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.

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.

To address the issues of reduced accuracy and misidentification of single water bodies caused by interpolation in large-scale flood inundation mapping using spaceborne GNSS-R technology, this paper proposes a dynamic monitoring method based on the reflectivity change rate using period-matched observation-based interpolation (POBI). Utilizing CYGNSS reflectivity data during the rainy seasons from 2019 to 2023 and China's 1 km resolution monthly precipitation products, we constructed a POBI spatial interpolation model. By combining this with the bistatic radar equation to retrieve surface reflectivity, we systematically analyzed the flood evolution process during the extreme rainfall event in Guilin in June 2024. The results show that, for reflectivity interpolation in unsampled areas, the root mean square error (RMSE) decreased by an average of 20.98% compared to the natural neighbor method. The inundation identification mechanism based on the change rate effectively avoids the subjectivity of traditional threshold methods. The monitoring results indicate that the inundated area in the central urban district expanded by 48.68% from June 19 to June 12, with spatial evolution highly consistent with changes in SMAP soil moisture. This study provides reliable methodological support for refined flood monitoring in highly dynamic, low-coverage areas using spaceborne GNSS-R.

A high-precision seismic dataset was constructed using deep learning methods, and a regional three-dimensional velocity model was obtained through double-difference tomography. The study reveals a correlation between surface velocity structures and geological features: mountainous areas generally exhibit high-velocity P-wave anomalies, whereas depression zones are characterized by low-velocity P-wave anomalies. This pattern may be attributed to the widespread presence of bedrock with relatively high wave velocity in folded regions, in contrast to the Quaternary sedimentary layers with lower velocity in depression areas. Seismic epicenters are densely distributed along the transition zones between high- and low-velocity anomalies. These transitional regions experience significant stress gradients, which facilitate rock fracturing and lead to heightened seismic activity.The Luxi uplift is characterized by several near-vertical high- and low-velocity blocks, with some earthquake epicenters also displaying steeply dipping distributions, suggesting the presence of high-angle faults. This structural configuration may result from multiple phases of compression and extension under the influence of Pacific Plate subduction and the tectonic activity of the Tan-Lu fault zone. These processes have led to cyclic uplift and subsidence in the region, during which progressive compression and extension of rock folds gradually increased their dip angles, forming near-vertical velocity blocks and high-angle faults.Integrated analysis of P- and S-wave velocities suggests the presence of mantle-derived basaltic magmatic intrusions beneath both the Nishan area and the northeastern region of the Lushan. In the Nishan area, the intrusions are predominantly plutonic, whereas in northeastern Lushan, mantle-derived basaltic magma likely reached the surface, forming ancient volcanoes and extrusive rocks.

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.

Based on over 20 years of MODIS satellite remote sensing infrared data, we extract brightness temperature low-frequency information using anomaly method and spatial anomaly superposition method, and investigate the temporal and spatial evolution and characteristics of infrared radiation anomalies before the Wenchuan M_S8.0 earthquake on May 12, 2008. The results show that there was a significant radiation enhancement anomaly before the earthquake. In terms of time, from 2006 to 2007, there was a trend of radiation enhancement in the core area located west of the epicenter. Conversely, from 2008 until the earthquake occurrence, this radiation enhancement exhibited a weakening trend. Spatially, the core area of radiation enhancement is located in the eastern section of Bayan Har and Qiangtang blocks, encompassing approximately 5.8×10⁵ km². The spatial distribution characteristics are consistent with the dynamic background of Wenchuan earthquake.

Based on the observation data of VP-type broadband vertical pendulum inclinometers from 5 stations in Inner Mongolia, this study adopted a method combining the wave propagation theoretical model and the deep learning prediction framework to preliminarily reveal the multi-frequency quantitative influence mechanism of the overburden thickness of observation caves on the observation signals. The results show that the overburden thickness is significantly negatively correlated with background noise (especially in the north-south direction), but has a weak impact on low-frequency signals. It exerts a directional regulatory effect on the amplitude of M₂ tidal waves: the amplitude of the north-south component increases linearly with the increase of thickness, while the east-west component shows a unique resonance amplification effect at the thickness of 20 m (the peak value is about 3.6 times higher than that at 10 m). When the thickness is greater than or equal to 20 m, it can effectively suppress the seasonal fluctuation of data, significantly reduce the variation amplitude, delay the peak value by 1-2 seasons, and simultaneously decrease the barometric admittance coefficient remarkably. Verified by the random forest model, it is found that the optimal thickness range of the cave overburden is 18-22 m (with 20 m as the optimal value). Lithology is a secondary influencing factor, while the overburden thickness and its interaction with the density of mountain lithologic media constitute the dominant control parameters. In this study, 20 m of overburden thickness was selected as the engineering threshold, which provides an important design basis for effectively suppressing background noise, ensuring the fidelity of tidal signals and resisting seasonal interference, and gradually promotes the construction of cave-based observation at stations from an empirical mode to a quantitative and engineering-oriented direction.

Landslide disasters pose a continuous threat to the safety of life and property of mountain residents. Especially under heavy rainfall, landslide areas are often accompanied by severe surface deformation, resulting in serious incoherence of synthetic aperture radar interference data, which restricts the deformation monitoring capability of traditional timing InSAR technology in this type of area. To address this problem, this paper takes the large landslide that occurred in Nanyu township, Zhouqu county, Gansu province in July 2018 as the research object, and prensents the distributed scatterer InSAR (DS-InSAR) deformation monitoring method based on statistical homogeneity detection and time phase link optimization. This method introduces a statistical homogeneity determination model under the traditional DS-InSAR framework, and identifies clusters of pixels with stable scattering characteristics through non-parametric testing, thereby expanding the monitoring coverage of low-coherence areas. At the same time, the shortest path constraint strategy is used to optimize the interferogram network structure to improve connection density and temporal coherence. It is also combined with a time-Phase Linking joint estimation algorithm to optimize the interference phase as a whole, enhancing the temporal continuity and stability of deformation information. The research results show that this method significantly improves the distribution density and temporal coherence of monitoring points in the collapse area of Nanyu landslide. The number of effective coherent points in creased from 23 232 to 43 463, an increase of about 87%, and the average posterior coherence coefficient was improved by 0.3, which effectively revealing the dynamic evolution characteristics of the landslide main body. The optimized annual average deformation rate chart shows that the maximum sliding rate of the main deformation area of the landslide exceeds 70 mm/a, and the timing curve of the characteristic point is highly consistent with the rainfall process. This study verifies the applicability and effectiveness of the timing phase optimization method in high-voltage landslide areas, and provides strong technical support for InSAR monitoring of landslides in complex mountainous areas.