Deep clustering of traffic signals using a single seismic station

In this study, we present an unsupervised deep clustering framework based on Deep Embedded Clustering (DEC) to identify traffic-induced signal segments from high-frequency seismic noise recorded by a single seismic station. This method enables cost-effective event extraction from ambient noise without labeled data or dense sensors, offering a alternative for urban traffic monitoring, particularly in areas with limited data or few monitoring devices. The presented dataset is part of our study on using DEC to analyze traffic-induced seismic signals at urban scales. 1. The Field Data folder contains raw data from three sites.2. The input for DEC folder provides the dataset used for model training.本研究提出了一种基于深度嵌入聚类（Deep Embedded Clustering, DEC）的无监督深度聚类框架，用于从单地震台站记录的高频地震噪声中识别交通诱发的信号片段。该方法无需标注数据与密集布设的传感器，即可从环境噪声中高效低成本地提取事件信号，为城市交通监测提供了可行方案，尤其适用于数据匮乏或监测设备不足的区域。本次发布的数据集属于本研究中利用DEC开展城市尺度交通诱发地震信号分析工作的一部分。 1. 野外数据（Field Data）文件夹：包含3个测点的原始观测数据。 2. DEC模型训练输入（input for DEC）文件夹：存储用于模型训练的数据集。