15 January 2026, Volume 46 Issue 1
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Journal of Geodesy and Geodynamics. 2026, 46(1): 1-8. https://doi.org/10.14075/j.jgg.2024.12.560
In response to the issue that existing cycle slip detection methods lack systematic performance evaluation tools, this paper proposes a comprehensive evaluation approach that considers all epochs. By uniformly introducing cycle slips across all epochs, the method comprehensively reflects the true performance of different cycle slip detection algorithms over the entire observation period. Starting from three dimensions: combined noise, test statistic detection performance, and cycle slip detection results, three performance indicators are constructed: the root mean square error (RMSE) of the test statistic, the ratio of test statistic variation to RMSE, and the missed detection probability over all epochs. These indicators are used to systematically evaluate the performance differences of five cycle slip detection algorithms: Melbourne-Wübbena (MW), sliding window improved MW, triple-frequency pseudorange and phase, triple-frequency geometry-free (GF), and triple-frequency second-order time-difference phase ionospheric residual (STPIR). The results show that for all cycle slips within two weeks of the B1C/B2a/B3I signals, the triple-frequency GF cycle slip detection algorithm with a combination coefficient of (1, 2, -3) performs the best. Its test statistic RMSE is 0.006 2, the ratio of test statistic variation to RMSE is 100.56, and the missed detection probability over all epochs is only 7.28%. This performance evaluation method provides an objective quantitative basis for comparing the performance of cycle slip detection algorithms.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 9-16. https://doi.org/10.14075/j.jgg.2025.02.037
In urban environments, range measurement errors caused by non-line-of-sight (NLOS) signals significantly impact the precise positioning performance of global navigation satellite systems (GNSS), especially for low-cost devices and precise point positioning (PPP) that require accurate error processing. To address this issue, this paper designs a method for detecting NLOS signals based on the inertial navigation system (INS) and fisheye camera, and proposes an equivalent weight matrix update scheme for NLOS signals based on observation residuals to mitigate the adverse effects of NLOS signals on PPP. In the experiments, low-cost helical antennas and u-blox boards were used to collect two sets of vehicle-mounted data in Wuhan and Zhengzhou. The results show that the proportion of NLOS signal occurrences varies significantly across different scenarios, with tree shading and building blockage being the main factors causing NLOS signals. The optimized PPP achieves significant improvements in positioning accuracy and stability, especially in bridge-shaded and tree-shaded environments. The positioning errors were reduced from 21.259 m and 11.814 m to 17.071 m and 9.239 m, respectively, with improvement rates of 19.7% and 21.8%.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 17-25. https://doi.org/10.14075/j.jgg.2025.01.593
The BDS-3 and Galileo both provide five-frequency signal services. To fully utilize the multi-frequency signal data, a BDS-3/Galileo multi-frequency single point positioning (SPP) model based on the ionosphere-free combination is established, and the multi-frequency ionosphere-free combination coefficients are determined. Data from 48 multi-GNSS experiment (MGEX) stations are selected to systematically evaluate the performance of multi-frequency pseudorange SPP based on the ionosphere-free combination. The experimental results show that multi-frequency combined observations help to improve the SPP positioning accuracy of the BDS-3, Galileo, and BDS-3/Galileo systems. Compared with three-frequency SPP positioning, the positioning accuracy of four-frequency and five-frequency SPP has been improved to varying degrees. The improvement is most significant for the BDS-3 system. For the BDS-3 three-frequency SPP positioning, the RMS mean values in the E, N, U directions are 0.56 m, 0.59 m, 1.46 m, respectively, while the RMS mean values for four-frequency and five-frequency positioning are similar, at 0.53 m, 0.55 m, 1.40 m and 0.52 m, 0.55 m, 1.38 m, respectively. Compared with the single BDS-3 and Galileo systems, the five-frequency SPP positioning accuracy of the BDS-3/Galileo combined system has increased by 30.8%, 29.1%, 25.4% and 28.0%, 30.4%, 25.9%, respectively.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 26-33. https://doi.org/10.14075/j.jgg.2024.12.577
Traditional satellite orbit determination methods primarily rely on post-processing on the ground, which results in significant time delays and fails to meet the real-time requirements of autonomous on-board tasks. To address this issue, this paper proposes an autonomous real-time orbit determination algorithm based on the extended Kalman filter (EKF). The algorithm integrates quality control of observational data, dynamic orbit integration, and parameter estimation techniques to achieve real-time orbit determination for low-Earth orbit (LEO) satellites. The accuracy of the algorithm was assessed by comparing the real-time and predicted orbits with the ground-based post-processed precise reference orbits. Results show that, after accumulating 72 hours of observational data, the three-dimensional position error of real-time orbit determination was 15.18 cm, and the three-dimensional position error of orbit prediction within 2 hours was better than 18.75 cm, meeting the requirements for autonomous mission planning. Moreover, validated on an embedded platform, the algorithm has a single calculation cycle of less than 35 seconds and a memory footprint of less than 4 MB, complying with the onboard resource requirements. This study not only provides a theoretical and technical foundation for the design of autonomous navigation systems for medium and high Earth orbit satellites but also holds engineering value for enhancing the real-time response capabilities of satellites in orbit.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 34-39. https://doi.org/10.14075/j.jgg.2025.01.588
At present, the common detection method of integrated navigation system soft faults is to combine the sequential probability ratio test (SPRT) algorithm with the residual Chi-square detection algorithm. However, this algorithm has the defects of low detection accuracy and inability to detect multiple consecutive soft faults. Aiming at this problem, this paper first proposes an improved SPRT detection algorithm based on fading factor to reduce the influence of historical information on fault detection statistics and enhance the effect of current information on fault detection statistics. Then, the fault check statistic of the improved SPRT detection algorithm is designed, and combined with the residual Chi-square detection algorithm, an integrated navigation soft fault detection algorithm is proposed. Finally, based on GNSS/SINS integrated navigation system, this article has carried on the integrated navigation system soft failure detection simulation experiment.The experimental results show that compared with the classical algorithm, residual Chi-square detection algorithm, the improved algorithm not only has high precision and can accurately detect multiple consecutive soft faults, which can guarantee the stability of the integrated navigation system.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 40-47, 54. https://doi.org/10.14075/j.jgg.2025.02.041
We conduct a detailed analysis of the spatial configuration of Beidou-3 constellation, and construct a dynamic link constraint model from two aspects: satellite visibility and antenna directivity. We obtain the real satellite orbit parameters through two line elements (TLE) file, construct the Beidou-3 constellation based on STK, and comprehensively analyze the topology characteristics of Beidou inter-satellite link. The simulation results have important guiding significance for further completing the link budget, achieving autonomous orbit determination and time synchronization between satellites.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 48-54. https://doi.org/10.14075/j.jgg.2025.02.034
This paper employs the spherical radial basis function (SRBF) method to integrate terrestrial gravity and airborne gravity data for the computation of the gravity quasi-geoid. Experiments are conducted from aspects such as terrain effects, model order, and basis function parameters to investigate the impact and variation patterns of different parameters.The results show that: 1) The burial depth, effective distance, and expansion order of the basis function all affect the quality of the quasi-geoid refined by the SRBF method. The precision difference of the geoidal height anomalies corresponding to different parameter settings can reach up to 2.4 cm; 2) Considering the terrain effects when calculating residual gravity data can improve the final quasi-geoid precision by 21.6%; 3) Using two sets of GNSS/leveling points with different precisions and distributions for external validation of the quasi-geoid results, the best validation precision differs by 4.3 cm; 4) In mountainous areas, by optimizing parameters to integrate terrestrial and airborne data, the optimal precision of the quasi-geoid result can reach 2.7 cm.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 55-62, 85. https://doi.org/10.14075/j.jgg.2025.02.038
Heat flow (HF) refers to the heat energy transmitted from the Earth's interior to the surface. It can reveal various processes occurring in the deep Earth and information about energy balance. In the Antarctic region, understanding heat flow is of great significance for simulating the dynamic changes of ice sheets. This study employs the Stacking algorithm in machine learning to construct a heat flow prediction model for Antarctica. The model integrates 13 types of geological and geophysical features related to heat flow as observational input data and incorporates six machine learning algorithms commonly used for regression prediction problems, namely GBDT, XGBoost, RF, LightGBM, ET, and MLP, to predict the distribution characteristics of heat flow. The experimental results show that the prediction accuracy of the Stacking model is superior to that of several benchmark models. The new Antarctic heat flow distribution prediction map obtained through this model is more in line with the actual distribution of heat flow in Antarctica compared with the large-scale estimated heat flow distribution maps drawn by traditional methods, demonstrating more excellent performance.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 63-67. https://doi.org/10.14075/j.jgg.2025.02.035
We select CB and MB superconducting gravimeter (SG) stations with longer data length (2004-2020) and better observation quality, and use the oceanic Ni1o index (ONI) as the El Ni1o-southern oscillation (ENSO) index to assess the possible impact on SG observations at the interannual timescale (primarily 1-5 a). First, we remove the influences of instrument response, atmospheric tides, tidal effects, hydrological factors, and polar motion from the CB and MB observation series. Then, we apply the Morlet wavelet transform and cross-wavelet analysis to the data. The results show there is a significant correlation between SG observations and ONI, primarily at periods of approximately 1.5 a and 3 a. Specifically, gravity variations at CB station exhibit a negative correlation with ONI, while at MB station, the correlation is positive, with a time delay in the gravity observation relative to ONI. Furthermore, the ENSO effect on CB station is more pronounced than that on MB station, especially at about 3 a period, where ENSO shows a negative correlation with gravity variations at CB station. We hypothesize that this discrepancy is largely due to the different geographical locations of stations. However, more detailed quantitative studies are needed to further explore this relationship.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 68-77. https://doi.org/10.14075/j.jgg.2024.12.596
This paper uses historical landslide and hydrogeological data from Anhui province as the original dataset, employing five typical machine learning models and selects nine environmental disaster-inducing factors as initial input variables. To explore the impact of different machine learning input variable selection combinations on the performance of various models, multiple factor selection methods and several evaluation metrics are used to examine the prediction accuracy of each algorithm model under different selection combinations for landslide susceptibility. The evaluation results show that the combination of the full selection set and the lightweight gradient boosting machine (LightGBM). model yields the best performance. Therefore, this combination is chosen for the landslide susceptibility mapping in the study area. Moreover, the study suggests that factor selection for machine learning inputs does not necessarily improve model performance in landslide susceptibility studies of the research area. Additionally, ensemble models outperform individual estimator models, and LightGBM, using a leaf-node-based decision tree algorithm, shows improved performance compared to other ensemble algorithms.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 78-85. https://doi.org/10.14075/j.jgg.2025.01.002
Based on the seismic monitoring data from the Ganzhou Earthquake Monitoring Center Station of the Jiangxi Seismological Agency, real-time monitoring of blasting vibrations from different locations and explosive quantities at the Ganzhou Tieshanlong Tungsten Mine was conducted from multiple directions and long distances. The microseismic signals generated by blasting were obtained and analyzed to identify the propagation patterns of blasting vibration signals in medium and far blasting zones. Considering the influence of complex environmental conditions on blasting vibration signals, a Butterworth filter was used to eliminate trend components, and a combination of wavelet denoising and HHT denoising was applied to analyze the time-frequency characteristics, frequency band energy characteristics, and the impact of terrestrial environment on the spectral energy propagation of microseismic signals under different monitoring base stations and different blasting events. The study shows that the Sadovsky empirical formula is still applicable to the propagation patterns of blasting vibration signals in the medium and far blasting zones (32-120 km), where: 1) in the medium and far blasting zones, signal energy is highly concentrated in the low-frequency band and shifts downward with increasing distance from the blast center (R); 2) when the distance from the blast center is fixed, signal energy increases significantly with the increase in explosive quantity (Q), and the concentrated frequency band fluctuates within a normal range; 3) the attenuation of blasting vibration signals is also affected by the surface environment along the propagation path.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 86-93. https://doi.org/10.14075/j.jgg.2025.01.011
A post-earthquake affected population prediction model based on support vector machines(SVM) optimized by particle swarm optimization(PSO) is established, and the SST(safety stock theory) earthquake emergency supply demand prediction model is constructed. Nine indicator parameters, including seismic hazard and damage severity, are selected and processed through dimensionality reduction and redundancy removal as input variables for the PSO-optimized SVM model to predict the affected population. Based on the relationship between the affected population and emergency supplies in disaster areas, the SST model is applied to indirectly estimate the quantities of typical supplies required in the immediate aftermath of the Jiuzhaigou earthquake. The experimental results are as follows: By employing an error comparison analysis method to validate the model's effectiveness, the PSO-SVM model demonstrates a 14.27% reduction in prediction error compared to the SVM model, with a significant improvement in prediction accuracy. The estimated demand for typical supplies in the aftermath of the Jiuzhaigou earthquake provides a certain degree of reference, indicating that the PSO-SVM-SST prediction model possesses both theoretical and practical rationality and utility.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 94-105, 114. https://doi.org/10.14075/j.jgg.2024.12.569
This paper reviews and summarizes traditional estimation methods, highlighting their reliance solelyon observed seismic activity data. If the return period of the maximum earthquake exceeds the data coverage period or seismic activity rates fluctuate over longer timescales, these methods become ineffective. Furthermore, in the absence of physical constraints, traditional approaches may predict unbounded growth in the maximum event magnitude as the temporal scope expands.To address these limitations, this study introduces a new model based on seismic moment conservation. This model integrates instrumental records, fault slip rates, fault geometry, and seismic activity patterns, assuming that seismicity follows the Gutenberg-Richter(G-R) law. By simulating missing large earthquakes and their aftershocks, it constructs a long-term seismic catalog and imposes physical constraints on the maximum magnitude using the seismic moment accumulation rate, effectively mitigating the reliance of traditional methods on observational periods.The model is applied to predict the Mmax and return period of the Aerjin fault zone, the Kunlun fault zone, and the Longmenshan fault zone. The results show high consistency with historical earthquake records, paleoearthquake data, and observed slip rates, validating the model's efficacy. This research offers a new perspective on maximum magnitude estimation and provides a valuable tool for seismic hazard assessment in regions with incomplete historical records.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 106-114. https://doi.org/10.14075/j.jgg.2025.01.603
Based on the statistical analysis, correlation fitting and preprocessing of high-precision continuous observation data of four trace hydrogen observation points in fault zones of northwestern Yunnan over the past five years, we evaluate the seismic prediction efficiency of observational data, and analyze the characteristics and causes of trace hydrogen concentration anomalies related to earthquakes combining with previous studies. The results show that: 1) The concentration variations of hydrogen escaping from hot springs in northwestern Yunnan are correlated with temperature and air pressure, showing distinct annual variation features. In contrast, hydrogen escaping from soil is essentially uncorrelated with meteorological elements. Comparison with previous studies, there is a significant spatial heterogeneity in the distribution of trace hydrogen concentration in fault zones, which is not controlled by a single factor. 2) All of four trace hydrogen observation points in northwestern Yunnan are effective seismic prediction indicators with statistical significance. The short-term prediction effect of hydrogen escaping from soil is better but with relatively lower credibility. The prediction efficiency of hydrogen escaping from hot springs is mainly in medium to long term, with relatively higher credibility. 3) There is a close correlation between hydrogen anomalies in fault zones and seismic activities in northwestern Yunnan. Seismic precursory anomalies typically manifest as sudden increases or sawtooth fluctuations. Due to differences in hydrogen sources, escaping processes, and geothermal storage temperatures in different regions, the geochemical background values of hydrogen escaping from hot springs and soil also exhibit significant spatial heterogeneity.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 115-122, 130. https://doi.org/10.14075/j.jgg.2025.01.591
Based on the observational data of two types of borehole strainmeters at Xuzhou seismic station, we use the R-value test method to calculate and score the forecasting efficiency of trend-breakinganomalies, annual breaking anomalies and rate anomalies recorded by the two instruments. A systematic comparative analysis of the forecasting efficiency of the two borehole strain instruments at the same site is conducted to clarify their complementarity in earthquake precursor monitoring. The results show that the borehole body strainmeter performs well in the forecasting of trend-breaking anomalies and annual breaking anomalies, and is suitable for capturing the earthquake precursor anomaly signals related to long-term geological tectonic activities. The borehole component strainmeter shows a strong ability in the forecasting of trend-breaking anomalies and rate anomalies, and is more adept at capturing the trend-breaking precursor anomaly signals caused by stress state changes of geological body, and has high sensitivity for the monitoring of rapid changes in regional stress and strain. These findings reveal the efficiency differences of borehole strainmeters in earthquake precursor monitoring and provide the application focus and optimization direction of the two types of borehole strainmeters in earthquake forecasting.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 123-130. https://doi.org/10.14075/j.jgg.2024.12.555
Based on ground-based single-antenna GNSS receivers, the global navigation satellite system-interferometric reflectometry(GNSS-IR) technique can utilize the signal-to-noise ratio(SNR) to invert the vertical distance between the reflective surface and the antenna, thereby estimating water level heights. To reduce equipment costs and enhance the accessibility of GNSS-IR technology, while fully leveraging existing deformation monitoring GNSS stations, this study proposes the use of universal BDS/GNSS receivers for monitoring the structural deformation of large bridges. By integrating quality control, error correction models, and result fusion methods, effective monitoring of water level changes beneath bridges is achieved. Processing data from two universal BDS/GNSS receivers installed on the main cable of the Guojiatuo Changjiang bridge in Chongqing, which are used for monitoring bridge structural deformation, the study results indicate that the GNSS-IR water level time series, after quality control, error correction, and fusion processing, are highly consistent with the data trends from traditional hydrological stations, with a correlation coefficient reaching 0.99. This outcome not only validates the feasibility of this method in monitoring water level changes beneath large bridges but also demonstrates the broad application prospects of universal BDS/GNSS receivers in river water level monitoring, providing robust data support for flood early warning and water resource assessment.
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Journal of Geodesy and Geodynamics. 2026, 46(1): 131-138. https://doi.org/10.14075/j.jgg.2025.01.601
We quantify surface water storage anomalies using GRACE satellite Mascon data and hydrological model data combining with water level and area data, estimate groundwater storage anomalies in Dongting Lake basin based on water balance equation, analyze the spatiotemporal variation characteristics and influencing factors of groundwater storage anomalies. The results show that after incorporating changes in surface water storage, the correlation between the inverted groundwater storage anomalies at the lake scale and the observed groundwater data improved from 0.40 to 0.65, reaching 0.75 in areas with dense groundwater wells. The overall groundwater storage anomalies in Dongting Lake basin show a downward trend, with a loss rate of-5.05 mm/a. Spatially, there is a significant difference between the northern and southern areas. Precipitation influences groundwater storage anomalies through its impact on surface water, with some lag due to hydrological processes, while human activities have a limited effect. This study reduces uncertainties generated in separating surface water storage, providing data support for groundwater dynamic research and water resource management in Dongting Lake basin.
