Spatio-Temporal Graph Structure Learning for Earthquake Detection
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915197970546688 |
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| author | Piriyasatit, Suchanun Kuruoglu, Ercan Engin Ozeren, Mehmet Sinan |
| author_facet | Piriyasatit, Suchanun Kuruoglu, Ercan Engin Ozeren, Mehmet Sinan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11215 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spatio-Temporal Graph Structure Learning for Earthquake Detection Piriyasatit, Suchanun Kuruoglu, Ercan Engin Ozeren, Mehmet Sinan Machine Learning 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. |
| title | Spatio-Temporal Graph Structure Learning for Earthquake Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.11215 |