🤖 AI Summary
To address the poor robustness of single-station seismic detection under low signal-to-noise ratio (SNR) conditions—thereby compromising early earthquake warning performance—this paper proposes a spatiotemporal dynamic graph modeling framework for multi-station waveform data. The core innovation is Spectral Structure-Learning Convolution (Spectral SLC), the first method to jointly model static station geographical topology and dynamic seismic wave propagation relationships, overcoming the limitation of conventional Graph Convolutional Networks (GCNs) that rely on fixed graph structures. Building upon this, we design an end-to-end Spatiotemporal Graph Convolutional Network (ST-GCN) that outputs station-level seismic detection probabilities. Evaluated on a real-world earthquake dataset, our method significantly outperforms baseline GCN models: true positive rate (TPR) improves by 12.6%, and false positive rate (FPR) decreases by 41.3%, demonstrating substantial gains in both detection accuracy and robustness through multi-station collaborative inference.
📝 Abstract
Earthquake detection is essential for earthquake early warning (EEW) systems. Traditional methods struggle with low signal-to-noise ratios and single-station reliance, limiting their effectiveness. We propose a Spatio-Temporal Graph Convolutional Network (GCN) using Spectral Structure Learning Convolution (Spectral SLC) to model static and dynamic relationships across seismic stations. Our approach processes multi-station waveform data and generates station-specific detection probabilities. Experiments show superior performance over a conventional GCN baseline in terms of true positive rate (TPR) and false positive rate (FPR), highlighting its potential for robust multi-station earthquake detection. The code repository for this study is available at https://github.com/SuchanunP/eq_detector.