Geodesy and Geodynamics最新文献

In this paper, we make a statistical analysis of the fault information of the underground fluid instruments of 12 models in China from January 2021 to May 2022 based on the Pearson correlation coefficient, and compare the fault statistics of the meteorological three-element instruments of 3 models during the study period. The results show that: (1) The numbers of faults of the underground fluid instruments of 12 models with different service times are basically positively correlated with the numbers of the corresponding instruments, with good consistency. Moreover, the automatic observation instruments (8 models) with more than 30 units are significantly correlated at a 0.05 significance level (95% confidence level). Even at a 0.01 significance level (99% confidence level), there are 7 models (7/8) with significant correlation. (2) The positive and negative correlations between the monthly average number of faults and the corresponding service times of the underground fluid instruments of 12 models with different service times are random, and there are 9 models (75%) with no significant correlation at a 0.05 significance level (95% confidence level), while 12 models (100%) with no significant correlation at a 0.01 significance level (99% confidence level). (3) The monthly average numbers of faults of the underground fluid instruments of 12 models are basically 0.02–0.05 times/(unit·month), and the overall fault frequency is low. (4) The fault statistics results of the meteorological three-element instruments of 3 models are consistent with the characteristics of the underground fluid instruments of 12 models. In general, there is no significant correlation between the fault frequency and the service time of underground fluid instruments. (5) The results of this paper demonstrate that the service time of underground fluid instruments cannot be taken as the main reason for whether to update the instruments. Similarly, the fault frequency of the instruments cannot be taken as the main reason for the service life of the instruments in the process of formulating the service life standards of underground fluid instruments.

The Palu M_W7.4 earthquake occurred on September 28, 2018, with the epicenter at 119.86°, 0.72°. The severe shaking caused severe damage in Central Sulawesi, especially in Palu. We conducted a postseismic deformation study to determine the deformation pattern and reduce future earthquakes' impact. Interferometric Synthetic Aperture Radar (InSAR) data were processed using LiCSBAS to get the time series. The time series data were fitted to exponential and logarithmic functions to determine the mechanism of postseismic deformation. The exponential model identified the influence of the viscoelastic mechanism, and the logarithm identified the afterslip mechanism. The Palu earthquake was fitted to logarithmic and exponential, but the logarithmic was more significant than an exponential function. Afterslip mechanism predominates, and viscoelastic mechanisms play a minor role in this postseismic deformation.

Three A10 absolute gravimeters (AGs) were first acquired in China by the Hubei Earthquake Agency under the Belt and Road Seismic Monitoring Network Project. Although AG measuring and testing technique is not new, the purchase and simultaneous testing of 3 A10 absolute gravimeters is unprecedented in China. This study conducted the first acceptance testing of the AGs at 3 locations (the Jiufengshan Gravity Observation Station, the Global Navigation Satellite System Observation Station in Wuhan, and the Jiugongshan Observation Station in Xianning). The results were compared using a method based on expert validation, and the acceptance testing scheme was formulated by referring to the Technical Regulations for Tectonic Environment Monitoring Networks in China and Specifications for Gravimetry Control. Based on the repeatability, precision, and consistency of the measured g values, the results from each instrument were evaluated using the air pressure precision test. Comparing the instrument reference values, the final test results can identify the indicator parameters for 3 A10 AGs, the effects of the surrounding environment, and the related parameters on measurement precision. The precision of A10-059, A10-058, and A10-057 exceeded 0.78 μGal, 0.79 μGal, and 0.42 μGal, respectively. This testing scheme can be used as a reference for conducting acceptance testing of AGs in the future and obtaining absolute gravimetric measurements.

Based on the relative and absolute gravity measurements in the southern South-North Seismic Belt since 2015, we analyzed the dynamic change of the regional gravity field and its relationship with the Yangbi M_S6.4 earthquake that occurred on May 21, 2021. The results show that: 1) The regional gravity field changes are closely related to the Weixi-Qiaohou fault, which reflects the surface gravity field changes caused by the fault activity from 2015 to 2021; 2) The gravity field change related to the preparation of Yangbi earthquake has experienced the evolution process of " steady state - regional gravity anomaly - local gravity anomaly - four-quadrant distribution - large area positive anomaly - earthquake occurring in the reverse change process "; 3) The cumulative and differential change images of the gravity field show that there were significant gravity changes in the two years preceding the Yangbi earthquake, and the earthquake occurred in the high-gradient belt of gravity variation, the center of the four-quadrant, and close to the zero contour turn; 4) The dynamic evolution image of the gravity field can well reflect the precursory phenomena during the preparation for the Yangbi M_S6.4 earthquake. Based on the anomaly change of mobile gravity, a certain degree of medium-term prediction was made before the Yangbi M_S6.4 earthquake, especially the determination of strong earthquake location.

Islands and the mainland are separated by seas, and the distances between them might be so long that the height on the mainland cannot be exactly translated to the islands, resulting in different height systems on the mainland and the islands. In this study, we used astrogeodetic deflections of the vertical and ellipsoidal heights of points on the mainland and island near their coastlines to implement height connection across sea areas. First, the modeled gravity and modeled astrogeodetic vertical deflections of segmentation points along connecting routes over the sea between the mainland and the island were determined by Earth Gravity Model (EGM), and the ellipsoidal heights of segmentation points were determined by the satellite altimetry data sets. Second, we used a linear interpolation model to increase the precision of the vertical deflections of segmentation points. Third, we computed the geopotential difference of points between the mainland and the island using a method derived from geopotential theory and the astronomical leveling principle. Finally, we estimated the normal height of the point on the island using the geopotential-difference iterative computation approach. Using observed data of normal heights, ellipsoidal heights, and astrogeodetic vertical deflections referring to height sites in Qingdao, Shandong Province, we conducted a numerical experiment involving the normal height connection across sea regions. We determined the data of the ellipsoidal heights and gravity of segmentation points along the connecting route across the water in the numerical experiment using DTU10. The distance of the height connection across the sea was approximately 10.5 km. According to China's official leveling specifications, the experimental results met the criterion of third-class leveling precision.

The gravity and magnetic data can be adopted to interpret the internal structure of the Earth. To improve the calculation efficiency during the inversion process and the accuracy and reliability of the reconstructed physical property models, the triple strategy is adopted in this paper to develop a fast cross-gradient joint inversion for gravity and magnetic data. The cross-gradient constraint contains solving the gradients of the physical property models and performing the cross-product calculation of their gradients. The sparse matrices are first obtained by calculating the gradients of the physical property models derived from the first-order finite difference. Then, the triple method is applied to optimize the storages and the calculations related to the gradients of the physical property models. Therefore, the storage compression amount of the calculations related to the gradients of the physical property models and the cross-gradient constraint are reduced to one-fold of the number of grid cells at least, and the compression ratio increases with the increase of the number of grid cells. The test results from the synthetic data and field data prove that the structural coupling is achieved by using the fast cross-gradient joint inversion method to effectively reduce the multiplicity of solutions and improve the computing efficiency.

This paper investigates the correlation between tidal stress and earthquakes for periods ranging from hours to months in the limited zone of the Palu region (Central Sulawesi, Indonesia). Through Schuster and binomial tests, we examined the relation between the seismicity (time density of seismic events) and tidal potential arising from the Moon and Sun, using all tidal components simultaneously and focusing on the estimation of specific terms. The results show significant correlations between the seismicity and tidal potential for S₂ (0.5 d) and O₁ (1.075 d) tidal components in the case of solely isolated earthquake events, particularly for shallow earthquakes. Meanwhile, there is a strong relationship between aftershocks and tidal components larger than the M_f period (13.661 d). Finally, the analysis of the temporal variation of the earthquake-tide relation reveals an optimal correlation for about six years before the 2018 great Palu earthquake. The correlation becomes insignificant afterwards.

High-precision polar motion (PM) prediction is of important significance in astronomy, geodesy, aviation, hydrographic mapping, interstellar navigation, and so on. SSA can effectively extract the trend and period terms of PM,in the process of achieving high-precision medium- and long-term polar motion prediction, it is necessary to solve the end effect problem and overfitting problem of SSA forecasting method; therefore, ARMA was applied to decreasethe end effect, and a suitable combination of reconstructed components was determined to avoid the high variance reaction of SSA overfitting. Based on the decomposition and reconstruction of the PM by SSA, the reconstructed components are determined to participate in the SSA iterative fitting model according to the variance contribution rate. The combination of the reconstructed components representing the polar motion period term and the trend term is determined according to the correlation analysis of the selected reconstructed components. After the above work, the principal component prediction sequence is obtained by fitting the period term and the trend term to convergence, respectively, and then, the SSA end effect is modified, and the residual term is predicted based on ARMA. The test results show that he prediction accuracy of SSA + ARMA at the front of the X and Y directions are improved by 96.90% and 97.53% compared with those of SSA, respectively, and the forecast accuracy of 365 days are improved by 37.93% and 19.53% in the X and Y directions compared with those of Bulletin A, respectively.

To estimate the parameters of the mixed additive and multiplicative (MAM) random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array, we introduce a derivative-free cat swarm optimization for parameter estimation. We embed the Powell method, which uses conjugate direction acceleration and does not need to derive the objective function, into the original cat swarm optimization to accelerate its convergence speed and search accuracy. We use the ordinary least squares, weighted least squares, original cat swarm optimization, particle swarm algorithm and improved cat swarm optimization to estimate the parameters of the straight-line fitting MAM model with lower nonlinearity and the DEM MAM model with higher nonlinearity, respectively. The experimental results show that the improved cat swarm optimization has faster convergence speed, higher search accuracy, and better stability than the original cat swarm optimization and the particle swarm algorithm. At the same time, the improved cat swarm optimization can obtain results consistent with the weighted least squares method based on the objective function only while avoiding multiple complex weight array derivations. The method in this paper provides a new idea for theoretical research on parameter estimation of MAM error models.

Alterations made to the natural ground surface and the anthropogenic activity elevate the surface and air temperature in the urban areas compared with the surrounding rural areas, known as urban heat island effect. Thermal remote sensors measure the radiation emitted by ground objects, which can be used to estimate the land surface temperature and are beneficial for studying urban heat island effects. The present study investigates the spatial and temporal variations in the effects of urban heat island over Tiruchirappalli city in India during the summer and winter seasons. The study also identifies hot spots and cold spots within the study area. In this study, a significant land surface temperature difference was observed between the urban and rural areas, predominantly at night, indicating the presence of urban heat island at night. These diurnal land surface temperature fluctuations are also detected seasonally, with a relatively higher temperature intensity during the summer. The trend line analysis shows that the mean land surface temperature of the study area is increasing at a rate of 0.166 K/decade with p less than 0.01. By using the spatial autocorrelation method with the urban heat island index as the key parameter, hot spots with a 99 percent confidence level and a 95 percent confidence level were found within the urban area. A hot spot with 95 and 90 percent confidence level was identified outside the urban area. This spike in temperature for a particular region in the rural area is due to industry and the associated built-up area. The study also identified cold spots with a 90 percent confidence level within the rural area. However, cold spots with a 95 and 99 percent confidence level were not identified within the study area.