Spatio-Temporal Graph Structure Learning for Earthquake Detection

Suchanun Piriyasatit, Ercan Engin Kuruoglu, Mehmet Sinan Ozeren
3/14/2025
cs.LG

Official code: SuchanunP/eq_detector (0 stars, last commit 2025-05-30).

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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Cite this paper

@article{piriyasatit2025spatiotemporal,
  title  = {Spatio-Temporal Graph Structure Learning for Earthquake Detection},
  author = {Suchanun Piriyasatit and Ercan Engin Kuruoglu and Mehmet Sinan Ozeren},
  year   = {2025},
  eprint = {2503.11215},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.11215v1}
}

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