Surface deformation monitoring is essential for controlling instability processes such as urban infrastructure deformation, mining-induced subsidence, and landslide deformation. However, missing data often disrupt the continuity of the various deformation time series and compromise the reliability of monitoring results. This issue is particularly critical in long-term landslide studies, where conventional missing data imputation methods often neglect the nonlinear characteristics of slope deformation and fail to account for external influences under complex environmental conditions. To address these limitations, this study proposes a deep learning-based imputation method that integrates multi-source monitoring data. A Seq2Seq LSTM (sequence-to-sequence long short-term memory) model is constructed to reconstruct missing deformation values, and a posterior correction module is integrated to optimize the preliminary outputs, thereby enhancing imputation accuracy. The proposed approach is validated using a case study of the southern dump slope landslide at the Hesigewula South Open-Pit Coal Mine in Inner Mongolia, China. Experimental results on the test set demonstrate that the Seq2Seq LSTM–Posterior Correction model significantly outperforms traditional methods such as linear regression and baseline LSTM models. This method offers an effective solution to data gaps in landslide deformation monitoring, demonstrating strong potential for accurate nonlinear imputation in complex environments and providing a practical approach for long-term InSAR-based landslide studies in areas affected by missing SAR data.

Large stilling basins founded on heterogeneous weak-rock foundations may experience nonuniform deformation due to spatial variations in foundation stiffness. Although pile reinforcement is widely used for deformation control, its effectiveness may vary with local structure–foundation conditions. Overall and local deformation may also respond differently to pile parameters. This study conducted three-dimensional FLAC3D analyses of the Kala Hydropower Station stilling basin under the check-flood condition. Two representative transverse sections with different weak-rock distributions, pile embedment conditions, and structural configurations were compared under unreinforced and pile-reinforced conditions. The maximum resultant displacement (U_max) and maximum vertical joint offset (ΔU_joint) were used to characterize overall and local differential deformation, respectively. Under the 2.5 m × 2.5 m pile layout, U_max and ΔU_joint decreased by only 3.1% and 3.2% in Section 14–14, compared with 33.7% and 58.9% in Section 16–16. The two deformation measures also responded differently to pile spacing and pile length, and denser layouts or longer piles did not consistently reduce both overall and local deformation. Varying the thickness of competent rock overlying the weak rock produced only small changes in deformation, whereas increasing the weak-rock deformation modulus reduced both deformation measures, particularly in Section 16–16. These findings show that the effectiveness of pile reinforcement varies among structural locations with different foundation and embedment conditions. Pile layouts should therefore be evaluated for individual structural locations using both overall displacement and joint differential movement.