Taking the March 28, 2025, M_S7.9 earthquake in Myanmar as an example, this study systematically analyzes the coseismic displacement and the resulting permanent displacement of the earthquake using 1 Hz high-frequency observation data from 21 GNSS reference stations in Yunnan, China, near the epicenter, based on the precise point positioning ambiguity resolution (PPPAR) method. The results indicate that the mean standard deviations of multi-system GNSS before the earthquake in the N (north), E (east), and U (up) directions are 0.7 cm, 1.1 cm, and 2.0 cm, respectively, representing accuracy improvements of 36.3%, 15.4%, and 39.4% compared to single-system GNSS. After the earthquake, high-frequency GNSS near the epicenter can detect distinct coseismic displacement waveforms, with the YNRL station, closest to the epicenter, showing a maximum peak change of 10.0 cm in the N direction and 6.2 cm in the E direction. The earthquake magnitude in Myanmar, calculated based on GNSS coseismic displacement results, is M_S7.8, which is generally consistent with the officially released magnitude of M_S7.9. Additionally, calculations reveal that multiple GNSS stations exhibit significant permanent displacements due to the earthquake, with the maximum displacement exceeding 2.0 cm. This study provides reference and data support for the application of high-frequency GNSS in monitoring coseismic displacement and analyzing earthquake magnitudes.

On January 7, 2025, an earthquake of M_S6.8 occurred in Dingri, Xizang, which produced a surface deformation zone about 35 km long. By means of high-resolution image interpretation, field geological survey and aerial photogrammetry of unmanned aerial vehicle, it is found that the northern part of the surface deformation zone of the Dingri earthquake is mainly a normal fault seismic fracture zone with vertical coseismic dislocation of 2 m to 3 m, and secondary rupture development of the nature of the strike-slip is also visible. However, the southern part of the deformation zone presents complex deformation characteristics, including both tensile deformation and extrusion deformation. Among them, the tensile deformation is mainly located on the west side of the linear fault scarp of the SN-trending Dengmocuo fault, and the deformation zone with the same strike as the fault may be a seismic surface fracture zone with structural significance. while the extrusion deformation zone is mainly distributed at the eastern boundary of lake-eroded shoal, located on the west side of the tensile deformation zone, with different directions. It is speculated that under a westward thrusting force, the surface frozen layer, as a rigid slider, slipped along the underlying saturated aquifer, cracked into multiple blocks, slipped westward and collided with each other, forming a multi-directional extrusion deformation zone.

Based on a two-dimensional viscoelastic spherical Earth model, this study constrains the mantle viscosity structure beneath the Sumatra region using GRACE RL06 satellite data and the VISCO2.5D code. The aim is to reveal the spatiotemporal characteristics of co- and post-seismic gravity changes induced by the 2004 Sumatra M_W9.3 earthquake and to analyze the lateral variation information of mantle viscosity contained within the gravity signals. GRACE satellite gravity observations were processed through filtering and smoothing to isolate the co- and post-seismic gravity change signals associated with the 2004 event. The extracted signal exhibits a negative-positive-negative pattern from southwest to northeast (Indian ocean-Sumatra-Malaysia). During the observation period from 2005 to 2012, the magnitude of post-seismic gravity change was comparable to the co-seismic change, with a maximum negative change of -3.7 μGal and a maximum positive change of 8.3 μGal. Considering the influence of the subducting slab near the trench, the observed large-scale co-and post-seismic gravity changes were simulated using the 2D viscoelastic Earth model and the spectral-element method in VISCO2.5D. The results show that when the mantle viscosity coefficients are set to 2×10¹⁹ Pa·s on the oceanic side and 1×10¹⁹ Pa·s on the continental side of the seismogenic fault, with a subducting slab near the trench and an elastic lithospheric thickness of 60 km, the simulated post-seismic gravity changes closely match the GRACE observations. This indicates that the post-seismic gravity changes in the Sumatra region are primarily driven by mantle viscoelastic relaxation, with a negligible contribution from aftershock effects.

By employing fundamental methods from the theory of curves and surfaces, the concepts of different types of latitude and longitude (reduced latitude and longitude, geometric latitude and longitude, and geodetic latitude and longitude) at an arbitrary point on a triaxial ellipsoidal surface are proposed. Theoretical relationship models are established between Cartesian coordinates (spatial rectangular coordinates) and geodetic coordinates (geodetic latitude, longitude, and height), as well as between Cartesian coordinates and geometric coordinates (geometric latitude, longitude, and height), for any point in space both inside and outside the ellipsoid. Based on the theoretical relationships among the different latitude and longitude types, approximate geodetic coordinates of the study point are derived. Using these approximations as initial values, a novel method for the inverse transformation of coordinates is presented via Newton's iterative approach. Extensive numerical calculations demonstrate that, for any arbitrary point in space, the proposed method accomplishes the transformation from Cartesian to geodetic coordinates nearly instantaneously; even for points near the Earth's surface, only three iterations are required to achieve convergence.

Based on the methods of low Earth orbit (LEO) satellite orbit determination and inter-satellite single-difference ambiguity resolution, this paper utilizes uncalibrated phase delay (UPD) and observable-specific signal bias (OSB) products to achieve precise orbit determination for eight LEO satellites, namely GRACE-C/D, SWARM-A/B/C, and SENTINEL-3A/3B/6A, and evaluates the impact of these two products on orbit determination accuracy. The results indicate that both products achieve a nearly 100% fixed rate for wide-lane ambiguities; except for the GRACE satellites, which have a narrow-lane fixed rate of approximately 85%, the remaining satellites achieve rates close to 95%. Ambiguity resolution significantly enhances orbit determination accuracy, with the fixed solutions obtained using UPD and OSB products showing comparable three-dimensional accuracy, reaching 1.5 cm, 1.7 cm, and 0.9 cm for the GRACE-FO, SWARM, and SENTINEL series satellites, respectively, representing improvements of up to 50% compared to the float solutions. Additionally, a multi-system analysis of the SENTINEL-6A satellite reveals that the fixed solution accuracy for the combined GPS/Galileo orbit determination is 0.9 cm, representing improvements of 10% and 18% compared to single-GPS and single-Galileo systems, respectively. This study confirms that ambiguity resolution can effectively improve the orbit determination accuracy of LEO satellites and provides a reference for future research on orbit determination for large-scale LEO constellations.

To systematically evaluate the accuracy performance of the next-generation VLBI global observing system (VGOS) in determining UT1-UTC, we process synchronous observation data from the 2024 VGOS-INT-A and traditional S/X-band IVS-INT-1 sessions using identical strategies, with the international IERS C04 series serving as the reference for accuracy assessment. The results indicate that, under identical station counts and geometric configurations, the number of observations per session for VGOS-INT-A (approximately 65) is significantly greater than that for IVS-INT-1 (approximately 30). The weighted root mean square (WRMS) of the post-fit delay residuals for both systems is comparable. Regarding UT1-UTC solution accuracy, VGOS-INT-A demonstrates a clear advantage over IVS-INT-1, the RMS of its deviation with the C04 series is ±0.046 ms, superior to the ±0.060 ms of the latter. Its mean formal error is 14.26 μs, while that of IVS-INT-1 is 30.38 μs. Furthermore, preliminary analysis of data from China's VGOS stations shows good formal error (22.11 μs) and high stability in the solutions, although external agreement accuracy still lags behind international levels. This research provides quantitative evidence for the precision efficacy of VGOS technology in practical applications and highlights its potential for high-frequency Earth orientation parameter monitoring.

In this study, 1 216 consecutive days of ESA precise clock products (January 2022 to April 2025) were analyzed by combining clock-fit parameters, frequency-stability metrics, and spectral analysis to assess the long-term behavior of the onboard clocks. Results show that E14 experienced three phase jumps and two frequency jumps during this period, more frequently than nominal circular-orbit satellites. Following the frequency jump in April 2024, its frequency drift and fitting accuracy deteriorated markedly: residual peaks exceeded 10 ns, and an anomalous periodicity of about 0.63 h emerged with increasing amplitude and no evident secondary component. The frequency stability simultaneously worsened, showing oscillatory behavior near 100 s. After another frequency jump in March 2025, both residuals and stability improved, with stability reaching 2.50×10^-13, 6.52×10^-14, and 9.70×10^-14 at 100 s, 1 000 s, and 10 000 s, respectively. No dominant periodic term was observed afterward, but the drift exhibited a systematic bias, and a "dip" feature appeared near 4 000 s. In contrast, the co-orbital E18 satellite maintained stable performance throughout the same period, with fitting residuals confined within ±0.2 ns, no significant high-frequency periodic components, and frequency stability comparable to that of nominal MEO satellites, showing no anomalous behavior.

The synchronization accuracy of the pseudo-range single-point positioning method is low, making it unable to meet the high-precision time synchronization requirements of low Earth orbit (LEO) satellites. Meanwhile, the inter-satellite link time synchronization method faces challenges in large-scale LEO constellation applications due to factors such as complex payloads, high equipment costs, and susceptibility to interference from the space environment.This study utilizes onboard GPS observation data from the GRACE-FO satellites to design and investigate an inter-satellite time synchronization method for low Earth orbit satellites based on precise point positioning (PPP). Experimental results show that the orbit determination accuracy (RMS) of the GRACE-FO satellites in all directions is approximately 7 cm, and the GNSS timing accuracy (STD) of the two satellites is 0.78 ns and 0.77 ns, respectively, with short-term stability (1 280 s) reaching 2.22×10^-13 and 2.13×10^-13, and long-term stability (10 240 s) reaching 2.69×10^-14 and 3.21×10^-14. Meanwhile, the inter-satellite time synchronization accuracy (STD) is 0.46 ns, with short-term stability (1 280 s) of 2.24×10^-13 and long-term stability (10 240 s) of 3.47×10^-14. These results validate the feasibility of the proposed algorithm and provide an effective approach for high-precision inter-satellite time synchronization in LEO satellite systems.

Aiming at the limitation of real/near-real-time GNSS PWV retrieval under missing meteorological parameters, three PWV estimation models without the need for measured meteorological parameters were established in the Guangxi region based on the XGBoost model. First, the XGBZ-PWV model was developed with inputs including station time (DOY and HOD), location (Longitude, Latitude, and Height), and GNSS ZTD, and the output feature being GNSS PWV. Then, based on the XGBZ-PWV model, two empirical PWV values were incorporated to establish the XGBZG-PWV and XGBZE-PWV models, respectively. For comparison, the GPT3 model was used to provide pressure and temperature for PWV retrieval based on GNSS ZTD (GPT3-PWV model). The accuracy of the established models was validated using GNSS PWV retrieved from GNSS ZTD, ERA5 surface pressure, and temperature in the Guangxi region in 2022 as the reference value. The results show that, compared to the GPT3-PWV model, the estimation accuracy of the XGBZ-PWV, XGBZG-PWV, and XGBZE-PWV models improved by 22.98%, 29.03%, and 31.45%, respectively, with the XGBZE-PWV model performing the best. During two extreme rainfall events in 2022, the spatiotemporal evolution characteristics of PWV and rainfall were analyzed. The results demonstrate that the XGBZE-PWV model maintains good applicability even under extreme weather conditions.

Far-field earthquake triggering mechanisms represent a critical frontier in earthquake prediction research. To explore the spatiotemporal relationship between strong seismic activity in Japan and moderate-to-strong earthquakes in northeast China, this study develops a data-driven prediction model. Based on the USGS earthquake catalog from 1980 to 2024, and using M≥6.0 earthquakes in Japan as potential triggers, the model predicts the probability of earthquakes with M≥4 occurring in northeast China within the next 60 days. A daily-resolution time series dataset is constructed, incorporating 32-dimensional features covering earthquake statistics, spatial distribution, energy release, and aftershock sequences. Deep learning models including long short-term memory (LSTM) neural networks, attention mechanism-enhanced LSTM (Attention-LSTM), and Transformer are systematically compared against traditional machine learning methods such as logistic regression and random forest. Results indicate that the Attention-LSTM model performs optimally, achieving an F1 score of 0.915 and an AUC value of 0.661, and shows significant advantages in Molchan error diagram analysis, a method specific to earthquake prediction evaluation. The model enables both binary classification prediction and generation of spatial probability distribution maps at 1°×1° grid resolution. This study demonstrates the potential of deep learning for revealing cross-regional far-field earthquake triggering mechanisms and provides new insights for short-to-medium-term regional seismic hazard assessment.