We propose a method for detecting earthquakes for high-speed trains based on unsupervised anomaly-detection techniques. In particular, we utilized autoencoder-based deep learning models for unsupervised learning using only normal training vibration data. Datasets were generated from South Korean high-speed train data, and seismic data were measured using seismometers nationwide. The proposed method is compared with the conventional Short Time Average over Long Time Average (STA/LTA) model, considering earthquake detection capabilities, focusing on a Peak Ground Acceleration (PGA) threshold of 0.07, a criterion for track derailment. The results show that the proposed model exhibit improved earthquake detection capabilities than STA/LTA for PGA of 0.07 or higher. Furthermore, the proposed model reduced false earthquake detections under normal operating conditions and accurately identified normal states. In contrast, the STA/LTA method demonstrated a high rate of false earthquake detection under normal operating conditions, underscoring its propensity for inaccurate detection in many instances. The proposed approach shows promising performance even in situations with limited seismic data and offers a viable solution for earthquake detection in regions with relatively few seismic events.

我们提出了一种基于无监督异常检测技术的高速列车地震检测方法。特别是，我们利用基于自动编码器的深度学习模型仅使用正常的训练振动数据进行无监督学习。数据集是根据韩国高铁数据生成的，地震数据是使用全国地震仪测量的。将所提出的方法与传统的短时平均长期平均（STA/LTA）模型进行比较，考虑地震检测能力，重点关注峰值地面加速度（PGA）阈值0.07，这是轨道脱轨的标准。结果表明，对于 PGA 为 0.07 或更高的情况，所提出的模型表现出比 STA/LTA ​​更高的地震检测能力。此外，所提出的模型减少了正常运行条件下的错误地震检测，并准确识别了正常状态。相比之下，STA/LTA ​​方法在正常操作条件下表现出很高的地震错误检测率，这凸显了其在许多情况下检测不准确的倾向。即使在地震数据有限的情况下，所提出的方法也显示出良好的性能，并为地震事件相对较少的地区的地震检测提供了可行的解决方案。