## 3 Methods  
### Framework  
Given the drawbacks of previous studies, such as the difficulty in accurately recognizing cracks in subway environments and the inability to obtain crack information, this study aims to provide a large-area settlement solution based on PS-InSAR technology, considering its ability for monitoring subway facilities with high accuracy over a long period of time. Meanwhile, a crack detection method based on UAVs, deep learning and digital image processing is proposed, which can accurately quantify the length and width of cracks in the complex environment around the subway. The research concept is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & **Previous Research** & **Authors** & **Limitations** \\ \hline \multirow{4}{*}{PS-InSAR} & Detect the subsidence & \multirow{2}{*}{[PERSON] et al.} & The provided settlement \\  & and ground fissure & & information is not \\ \cline{3-3}  & Analyze the surface & & \\ \cline{3-3}  & deformation & & \\ \cline{3-3}  & Calculate the vertical & & \\ \cline{3-3}  & surface displacements & & \\ \hline \multirow{4}{*}{Crack recognition} & Proposed a computer & \multirow{4}{*}{[PERSON] et al.} & \multirow{4}{*}{Recognition results are greatly influenced by noise} \\  & vision-based & & \\ \cline{3-3}  & pavement crack & & \\ \cline{3-3}  & detection algorithm & & \\ \cline{3-3} Crack recognition & Proposed a new & & \\ \cline{3-3}  & base on image & & \\ \cline{3-3}  & processing method & & \\ \cline{3-3}  & for detecting cracks & & \\ \cline{3-3}  & on concrete surfaces & & \\ \cline{3-3}  & under various & & \\ \cline{3-3}  & conditions & & \\ \hline \multirow{4}{*}{Crack recognition} & Proposed a NB-CNN & \multirow{4}{*}{[PERSON] and} & \multirow{4}{*}{The accuracy of} \\  & deep learning & & \\ \cline{3-3}  & framework for & & \\ \cline{3-3}  & frame-by-frame crack & & \\ \cline{3-3}  & detection in & & \\ \cline{3-3}  & base on classification & individual videos & & \\ \cline{3-3}  & Proposed a & & \\ \cline{3-3}  & CNN-based method & & \\ \cline{3-3}  & for pavement crack & & \\ \cline{3-3}  & detection & & \\ \hline \multirow{4}{*}{Crack measurement} & Proposed a method to & \multirow{4}{*}{[PERSON] et al.} & \multirow{4}{*}{[PERSON] et al.} \\  & measure cracks with & & \\ \cline{3-3}  & handheld devices & & \\ \cline{3-3}  & Applied the Gabor & \multirow{4}{*}{Sulistyangirum} & \multirow{4}{*}{Handheld devices have significant limitations for crack detection in} \\ \cline{3-3}  & filter method to crack & & \\ \cline{3-3}  & measurement & & \\ \hline \multirow{4}{*}{The proposed method} & \multirow{4}{*}{Integrated PS-InSAR, convolutional neural network, and digital image processing techniques.} & \multirow{4}{*}{Large-area, all-weather, all-day automated settlement monitoring} \\ \cline{3-3}  & & & \\ \cline{3-3}  & & & \\ \cline{3-3}  & & & \\ \cline{3-3}  & & & \\ \cline{3-3}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of prior work and description of the proposed method.  
width, length and area can be calculated, laying the foundation for further hazard assessment.
4. Empirical research: Taking the Shenzhen University Section of Metro Line 9 as an example, the specific process of implementing the settlement monitoring model proposed in this paper is demonstrated, and the model's validity is verified.