## 4 Results and Discussion
### SBAS-InSAR Results
\begin{table}
\begin{tabular}{c c} \hline
**Time Period** & **Cumulative Deformation/mm** \\ \hline
2016.1–2016.12 & –430 \\
2017.1–2017.12 & –445 \\
2018.1–2018.12 & –442 \\
2019.1–2019.12 & –433 \\ \hline \end{tabular}
\end{table} 
Table 2: The annual cumulative deformation during the monitoring period. Figure 6: (**a**) Deformation rate map in the direction of LOS of the Guobu slope; (**b**) cumulative deformation map of the Guobu slope at some moments. We selected three distinct target points on the slope surface for time series analysis, as shown by the blue dots in Figure 6a. To mitigate the interference of random errors on these selected points, a 60 m buffer zone was created around each target point. The mean displacement of all deformation points within the buffer zone was chosen as the representative value for each target point, as shown in Figure 8. The error bars indicate the standard deviation of all measurements within the buffer zone. Notably, point P1, located in the upper-middle part of the landslide, has an accumulated deformation of over 1.75 m during the monitoring period, while point P2, situated in the bottom area of the landslide, has an accumulated deformation of approximately 1.2 m. Point P3, located in the central area of the plateau, has an accumulated deformation close to 1 m.  
The deformation time series of the feature points exhibits non-uniform linear changes from September 2015 to December 2019, which can be regarded as the arithmetic sum of linear and nonlinear components. The linear component represents the non-elastic displacement of the slope due to its own gravity, while the nonlinear component represents the acceleration or deceleration of displacement due to external factors [53]. In this study, the linear component of deformation was obtained through least-squares fitting, and the nonlinear component is defined as the difference between the deformation time series and the linear component. A plot of the displacement nonlinear component versus precipitation and water level data is constructed and shown in Figure 9. It can be observed that there is a certain periodicity and correlation between the nonlinear component of the feature points and the precipitation and reservoir water level data. To additionally discover their  
Figure 8: Cumulative deformation map of feature points. Figure 7: Profiles of deformation rate and elevation. correlations, the wavelet transform method is employed to analyze their correlations and lags in the time-frequency domain. We extracted the reservoir water level data from 2009 to 2014 and the 3D deformation data of 17 GPS monitoring points located in the headscarp of the Guobu slope from [PERSON]'s article [26], and used vector displacement, i.e., \(\sqrt{N^{2}+E^{2}+U^{2}}\), to represent the landslide movement, as shown in Figure 10. [PERSON] et al. performed a cross-correlation analysis of the reservoir water levels and the nonlinear components of 15 GPS monitoring points (excluding QC9 and IP10) between June 2011 and November 2013. They found that the changes were fully synchronized, indicating that the stability of the slope was entirely influenced by the fluctuation of the reservoir water levels and that the impact of precipitation could be disregarded [30].