This study employs the SBAS-InSAR technique to process 123 ascending Sentinel-1 images covering the intersected area of the active Nantinghe fault zone and the Nanyun expressway from 2019 to 2023, obtaining the deformation rate map of the intersected area. Combined with independent component analysis (ICA) and remote sensing images, the characteristics of surface deformation are derived. Subsequently, typical points including the intersection positions A₁ to A₃ of the fault zone and the expressway, as well as the large-span bridge position B, are selected to construct the ICA-VMD-LSTM deformation prediction model. The results indicate that the majority of the study area exhibits deformation rates within ±5 mm/a. The primary deformation patterns include sudden events, seasonal periodic deformation, and long-term linear trends. Compared with the conventional VMD-LSTM model, the proposed ICA-VMD-LSTM model demonstrates superior feature extraction capability and enhanced robustness to noise, thereby improving predictive accuracy. On average, the ICA-VMD-LSTM model achieved reductions of approximately 33.2% and 32.4% in MAE and RMSE, respectively, and an increase of 23.3% in R², indicating significant improvements in error control and trend representation.

Based on convolutional neural network (CNN), an automatic evaluation method for InSAR interferogram quality is proposed. This method is applied to Sentinel-1 data experiments, achieving accuracy and recall rate of 96.5% and 98% on the test set, respectively. The results of automatic screening and manual selection have good consistency. We apply the method to PS-InSAR deformation processing in Nanchang area, it is found that selecting high-quality interferograms for deformation analysis through quality evaluation can significantly improve the accuracy of deformation time series measurement.

Utilizing continuous observation data from 41 strong-motion stations and 20 China Mobile Beidou CORS stations near the epicenter of the M6.8 Dingri, Xizang earthquake on January 7, 2025, this study systematically evaluates seismic intensity distribution and co-seismic surface deformation characteristics. Results indicate that the maximum instrumental intensity reached 8.0 degree, with significant spatial attenuation (1.67 degrees per 100 km) and anisotropy (isoseismal major axis trending nearly N-S). High-precision co-seismic deformation fields near the epicenter were accurately obtained through Beidou static undifferenced PPP and double-difference baseline network solutions, combined with a dual-epoch median displacement differencing method. Discrepancies with GPS observations were at millimeter level. The Beidou high-dynamic PPP mode effectively captured centimeter-level transient deformation signals. Furthermore, spatial patterns of co-seismic displacement fields from Beidou observations align with theoretical rupture model simulations, consistently demonstrating horizontal expansion west and east of the epicentral area and convergence toward the epicenter from the northern sector.

Based on the GNSS earth observation network system (GEONET) in Japan and the Taiwan geophysical network for seismology (TGNS) in China, this paper investigates the propagation characteristics of co-seismic ionospheric disturbance(CsIDs) triggered by the 1 January 2024 Noto Peninsula M_W7.5 earthquake in Japan and the 2 April 2024 Hualien M_W7.4 earthquake in Taiwan, China. Meanwhile, the reasons for the anisotropy of the CsIDs produced by the two earthquakes are discussed. The results show that: 1) For the Noto Peninsula earthquake, the seismically excited acoustic waves propagated to the height of the F2 layer of the ionosphere within 8 to 9 minutes, and the triggered CsIDs propagated to the southeast of the epicenter at a speed of ~0.89 km/s, with a maximum amplitude of 0.768 TECu. The 2-minute rate of TEC index (ROTI) maps show higher sensitivity than the 2D-filtered TEC maps. 2) The CsIDs of the Hualien earthquake were also excited by acoustic waves and reached the height of the F2 layer of the ionosphere after ~8 minutes, with a maximum amplitude close to 0.4 TECu. 3) The propagation direction of the CsIDs triggered by the Noto Peninsula earthquake was strongly linked to the geomagnetic field, whereas those triggered by the Hualien earthquake were influenced by both the source mechanism and the geomagnetic field.

Considering the random characteristics of GNSS monitoring data, an adaptive identification method for the uniform deformation rate of landslides is proposed. By adopting low-order and high-order Savitzky-Golay (SG) filtering respectively, sequences suitable for early warning analysis and consistent with the deformation trend are obtained. On this basis, a rate fluctuation interval is defined, and the main deformation rate segments with dense sample points are located to determine the uniform deformation rate. Accordingly, a staged early warning model for landslide creep is constructed. This method is applied to three typical landslides, namely loess landslides, reservoir bank landslides, and coal mine landslides, to verify the identification of the uniform deformation rate and its early warning effect. The results show that this method can significantly suppress GNSS noise interference and improve the stability and engineering application value of the early warning system.

A multi-neural-network weighted ensemble model for landslide displacement prediction is constructed by integrating three machine learning models: support vector regression (SVR), back propagation neural network (BPNN), and linear regression neural network (LRNN). The model development process primarily consists of two components: individual model prediction and multi-neural-network weighting. Instead of emphasizing prediction errors from all historical moments preceding the current prediction point, this ensemble model focuses on the proximity period around the current prediction point. Optimal weight coefficients for individual prediction models are calculated using the least squares method based on observed data, followed by weighted summation of predictions from individual models at the current point using these weights, ultimately achieving high-precision forecasting results. Comparative tests conducted on Beidou/GNSS monitoring data from the Dangchuan landslide mass at Heifangtai in Gansu province—a typical loess landslide area in China—demonstrate that this proposed ensemble model exhibits superior prediction accuracy compared to single-model approaches, simple averaging ensemble models, and multi-neural-network switching models. The proposed methodology shows significant potential for practical applications in landslide displacement prediction.

This study integrates the high spatial-resolution monitoring advantages of InSAR with physics-based reduced-order modeling to invert sliding surface parameters. Focusing on the progressive Landslide 5# in Dangchuan, Heifangtai (Gansu province)—a typical loess landslide-prone area—we adapted the fault dislocation model from seismology to develop a novel inversion framework. By incorporating time-series InSAR deformation as surface constraints into the dislocation model, the method progressively inverts sliding surface geometry and position from a geodetic perspective. The results indicate that the sliding surface of the 5# landslide mass is located approximately 4.83±0.3 m beneath the slope surface, with a length of about 136.43±0.5 m, a width of around 47.82±0.3 m, and an aspect of approximately 272°±1.0°. The model inversion achieved a fitting degree of 99%, which is generally consistent with the results of geological surveys and statistical analyses.

Based on multi-frequency GNSS observations within a 450 km radius of the 2025 M_W6.8 Dingri earthquake epicenter, including low-frequency (30 s), high-rate (1 Hz), and ultra-high-rate (50 Hz) data, we derived both coseismic static displacement fields and dynamic waveforms using precise point positioning (PPP) technology. Our findings reveal that: 1) The low-frequency GNSS coseismic static displacement field shows a maximum permanent westward displacement of 40.0 mm in the epicentral region, consistent with regional tectonic stress field patterns; 2) High-frequency GNSS waveforms captured significant dynamic displacement peaks at near-field stations, with the XZZF (Everest) station exhibiting 28.83 mm east-west displacement, though a 12.5 mm deviation from low-frequency results was observed due to dynamic effects; 3) Ultra-high-rate GNSS waveforms recorded Love and Rayleigh waves in the far field, identifying double-pulse characteristics of fault segmented rupture. Spectral analysis of high- and ultra-high-rate coseismic waveforms demonstrated that near-field stations (such as XZZF) concentrated energy primarily at 0.5 to 5 Hz, reflecting initial fault rupture processes, while far-field stations (such as XZRK) were dominated by low-frequency surface waves (0.1 to 1 Hz), validating the complementary nature of multi-frequency GNSS data. Notably, ultra-high-rate observations proved irreplaceable for capturing high-rate components (>5 Hz) and transient rupture processes of seismic waves, providing critical constraints for earthquake source parameter inversion.

Based on gravity data, topographic information, and the Crust1.0 global Crustal structure model, this study employs discrete wavelet decomposition, analytical continuation, the Parker-Oldenburg iterative inversion algorithm, and gravity edge detection methods to investigate the characteristics of gravity anomalies and microplate tectonics in northeast Asia. This study divides northeast Asia into 15 microplates and classifies them into three categories including island arc type, cratonic type, and accretionary type. Since the Mesozoic, the tectonic and magmatic activities of the microplates in northeast Asia have been dominated by the subduction of the (Paleo-) Pacific plate, exhibiting significant spatial variations in crustal thickness. The Moho discontinuity is shallower in subduction zones, while it is deeper beneath orogenic belts and cratonic regions. Key tectonic boundaries such as the Mudanjiang suture zone and the Xinlin-Xiguitu suture zone archive the multi-stage amalgamation of microplates in northeast Asia. Both deep-seated geophysical anomalies and surficial magmatic activities collectively unravel the geodynamic linkage between (paleo-) Pacific subduction and the present-day tectonic architecture of northeast Asia.

To better understand the spatial heterogeneity of the lithospheric mechanical structure and its tectonic evolution in western Hubei and adjacent areas, this study calculates and analyzes the elastic lithosphere thickness (T_e) and deviatoric stress based on WGM2012 gravity field data, Crust1.0 model data, and ETOPO1 topographic data. The results indicate that: 1) The elastic lithosphere thickness exhibits significant spatial variation (5 to 50 km). The western margin of the Yangtze craton, including the Sichuan basin and Wuling overthrust (T_e>30 km), represents high-strength stable regions, whereas the Jiangnan orogenic belt (T_e < 20 km) constitutes a weak lithospheric zone; 2) The effective lithospheric thickness is negatively correlated with surface heat flow. The Wuling overthrust, as a cold lithospheric block, is characterized by low heat flow values (< 50 mW/m²) and thick T_e; 3) Seismic activity is controlled by the coupling of T_e and deviatoric stress. The western margin of the Sichuan basin exhibits low T_e values and a high deviatoric stress background (10 to 15 MPa), corresponding to earthquake clustering, whereas the Jiangnan orogenic belt, with low T_e and deviatoric stress generally below 3 MPa, experiences fewer seismic events. Regional seismic hazard assessment requires a comprehensive consideration of the coupling between elastic lithosphere thickness and deviatoric stress field.