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.

翻译：地震检测对于地震早期预警系统至关重要。传统方法受限于低信噪比和单台站依赖，其有效性受到限制。我们提出了一种采用谱结构学习卷积的时空图卷积网络，用于建模地震台站间的静态与动态关系。该方法处理多台站波形数据，并生成针对各台站的检测概率。实验表明，相较于传统图卷积网络基线，本方法在真阳性率和假阳性率方面均表现出更优性能，突显了其在稳健多台站地震检测中的潜力。本研究的代码仓库发布于 https://github.com/SuchanunP/eq_detector。