Spatio-Temporal Graph Structure Learning for Earthquake Detection

• URL: http://arxiv.org/abs/2503.11215v1
• Date: Fri, 14 Mar 2025 09:07:18 GMT
• Title: Spatio-Temporal Graph Structure Learning for Earthquake Detection
• Authors: Suchanun Piriyasatit, Ercan Engin Kuruoglu, Mehmet Sinan Ozeren,
• Abstract summary: We propose a Spatio-Temporal Graph Convolutional Network (GCN) to model static and dynamic relationships across seismic stations.<n>Our approach processes multi-station waveform data and generates station-specific detection probabilities.<n> Experiments show superior performance over a conventional GCN baseline in terms of true positive rate (TPR) and false positive rate (FPR)
• Score: 1.6044444452278062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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