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arXiv · 2503.11215

Spatio-Temporal Graph Structure Learning for Earthquake Detection

Suchanun Piriyasatit·Ercan Engin Kuruoglu·Mehmet Sinan Ozeren
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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.

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Suchanun Piriyasatit, Ercan Engin Kuruoglu, Mehmet Sinan Ozeren. 2025-03-14. Spatio-Temporal Graph Structure Learning for Earthquake Detection. https://arxiv.org/abs/2503.11215

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