dual attention mechanisms—Squeeze, Excitation, and Transformer Encoder (CNN-SE-T) —to refine feature extraction and improve detection sensitivity. We have integrated several post-processing techniques to further bolster the model's robustness against noise. We conducted comprehensive evaluations of our method using three diverse datasets: local earthquake data from East Java, the publicly available Seismic Waveform Data (STEAD), and a continuous waveform dataset spanning 12 hours from multiple Indonesian seismic stations. The performance of the CNN-SE-T P-wave detection model yielded exceptionally high F1 scores of 99.10% for East Java, 92.64% for STEAD, and 80% for the 12-hour continuous waveforms across Indonesia's network, demonstrating the model's effectiveness and potential for real-world application in earthquake early warning systems.