## 1 Introduction  
Metro systems are an essential segment of modern large-scale urban transportation systems. In particular, the track is an essential part of the metro infrastructure, and maintaining optimal track quality guarantees safe train operation and comfortable passenger rides. Track geometry is the most widely used indicator of track quality and serves as the foundation for planning track maintenance activities [1]. This quality indicator is mainly represented by the following track geometry parameters: the longitudinal level (LL), alignment (AL), cross-level (CL), gauge (GA), and twist (TW) [2], as shown in Figure 1. Several countries have specified the management value of the deviation of track geometric parameters [3; 4; 5] and classified the deviation level according to management specifications [3; 5]. In particular, a track geometry defect (TGD) reflects the state in which the track geometric parameter exceeds the management value. For example, in the field of urban rail transit in China, the deviation values of dynamic LLs are divided into four levels according to the national standard GB/T 39559.4-2020, and the management values are 12, 16, 22, and 26 mm [5]. According to the detection values of LL, severity levels of the defects can be determined based on these thresholds. For a metro track, TGDs reduce the comfort of passengers and, in serious cases, affect the safety of train operation and may even lead to a train derailment. Thus, metro-operating companies employ various countermeasures for different defect severity levels according to actual management requirements [5; 6]. As passenger demand for metro service quality is increasing, the metro-infrastructure management industry is gradually moving toward delicacy management, and condition-prediction-based preventive maintenance (CPM) for track geometry is gradually emerging as the mainstream technology. Thus, the foundation of CPM for track geometry is aimed at obtaining accurate and timely information concerning TGDs. The most common method to detect dynamic TGDs involves the use of a track geometry car (TGC), which is a specialized full-scale track vehicle. The no-contact optical inertial-based track geometry measurement system is the principal tool used by a TGC to detect TGDs [7; 8; 9; 10; 11]. The system uses accelerometers, gyroscopes, and optical instruments installed on the TGC to measure the track geometry parameters. However, TGC-based detection involves high operating costs while offering a low detection frequency. For example, metro companies in China perform TGC detection every two months [5]. Therefore, the existing TGC-detection method cannot meet the delicacy management requirements of the metro-infrastructure management industry. In recent years, the metro industry has employed ordinary in-service trains for detecting TGDs. In particular, the technology of installing sensors [12; 13] onto in-service trains or using portable sensing devices [14; 15] to acquire vehicle vibration data [16] is used for detecting TGDs. This technology uses accelerometers and gyroscopes to collect data about the motions of various parts of the vehicle, which are used to estimate the track geometry condition [9; 10; 17]. Moreover, portable devices mounted in the cabin are more convenient and flexible to use for acquiring car-body vibration data (CVD) and reduce the need for sensor maintenance, which can further improve efficiency and reduce the cost of detection. However, a complex nonlinear relationship exists between CVD and TGD. Thus, using portable device-acquired CVD to identify TGDs is a difficult task in field applications, and this strategy is currently in the preliminary stages of development. Thus, the detection accuracy and applicable defect types should be further researched.