Spatio-Temporal Graph Structure Learning for Earthquake Detection
This work addresses earthquake early warning systems by improving multi-station detection, though it appears incremental as it builds on existing GCN methods.
The paper tackled earthquake detection by proposing a Spatio-Temporal Graph Convolutional Network with Spectral Structure Learning Convolution to model relationships across seismic stations, achieving superior true positive and false positive rates compared to a baseline GCN.
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.