用变分自编码器检测地震波，关注全局特征更有效
Variational Autoencoders for P-wave Detection on Strong Motion Earthquake Spectrograms
- 将震波检测转为自监督异常检测，测试492种网络结构
- 注意力机制模型在近源区（0-40公里）AUC达0.91
- 避免过拟合噪声，适合实时地震预警场景
准确的P波检测对地震早期预警至关重要，但强震记录因噪声高、标注数据少、波形复杂而带来挑战。本研究将P波到达检测重新定义为自监督异常检测任务，评估不同网络结构在重建精度与异常判别能力间的权衡。通过492种变分自编码器配置的全面网格搜索发现：虽然跳跃连接可最小化重建误差（平均绝对误差约0.0012），但会导致“过度泛化”，使模型误重建噪声而掩盖检测信号；相反，注意力机制更重视全局上下文而非局部细节，在检测性能上表现最优，曲线下面积（AUC）达0.875。基于注意力机制的变分自编码器在0至40公里近源范围内取得AUC 0.91，展现出高度适用于即时早期预警。研究结果表明，强调全局上下文而非像素级重建的架构约束，对实现鲁棒的自监督P波检测至关重要。
原文摘要 · Abstract (English)
Accurate P-wave detection is critical for earthquake early warning, yet strong-motion records pose challenges due to high noise levels, limited labeled data, and complex waveform characteristics. This study reframes P-wave arrival detection as a self-supervised anomaly detection task to evaluate how architectural variations regulate the trade-off between reconstruction fidelity and anomaly discrimination. Through a comprehensive grid search of 492 Variational Autoencoder configurations, we show that while skip connections minimize reconstruction error (Mean Absolute Error approximately 0.0012), they induce "overgeneralization", allowing the model to reconstruct noise and masking the detection signal. In contrast, attention mechanisms prioritize global context over local detail and yield the highest detection performance with an area-under-the-curve of 0.875. The attention-based Variational Autoencoder achieves an area-under-the-curve of 0.91 in the 0 to 40-kilometer near-source range, demonstrating high suitability for immediate early warning applications. These findings establish that architectural constraints favoring global context over pixel-perfect reconstruction are essential for robust, self-supervised P-wave detection.
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