A wide range of mass gain in the inland of the Tibetan Plateau (TP) has been observed by the Gravity Recovery and Climate Experiment (GRACE). But there are still controversies on how to understand this phenomenon. In this paper, satellite geodesy data and hydrological models are combined and applied for extracting signals caused by tectonic processes in the TP during the period from 2003 to 2009. We find that in the inland of the TP there are three regions with a significant signal that should be attributed to tectonic processes. The three prominent mass signals, each located in the western, central, and eastern TP, are estimated to be at the rate of 1.47 ± 0.43, −0.85 ± 0.53, and 1.51 ± 0.56 cm/year, respectively, in equivalent water height. However, the rate caused by Earth surface deformation constrained by GPS is 0.33 ± 0.15/0.30 ± 0.10/0.14 ± 0.24 cm/year in equivalent water height, which cannot explain the whole tectonic signals and indicates that a significant mass change occurs beneath the crust, that is, the deformation of the Moho interface. We estimate the Moho change rate in the inland of the TP and find that the Moho interface is suffering an uneven deformation. The Moho is uprising at a rate of 18.1 ± 7.2 mm/year in the west and 21.7 ± 9.7 mm/year in the east of the TP, while there is an obvious deepening rate of −18.3 ± 8.6 mm/year in the central TP.

Efficient monitoring and early warning are the preconditions of realizing expansive soil landslide hazard prevention and control. Previous early warning of expansive soil landslides was evaluated through soil sampling experiments to analyze the stability coefficient. However, the existing methods lack timeliness and ignore the inconsistent deformation characteristics of different parts of the landslide mass. There are still difficulties in the dynamic numerical early warning of landslides at multiple points. Considering that the degradation of expansive soil landslides’ strength is directly reflected by surface displacement, for the Ningming expansive soil demonstration area and based on the GNSS shallow real-time displacement monitoring sequence, a landslide early-warning method based on the GNSS displacement rate combined with the GNSS displacement tangent angle model was proposed, and we thus designed early-warning thresholds for different warning levels. Combined with multi-source data such as soil moisture, soil pressure, and rainfall, the feasibility of accurate early warning of expansive soil landslides based on GNSS real-time surface displacement was verified. The proposed method does not require numerical calculation of internal stress and achieved two successful early warnings of landslides in the test area, which has a certain promotional value.

On 3 July 2015, the Mw 6.5 Pishan earthquake occurred at the junction of the southwestern margin of the Tarim Basin and the northwestern margin of the Tibetan Plateau. To understand the seismogenic mechanism and the post-seismic deformation behavior, we investigated the characteristics of the post-seismic deformation fields in the seismic area, using 9 Sentinel-1A TOPS synthetic aperture radar (SAR) images acquired from 18 July 2015 to 22 September 2016 with the Small Baseline Subset Interferometric SAR (SBAS-InSAR) technique. Postseismic LOS deformation displayed logarithmic behavior, and the temporal evolution of the post-seismic deformation is consistent with the aftershock sequence. The main driving mechanism of near-field post-seismic displacement was most likely to be afterslip on the fault and the entire creep process consists of three creeping stages. Afterward, we used the steepest descent method to invert the afterslip evolution process and analyzed the relationship between post-seismic afterslip and co-seismic slip. The results witness that 447 days after the mainshock (22 September 2016), the afterslip was concentrated within one principal slip center. It was located 5–25 km along the fault strike, 0–10 km along with the fault dip, with a cumulative peak slip of 0.18 m. The 447 days afterslip seismic moment was approximately 2.65 × 10¹⁷ N m, accounting for approximately 4.1% of the co-seismic geodetic moment. The deep afterslip revealed that a creeping process from steady-state “secondary” creeping to accelerating “tertiary” creep in the deep of fault. The future seismic hazard deserves further attention and research.

A non-periodic “step-like” variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R² and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R² is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides.