用时空图网络提升地震检测精度，兼顾多台站数据与噪声鲁棒性。
Spatio-Temporal Graph Structure Learning for Earthquake Detection
- 构建时空图卷积网络，学习地震台站间的静态与动态关系
- 在真实数据上实现更高检出率（TPR）和更低误报率（FPR）
- 适合需要高可靠性地震预警的科研与应急响应团队
地震检测对地震早期预警系统至关重要。传统方法受限于信噪比低和依赖单个台站，效果不佳。本文提出一种基于谱结构学习卷积（Spectral SLC）的时空图卷积网络（GCN），用于建模多个地震台站间的静态与动态关联。该方法处理多台站波形数据，输出各台站的地震检测概率。实验表明，在真阳性率（TPR）和假阳性率（FPR）方面均优于传统GCN基线模型，展现出在多台站地震检测中具备更强鲁棒性的潜力。相关代码已公开于https://github.com/SuchanunP/eq_detector。
原文摘要 · Abstract (English)
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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