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.

翻译：精确的P波检测对于地震早期预警至关重要，然而强震动记录由于高噪声水平、有限的标记数据以及复杂的波形特征而带来挑战。本研究将P波初至检测重新定义为一项自监督异常检测任务，以评估架构变化如何调节重建保真度与异常判别之间的权衡。通过对492种变分自编码器配置的全面网格搜索，我们发现虽然跳跃连接能最小化重建误差（平均绝对误差约为0.0012），但它们会引发"过度泛化"，使模型能够重建噪声并掩盖检测信号。相比之下，注意力机制优先考虑全局上下文而非局部细节，并实现了最高的检测性能，其曲线下面积达到0.875。基于注意力的变分自编码器在0至40公里近源范围内实现了0.91的曲线下面积，表明其对于即时早期预警应用具有高度适用性。这些发现证实，倾向于全局上下文而非像素级完美重建的架构约束对于稳健的自监督P波检测至关重要。