Time-variable gravity field models provide critical data support for monitoring large-scale surface mass changes. However, discrepancies in accuracy exist among models released by different institutions. To fully utilize the advantages of multi-source time-variable gravity field models, this study focuses on weighted fusion methods, investigating various weighting strategies, including variance component estimation (VCE), ocean noise RMS-based weighting, and degree variance-based weighting (incorporating two reference models). Time-variable gravity field models released by eight institutions worldwide, including HUST, were selected for fusion experiments and accuracy assessment. The results indicate: 1) The choice of weighting schemes across different degrees/orders influences the VCE fusion outcomes. When truncated to degree 90, the optimal weighting orders are 70 for GRACE models and 60 for GRACE-FO models. 2) All four weighting fusion schemes enhance model accuracy. Compared to the Tongji model, the pre-filtering degree-variance fusion (using the GOCO06s reference model) reduces ocean noise RMS by up to 23%, while the post-filtering VCE fusion achieves a maximum reduction of 29%. 3) Over six major river basins, the terrestrial water storage trends derived from different time-variable models show good consistency, and the fusion models exhibit higher correlation coefficients with Mascon solutions.

To realize the refined modeling and real-time monitoring of the ionosphere in mid-low latitude regions and meet the engineering demand for high-precision correction of regional ionospheric delay, an ionospheric grid model (GZIM) with a temporal resolution of 1 hour by utilizing BDS/GNSS observation data from 75 continuous operating reference stations (CORS) in Guizhou. The ionospheric delay is extracted using the un-difference and un-combined algorithm, and a regional vertical total electron content (VTEC) model is constructed with the 4th-order spherical harmonic function. After eliminating data disturbed by geomagnetic storms, the spatiotemporal distribution characteristics of GZIM are compared with the global ionospheric model products released by IGS and CODE, and the model performance is verified through dynamic single-frequency precise point positioning (PPP). The experimental results show that GZIM is generally consistent with global ionospheric grid products in terms of temporal variation characteristics, and can depict the ionospheric TEC gradient at small scales more elaborately. In the dynamic single-frequency PPP experiments on geomagnetically quiet days, GZIM achieves higher ionospheric delay correction accuracy than global ionospheric products, with the root mean square (RMS) values of positioning errors being 8.5 cm in the east direction, 10.0 cm in the north direction, and 24.2 cm in the up direction, respectively. Specifically, the up-direction positioning accuracy is improved by 50.0% and 54.1% compared with IGSG and CODG global products, respectively.

Aiming at the significant multipath effect of GNSS at sea and the decline in observation data quality caused by various environmental factors, the performance of traditional Kalman filter algorithm is often significantly affected under such complex conditions. To improve the positioning accuracy in dynamic marine environment, we propose a robust Kalman filter baseline differential positioning model based on the IGGⅢ weighting function. Considering the marine platform has multiple receivers, distance constraint conditions are introduced to correct the filter solution, and the influence of constraint conditions on positioning results is analyzed. The model is verified through real measurement data. The results show that in long baseline differential positioning, the robust Kalman filter can effectively suppress gross error interference, and the positioning accuracy is significantly better than that of the traditional extended Kalman filter algorithm. After introducing distance constraints, the accuracy of positioning solution is further improved, verifying the effectiveness and practicality of this method in complex marine environments.

Polar motion (PM) is not only a critical parameter linking the terrestrial reference system (TRS) and the celestial reference system (CRS), but also an important physical basis for exploring processes such as material movement and angular momentum exchange in the Earth system. As a core parameter describing the Earth rotation movement, the precise PM determination is essential for aerospace activities, and also provides an important theoretical basis for understanding the complex dynamical processes of the Earth system, predicting climate change, and monitoring seismic activity. We review traditional optical measurements, space geodesy, and the emerging inertial measurement technology based on large ring laser gyroscope (RLG), evaluate their principles and progress, and discuss the advantages of multi-technique fusion measurement to provide a comprehensive reference for research and application in PM measurement. Among them, the RLG has emerged as a prominent technology due to their characteristics of direct, independent, and real-time measurement of rotation signals, enabling high-precision measurement of Earth rotation signals such as polar motion.

The large phase gradient disrupts the assumption of phase continuity, often leading to an underestimation of deformation, which has become one of the key challenges constraining the accurate phase unwrapping of PSDS-InSAR technology. Although phase unwrapping methods assisted by deep learning and external prior information can alleviate this issue to some extent, their performance is often limited by factors such as insufficient model generalization capability and difficulties in obtaining external information. To address the problem of large-gradient phase unwrapping in PSDS-InSAR, we propose a data self-assisted iterative three-dimensional phase unwrapping algorithm that does not require external information based on the stepwise three-dimensional unwrapping algorithm. This algorithm uses the preliminarily estimated deformation phase model as prior information, and iteratively estimates and separates the trend phase to gradually reduce the phase gradient, thereby achieving robust phase unwrapping relying on the inherent information of the dataset. Experimental results using real data demonstrate that the proposed algorithm can accurately recover large-gradient phase information, achieving deformation monitoring with high spatial coverage, high accuracy, and high robustness.

Static and dynamic performance tests were conducted on three Sino-G5 relative gravimeters at the Jiugong Mountain national gravity baseline field, and the test data was analyzed. The results demonstrate that the instrument static data exhibits favorable linear characteristics, with dynamic measurement accuracy consistently better than 10 μGal, and the consistency among multiple instruments is also excellent. This indicates that the performance of the Sino-G5 gravimeter meets the requirements for high-precision gravity measurements in regional geophysical exploration.

In this paper, high-precision normal mode detection is achieved by using the observation data of the superconducting gravimeter and combining the normal time-frequency transform (NTFT) and optimal sequence estimation (OSE) methods. Firstly, the instantaneous phases of each station were extracted through NTFT, and it was found that the instantaneous phases of each station were not the same. It was speculated that there was a non-synchronous oscillation phenomenon in the normal mode. When different quantities or different combinations of station data were used, the detection results of the OSE method showed significant differences, indicating that this phenomenon had an important impact on the detection accuracy of the OSE method. Subsequently, OSE experiments were conducted using stations with relatively consistent phases, and it was found that the detection frequency was close to the PREM theoretical value, indicating that the issue of phase consistency must be taken into account when applying the OSE method. Based on this, this paper proposes a high-precision normal mode detection method that integrates NTFT and OSE and takes into account phase consistency. Taking the split state detection of the ₀S₂ model as an example, the effectiveness of this method was verified, and it was also demonstrated that phase consistency is a key factor for the effective application of the date stacking method. This research provides a reliable data foundation and technical support for deeply revealing the deep structure of the Earth and optimizing the Earth model.

Based on the travel time data of 4 501 earthquakes recorded by 55 seismic stations of the Hubei seismic network from January 2008 to June 2024, the three-dimensional P-wave and S-wave velocity structures and earthquake relocation results of Hubei and its adjacent areas were obtained using the double-difference tomography method. The results show that earthquakes are mainly distributed in high P-wave and S-wave velocity zones and the transition zones between high and low velocity anomalies. The Three Gorges area is a cluster of seismicity, while earthquakes in other regions are generally distributed linearly along fault strikes. The P-wave and S-wave velocity distribution in the upper crust of the study area are basically consistent with the surface geological tectonic framework. The P-wave and S-wave velocity distribution in the middle and lower crust are roughly bounded by the north-south gravity lineament, with high velocity anomalies on the east side and low velocity anomalies on the west side, reflecting the differences in crustal composition and thickness across the lineament. The relatively low P-wave and S-wave velocity anomalies in the middle and lower crust of the Dabie orogenic belt may represent molten mantle materials intruding into the crust along the Tan-Lu fault zone.

To address the insufficient robustness of traditional micro-seismic source localization methods under noise interference, we propose a robust micro-seismic source localization framework that integrates genetic algorithm (GA) global search with CTCM-optimized K-means clustering. First, an optimization objective function is constructed based on the arrival time difference mathematical model for different four-by-four combinations of measurement points. The genetic algorithm is employed to traverse and solve these cubic quadratic functions with the unknown three-dimensional coordinates of seismic source as independent variables, generating many source approximate solutions. Second, the CTCM algorithm is introduced to optimize the initial K-means clustering centers. The sum of squared error (SSE) of clustering results is used as the dependent variable of optimization objective function, and the state transition correlation matrix is used to enhance the clustering stability of high-dimensional data. Finally, the optimized K-means yields multiple clustering centers. The robust localization strategy is constructed using the median of these center coordinates and the Mahalanobis distance of each point. The median absolute deviation (MAD) is applied to automatically remove outliers, and the weighted arithmetic mean of the remaining clustering centers is the final solution. The results show that GA-CTCM-K-means method achieves superior anomaly elimination and localization performance in one planar simulation case and two real micro-seismic cases, with localization accuracy reaching 9.524 3 m, 137.865 1 m, and 28.100 9 m, respectively, which is superior to traditional methods such as the Geiger method, simplex method, and source scanning method. This method represents a micro-seismic source localization scheme with practical application value.

To assess the performance of BDS-3 and promote its application optimization, this study processed long-term observation data from 56 global IGS stations and 50 reference stations in mainland China from DOY 001, 2023 to DOY 067, 2025 using the GAMIT/GLOBK 10.71 software. The long-term positioning accuracy of BDS-3 and GPS was compared and analyzed at both global and regional scales across four dimensions: ambiguity fixed rate, satellite orbit accuracy, coordinate time series, and velocity field. The results indicate that the average narrow-lane ambiguity fixed rate of BDS-3 in the global network (72.7%) is lower than that of GPS (88.1%), but it performs better in the regional network (BDS-3: 80.1%, GPS: 76.2%). In terms of satellite orbit accuracy, the orbit integration residuals of BDS-3 are on average 1.5—2.8 mm higher than those of GPS. Analysis of coordinate time series shows that the average WRMS values for BDS-3 in the N, E, and U directions (2.44 mm, 2.8 mm, 7.0 mm) are higher than those of GPS (1.7 mm, 1.8 mm, 5.7 mm), with greater dispersion in the vertical direction. However, comparison of the velocity fields reveals a high consistency between the two systems (90% of stations exhibit rate differences within 1 mm/a). The study demonstrates that the positioning performance of BDS-3 at the regional scale is close to that of GPS, but its global accuracy and stability still require improvement. Future efforts should focus on enhancing models, optimizing algorithms, and advancing research on multi-system integration.