A novel step-like deformation model for reservoir landslide monitoring with multi-temporal InSAR

Reservoir landslides are significant geological hazards that pose severe risks to reservoir safety. Detecting the spatial-temporal evolution of slope movement is crucial for effective risk assessment and disaster mitigation. InSAR technology has been extensively employed to monitor surface deformations in reservoir landslides. However, the accuracy of InSAR-derived deformation fields is often limited by the reliability of prior deformation model. Traditional models, which primarily rely on linear or periodic function, frequently overlook the step-like evolution characteristics of reservoir landslides. To address this limitation, this study introduces a multi-temporal InSAR approach that incorporates Sigmoid function to enhance the deformation modeling of reservoir landslides. To solve the nonlinear parameters within the model, Taylor series expansion-based observation equation is constructed to estimate these parameters accurately. The proposed model was evaluated using both the simulated and real datasets from the Hongyanzi landslide in the Pubugou reservoir area. The results demonstrate that the proposed model significantly improves the accuracies of parameter estimation and deformation time-series. Experiments conducted under the sensitivity of interferogram stacks and varying atmospheric phase screen interference magnitudes further confirm the proposed model’s robustness and application potential. In addition, the sensitivity analysis of the initial parameters in the real data experiment scenario demonstrates the robustness of the proposed model’s nonlinear parameter estimation. Finally, the cross-correlation analysis reveals that the deformation of the Hongyanzi landslide is triggered by the decline of the reservoir water level, and quantitatively evaluates the lag time between the deformation and the reservoir water level. Our results offer novel insights for InSAR monitoring of other complex deformation evolution scenarios. Prior information is incorporated into the deformation modeling to estimate a more reliable InSAR deformation field.