Analysis of slow motion mechanism of Wangjiapo No. 3 landslide based on InSAR and numerical simulation

Currently, research on slow-moving slopes is hindered by a lack of integration between the temporal and spatial dimensions in analyzing movement characteristics and their underlying causes. Additionally, the potential instability of the No. 3 slope at Wangjiapo in Bailuyuan, an important residential and tourist area, poses significant risks of casualties and economic losses. To investigate the movement mechanism of this slow slope, we developed a collaborative analytical framework consisting of “temporal InSAR monitoring, multi-scale signal decomposition, three-dimensional numerical simulation, and field verification.” Initially, we extracted the annual average velocity field and time sequence displacement of the landslide mass movement by comprehensively utilizing SBAS-InSAR and StaMPS-InSAR technologies. Furthermore, we decomposed the high-precision InSAR time series displacement into periodic and trend components using wavelet decomposition. The trend component was fitted using a multi-segment nonlinear fitting method to calculate the temporal movement velocity of the landslide. We conducted Pearson correlation analysis between deep displacement measurements, GNSS data, and the wavelet-decomposed trend displacement. The relationship between rainfall and the displacement changes of the periodic components was quantified through linear regression combined with correlation analysis. Subsequently, we employed three MESH finite difference numerical models with varying degrees of freedom to assess the stability of the landslide and simulate its motion characteristics using the intensity reduction method. Finally, we performed field verification based on the velocity field derived from time-series InSAR, the wavelet-decomposed time-series displacement, and the numerical simulation results. The findings indicate that: (1) StaMPS-InSAR demonstrates higher accuracy compared to SBAS-InSAR. The InSAR velocity field reveals that the upper portion of the landslide moves more slowly, while the middle and lower sections exhibit faster velocities. (2) The Pearson correlation coefficients between the wavelet-decomposed InSAR trend displacement and the deep displacement measurements and GNSS data were 0.96 and 0.95, respectively. (3) The calculated time series velocity, following multi-segment nonlinear fitting of the trend displacement, shows spatiotemporal variations in the landslide’s movement, with fewer acceleration events in the upper part compared to the middle and lower sections. This variation in velocity changes correlates with differences in soil strength at different locations. Notably, there is no significant linear correlation between the periodic displacement changes and rainfall. The safety factor obtained from numerical simulations was 0.967, indicating that the lower part of the slope is a stress concentration area, consistent with findings from InSAR and on-site investigations. This analytical framework has achieved a multi-dimensional spatiotemporal analysis of landslide movement through the organic coupling and progressive verification of multiple technical methods, providing a scientific basis for addressing the Wangjiapo No. 3 landslide and offering valuable insights for studying the movement mechanisms of similar slow-moving landslides.