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 atthis https URL.
View on arXiv@article{piriyasatit2025_2503.11215,
title={ Spatio-Temporal Graph Structure Learning for Earthquake Detection },
author={ Suchanun Piriyasatit and Ercan Engin Kuruoglu and Mehmet Sinan Ozeren },
journal={arXiv preprint arXiv:2503.11215},
year={ 2025 }
}