Contemporary deep learning models have demonstrated promising results across various applications within seismology and earthquake engineering. These models rely primarily on utilizing ground motion records for tasks such as earthquake event classification, localization, earthquake early warning systems, and structural health monitoring. However, the extent to which these models effectively learn from these complex time-series signals has not been thoroughly analyzed. In this study, our objective is to evaluate the degree to which auxiliary information, such as seismic phase arrival times or seismic station distribution within a network, dominates the process of deep learning from ground motion records, potentially hindering its effectiveness. We perform a hyperparameter search on two deep learning models to assess their effectiveness in deep learning from ground motion records while also examining the impact of auxiliary information on model performance. Experimental results reveal a strong reliance on the highly correlated P and S phase arrival information. Our observations highlight a potential gap in the field, indicating an absence of robust methodologies for deep learning of single-station ground motion recordings independent of any auxiliary information.

翻译：当代深度学习模型在地震学与地震工程领域的各类应用中已展现出显著成效。这些模型主要依赖地震动记录完成地震事件分类、震源定位、地震预警系统及结构健康监测等任务。然而，这些模型从复杂时序信号中有效学习的程度尚未得到深入分析。本研究旨在评估辅助信息（如地震震相到时或台网内台站分布）对深度学习过程的主导程度及其可能造成的效能制约。我们通过对两种深度学习模型进行超参数搜索，评估其从地震动记录中深度学习的效果，同时探究辅助信息对模型性能的影响。实验结果表明，模型对高度相关的P波与S波到时信息存在显著依赖性。观察结果揭示了该领域存在的潜在空白：当前尚缺乏不依赖任何辅助信息即可实现单站地震动记录深度学习的稳健方法。