Developing and Testing a Prototype System for Earthquake Early Warning Based on Distributed Real-Time Computing Architecture

Abstract The current earthquake early warning system (EEWS) deployed by the China Earthquake Network Center processes data from more than 18,600 stations, handling approximately 110,000 packets per second in real time. However, the system is nearing its processing capacity limit and encountering bottlenecks. To address these limitations, we have developed and redesigned an earthquake early warning prototype system (EEWPS) by integrating existing earthquake early warning algorithms with the Flink distributed real-time big data processing engine. The system was tested using a dataset comprising 182 seismic events of magnitude M 3.0 or higher recorded in mainland China from 2008 to 2023. The EEWPS successfully processed 171 of these events, with 11 missed events and no false alarms. Notably, the time from the first station trigger to the first-warning report was 5.6 ± 3.7 s, whereas the time from the origin to the first report was 9.8 ± 5.5 s. The system demonstrated an epicenter deviation of 7.4 ± 9.6 km, a magnitude deviation of −0.12 ± 0.82, and a focal depth deviation of 1.3 ± 8.0 km. The results of the four representative test events further illustrate the system’s performance in practical scenarios. In the initial early warning reports, epicenter location errors were generally constrained within 10 km, whereas magnitude errors were maintained within 0.8 units. For stress testing, the EEWPS was deployed across four virtual machines, with a combination of event waveforms and simulated data. The system successfully processed data from approximately 31,000 stations, achieving an average throughput of over 186,700 packets per second. This performance indicates that the Flink-based EEWPS not only addresses the current processing bottlenecks of the existing EEWS but also offers a potential approach for the next generation of intelligent EEWS, capable of handling large-scale data and providing timely, accurate alerts in operational environments.