Toward a Multi-Station Deep Learning Framework for Enhanced Earthquake Early Warning

Earthquake Early Warning (EEW) systems are vital for providing timely alerts in seismically active regions, potentially reducing damage and saving lives. However, achieving both rapid and reliable alerts remains a significant challenge. Recent advances in deep learning (DL) and established workflows for picking, associating, and locating events offer complementary paths to improved performance. In this work, we propose to investigate a multi-station deep learning framework that can be integrated with existing event-location pipelines or used directly to estimate ground shaking (e.g., peak ground acceleration, PGA). By fusing raw seismic waveforms with station metadata (e.g., location, sensor characteristics) in an end-to-end manner, the approach aims to capture both local site conditions and regional propagation effects. As an initial step, we will establish baseline performance using simpler neural networks (e.g., CNNs, LSTMs), then expand to more advanced models to evaluate potential gains in accuracy and speed. Preliminary findings indicate that aggregating real-time signals from multiple stations can outperform single-station methods in both alert timing and predictive reliability. Ultimately, our goal is to develop an adaptable, data-driven EEW pipeline that accommodates either direct shaking forecasts or event-based parameter estimation, enabling seamless integration into larger-scale monitoring networks and enhancing the timeliness of earthquake alerts.