The frequency band of oceanic microseismic peaks lies outside the velocity flat response range of borehole short-period seismometers. This study employs a transfer function to restore the oceanic microseismic peaks recorded by low-noise short-period seismometers and uses these restored peaks as the signal source for calculating the installation azimuth angles of borehole short-period seismometers. To investigate the impact of preprocessing parameters on the qualification rate of the calculated installation azimuth angles of downhole short-period seismometers, six sets of filters with different bandwidths were first applied to preprocess 24 hours of continuous data. The continuous data were then divided into five different time lengths. Subsequently, the installation azimuth angles of the borehole short-period seismometers were calculated using a correlation method. Finally, the installation azimuth angles with an average correlation coefficient greater than 0.995 for both the east-west (EW) and north-south (NS) components, and with an angle between the EW and NS components not exceeding 1.2°, were selected. The results show that the most qualified azimuth angles were obtained when the data were preprocessed with a filter bandwidth of 0.2 to 0.6 Hz and a duration of 300 s. Moreover, the number of qualified azimuth angles was significantly higher during the period of 19:00 to 06:00 than during 07:00 to 16:00. A comparison of the Earth's background noise before and after the correction of the installation azimuth angles of surface and borehole short-period seismometers revealed that the EW and NS components did not coincide before correction but were perfectly aligned after correction, thereby validating the effectiveness of using a filter bandwidth of 0.2 to 0.6 Hz and a duration of 300 s for azimuth angle calculation. This set of preprocessing parameters can also be applied to the azimuth correction calculation of borehole very broadband seismometers and the alignment error testing of surface seismometers.

A thorough understanding of the static drift characteristics of CG6 relative gravimeter is crucial for obtaining high-quality relative gravity observation data. We utilize 65-hours observation data from CG6-90 relative gravimeter and iGrav-56 superconducting gravimeter installed on adjacent site at the Sanya seismic station. Three calculation schemes are designed to determine the static drift rate of CG6-90 relative gravimeter. The results show that CG6-90 relative gravimeter exhibits a static linear drift rate of -8.861±0.035 μGal/h, and there is a nonlinear drift phenomenon with gravity variation ranging from -1 to 1 μGal. When calculating the static drift rate of CG6 relative gravimeter, it is essential to apply corrections for gravity changes induced by solid Earth tides, ocean tides, and atmospheric pressure variations.

In this paper, a system identification method based on the combination of GNA and ARMA is proposed to model the transfer function of the pendulum tilt instrument system. The GNA algorithm dynamically adjusts the step size to optimize the estimation of system parameters and the ARMA model models the noise characteristics to suppress random fluctuations. The system identification results are comprehensively analyzed. Compared with the standalone GNA algorithm, the integration of the ARMA model improves the fitting accuracy of the system transfer function from 92.96% to 95.53%, and reduces FPE from 115.9 to 47.28. This enhancement significantly strengthens the system's ability to suppress external noise. Moreover, the zero-pole distribution and residual analysis further validate the improved stability and reliability of the identified transfer function. This method enhances the accuracy and robustness of transfer function identification for the pendulum tilt instrument system, providing a solid foundation for its calibration and performance optimization.

This study employs the probability density distribution method to extract and analyze high-frequency anomalies from digital water level and temperature records preceding the 2016 Cangwu M5.4 earthquake in Guangxi. The findings reveal: 1) Approximately 18 months pre-earthquake, five monitoring wells exhibited distinct high-frequency water-level anomalies demonstrating significant temporal synchronicity; 2) During 1-2 months prior to the event, two wells displayed pronounced short-term high-frequency anomalies, likely indicating a dynamic transition from regional stress accumulation to impending instability; 3) Spatially, anomalous points clustered predominantly along the SW direction of the epicenter, aligning with regional seismotectonic features. Most anomalies showed high spatial concentration consistent with the NW-SE oriented regional compressive stress field. This research establishes a viable methodology for identifying precursory anomalies in fluid monitoring data before moderate-to-strong earthquakes in south China.

In order to better promote the application of time-varying measurement results of ambient noise wave velocity in earthquake tracking and earthquake prediction, this paper comprehensively processes the time series of direct Rayleigh wave travel time migration in 43 stations in western Yunnan in the past five years, and extracts the time-varying anomaly identification index (comprehensive index) of wave velocity suitable for M_S≥5.0 earthquakes in western Yunnan. Combined with the seven M_S≥5.0 earthquake events in western Yunnan during the study period, the R-value scoring method was used to evaluate the prediction efficiency of the comprehensive index. The results show that: 1) The comprehensive index has a good indication for the occurrence time of M_S≥5.0 earthquakes in western Yunnan since 2020. Under the premise of passing the significance test of 97.5% confidence level (R>R₀), the best prediction window length of the comprehensive index is 90 d. For the actual occurrence of 7 M_S≥5.0 earthquakes, the comprehensive index accurately predicts 7 earthquakes, omits 0 earthquakes, and falsely reports 5 earthquakes. 2) The corresponding relationship between the epicenter of M_S≥5.0 earthquakes in the study area and the distribution of abnormal stations before the earthquake (when the index appears) shows that the precursory behavior monitored by the time-varying measurement of ambient noise wave velocity should be more 'field precursor', rather than 'source precursor'. In the actual location prediction, it is necessary to combine the abnormal areas of seismic activity (such as empty areas and bands) and the abnormal areas obtained by field observations such as gravity and mobile geomagnetic to find possible earthquake locations, so as to improve the accuracy of location prediction.

This paper presents the theoretical relationships between two sets of precession models relative to MERS and GCRS, as well as a complete precession model that accounts for frame bias. The theoretical relationships between different precession angles are derived using vector methods, and two new expressions for the general precession in longitude are provided, one of which is suitable for long-term precession studies. Through numerical calculations, the spherical angle expressions between the north ecliptic pole at the reference and observation epochs and the GCRS polar axis are revised. It is pointed out that the P03 or IAU2006 precession models contain errors on the order of microarcseconds, indicating inconsistencies among different precession angles at the level of several tens of microarcseconds, necessitating a re-examination of the numerical calculations of various precession angles.

During the integrated navigation process, the traditional Sage-Husa adaptive filter often suffers from decreased filtering accuracy and even divergence due to external disturbances. To address these challenges, this paper proposes an improved Kalman filter-based integrated navigation algorithm using multiple fading factors. The algorithm dynamically adjusts the predicted state covariance matrix through multiple fading factors to suppress filtering anomalies in high-dynamic environments. Meanwhile, control factors are constructed based on the error characteristics of different state components and combined with the abnormal measurement noise detection conditions to correct the impact of abnormal noise on the integrated navigation system. Simulation results show that when applied to the GNSS/SINS integrated navigation system, the algorithm can accurately track the changes in measurement noise of each state component, effectively prevent filter divergence, and significantly enhance the system's stability and filtering accuracy.

Using 60 days of observational data from 233 monitoring stations of the crustal movement observation network of China (CMONOC) in 2023, the ionospheric delay modeling accuracy and standard point positioning (SPP) accuracy of BDGIM, Klobuchar, NeQuickG and BDSBAS models are analyzed. The results show that the BDSBAS ionospheric correction has the highest accuracy, with the ionospheric delay residual error of 1.0 m and an average correction rate of 87% within the region, and 68% of the 3D positioning errors reaching 1.95 m. The NeQuickG and BDGIM models have similar performance, with the ionospheric delay residual error of 2.0 m and an average correction rate of 73%, and positioning accuracy of 2.59 m and 2.82 m, respectively. The Klobuchar model has a correction rate of 67% and a positioning accuracy of 4.35 m. For single frequency users within China, the BDSBAS ionospheric model is recommended as the first choice, followed by the BDGIM model, which has a relatively simpler algorithm and better accuracy.

The tropospheric delay information of the enhanced precise point positioning (PPP) comes from a small regional reference station network with station spacing of several tens of kilometers, which has the disadvantages of limited service scope and high construction cost. To investigate the impact of wide-area tropospheric delay models with larger station spacing on PPP augmentation performance, observational data from IGS tracking stations in Europe were used to establish tropospheric delay models with average inter-station spacings of approximately 200 km, 260 km, 320 km, and 380 km. These models were then compared with the GPT3 model to evaluate and analyze PPP enhancement performance. The results indicate that the wide-area tropospheric delay models with inter-station spacings of 200 km, 260 km, and 320 km all reduced PPP convergence time to varying degrees, with particularly notable improvements in the height component and its maximum enhancement can reach 39.8%. However, the enhancement effect diminished as average station spacing increased. When the station spacing reached 380 km, the PPP augmentation performance was inferior to that of the global GPT3 model.

We conduct a comparative analysis of RL06.3 time-variable gravity field models released by CSR, GFZ, and JPL, focusing on the degree one, C₂₀ and C₃₀ and their surface mass anomalies, mean degree variances, signal-to-noise ratios of global surface mass changes, and terrestrial water storage changes in typical regions. The results show that degree one, C₂₀ and their surface mass anomalies of three models exhibit significant differences in trend but little differences in annual terms. For the C₃₀, the trend shows considerable differences, while the annual terms are largely consistent. The C₂₀ and C₃₀ of RL06.3 models from the same institution with different truncation degrees have little impact on the trend and annual terms, but the differences between institutions are notable, particularly in the trend. The mean degree variances of three models are highly consistent in low-degree terms, while CSR RL06.3 shows the most gradual rise in noise fitting curves for high-degree terms. The terrestrial water storage inversion results from three models tend to be consistent, but the CSR and JPL models exhibit superior performance in terms of inversion accuracy and consistency. Conversely, the GFZ model generally presents a higher degree of uncertainty in its inversion results. When inverting terrestrial water storage changes, if the uncertainty of results is disregarded, it is recommended to use the GRACE time-variable gravity field models with higher truncation orders released by CSR or JPL. Otherwise, it is advisable to use the GRACE time-variable gravity field models with lower truncation orders released by CSR.