To accurately identify the Wuming mountain landslide hazards, based on SBAS-InSAR technology, we use Sentinel-1A/B ascending and descending track data to invert the vertical and slope deformation of the area from March 2017 to April 2021. We identify the landslide hazards and dangerous points, then analyze the time series of deformation and the reason of landslides. The results show that the potential geological hazards in Wuming mountain can be divided into three main areas, in which the maximum vertical cumulative deformation reaches -38.28 mm, and the maximum slope cumulative deformation near the free surface is 22.10 mm. The amount of precipitation has different degrees of influence on the stability of slope, and the peak value of cumulative deformation of slope lags that of precipitation. The research results can trace back the deformation characteristics of Wuming mountain landslide hazards in Jizhou, provide new reference and ideas for the monitoring and identification of geological disasters in northern mountainous areas of Tianjin and offer guarantees and technical support for disaster prevention and mitigation.

In order to accurately grasp the development law of landslide deformation, based on landslide deformation monitoring results, we construct a landslide early warning prediction model. We first carry out multifractal characteristics analysis of landslide deformation data using the MF-DFA model, and use M-K analysis to study landslide early warning by constructing dual criteria, namely Δa index criterion and Δf(a) index criterion. Secondly, based on the separation and processing of landslide deformation data by ensemble empirical mode decomposition method, we use the GOA-RNN-CT model to realize the sub item combination prediction of landslide deformation. The results show that the value of h(q) decreases with the decrease of q value of wave function, indicating that landslide deformation data has multifractal characteristics. Through the study of early warning classification, we conclude that the landslide early warning level is grade II, that is, the landslide deformation tends to develop in an unfavorable direction. At the same time, through the deformation prediction analysis, we conclude that the sub item combination prediction has better effect and stability in landslide deformation prediction, and the extrapolation prediction results show that the landslide deformation will continue to increase. Finally, the combined response of multifractal characteristics results and deformation prediction analysis results show that the existing early warning level of landslide is relatively unfavorable, and the subsequent deformation will further increase and tend to further instability. We suggest taking necessary prevention and control measures for landslide.

Taking Shuicheng county, Guizhou province as the study area, we use SBAS InSAR to extract surface deformation fields by processing data from 2018-07 to 2019-07 for 31-period ascending and 30-period descending Sentinel-1A before the occurrence of the landslide in Jichang town, respectively. The results show that: 1) There is no obvious deformation information in SBAS InSAR results before Jichang landslide occurred, demonstrating that the potential landslides detection of Jichang landslide has exceeded the capability of SAR in the 12 d revisit period. 2) There are five obvious deformation zones in the study area, which may be related to slope instability, underground/open-pit mining and drainage of mineral processing. 3) The SBAS InSAR deformation results of the ascending and descending orbit data can be complementary and verified, significantly improving the ability of satellite radar for potential landslide detection and deformation monitoring in mountainous area. The research method can provide technical reference for the investigation and early identification of landslide hazards in Guizhou province and mountainous area of southwest China.

According to the non-stationary and nonlinear characteristics of GNSS time series, we analyze the applicability and characteristics of XGBoost and Prophet models, and construct a Prophet-XGBoost prediction model. Firstly, using the Prophet model, we decompose the GNSS original time series, then carry out partial prediction by XGBoost model. We obtain prediction results by equal weight addition. We select the daily coordinate time series data of U component of ALGO, ALRT and BRST IGS stations in the experiment, and use MAE and RMSE as evaluation indexes. The experimental results show that compared with the single XGBoost model and Prophet model, the MAE and RMSE values of Prophet-XGBoost model are optimized to a certain extent. The effectiveness of this method is verified and can be used for GNSS time series prediction.

Based on the ability of ICEEMDAN algorithm to accurately separate and extract low-frequency signals and trend information without a priori information, and the advantage of SSA with better signal reconstruction, we propose a joint reconstruction method based on ICEEMDAN and SSA. The method extracts and reconstructs the weak periodic signals by using ICEEMDAN method; this makes up for a deficiency of the SSA method, namely that it is difficult to extract periodic signals when the singular values of the Hankel matrix corresponding to the weak periodic signals are close to the singular values of the noisy Hankel matrix, which are easily masked by noise. We verify the decomposition and reconstruction accuracy of the algorithm by simulated experiments and real site data. We further compare the algorithm with the singular spectrum analysis, wavelet decomposition, and moving ordinary least squares methods. The experimental results show that the joint ICEEMDAN-SSA algorithm has better reconstruction accuracy compared with existing methods.

Using the 2019 doy110-139 observation data from 120 tracking stations around the world, we carry out precise GPS orbit determination. Then, we carry out PPP experiments using 7 stations not involved in orbit determination with three solar radiation pressure models: ECOM1, ECOM1+BW, ECOM1+ABW. The results show that the orbit accuracy obtained by the ECOM1+ABW combination model is highest, with three-dimensional orbit accuracy in no-earth eclipse seasons better than 4 cm. For static PPP, the accuracy of horizontal direction is better than 0.8 cm and the accuracy of vertical direction is better than 1.2 cm after convergence. For kinematic PPP, the convergence time is about 30 min. After convergence, the accuracy of horizontal direction is better than 1.4 cm and the accuracy of vertical direction is better than 2.0 cm.

To analyze the positioning accuracy of BDS-3 in polar regions, 7 consecutive days of observation data from 10 MGEX stations are selected for SPP and PPP experiments. The results show that the number of visible satellites of BDS-3 in Arctic and Antarctic regions is similar, which have an average of 9 visible satellites, and the value of PDOP is same, which is about 2.3. The positioning accuracy of BDS-3 is analogous among different frequency points. The positioning accuracy of SPP in Antarctic region is slightly better than that of Arctic region, especially in the U direction. The positioning accuracy is better than 1 m, 1 m, and 5 m in the E, N, and U directions in Arctic region, respectively, and better than 1 m, 1 m, and 2 m in the E, N, and U directions in Antarctic region, respectively. BDS-3 has the same PPP positioning accuracy in polar regions, which is basically consistent with GPS. The positioning accuracy is better than 2 cm in the E, N, and U directions of each combined frequency point.

Considering the problem that the performance of the existing improved GM(1,1) model is not significantly improved in satellite clock offset prediction, we present a method to improve prediction accuracy by optimizing the initial conditions. Firstly, we construct the GM(1,1) prediction model with unknown initial conditions, and then use the latest component of the original sequence to solve the initial conditions. Finally, we use the model to predict the precise clock offset data provided by IGS. The results show that the GM(1,1) model with optimized initial conditions is feasible for clock offset prediction, and the prediction accuracy is greatly improved compared with the traditional GM(1,1) model.

Based on DE405, DE421, DE430 and DE440, we calculate the position of major planets under the geocentric celestial reference system(GCRS) and the barycentric celestial reference system(BCRS). We then compare the position accuracy of other ephemerides relative to DE440. We discuss the position and velocity accuracy of the Moon under GCRS based on different ephemerides, and analyze the effects on the conversion from lunar centered inertial system to the lunar fixed system and give some advice on application. The results show that due to observation data and other factors, the position accuracy of the major planets varies greatly, ranging from the metre level to the 10⁶ m level. As for the position accuracy, DE421 and DE430 have an improvement of 1-2 orders of magnitude when compared to DE405, and DE430 improves 50% over DE421. The accuracy of lunar position and velocity relative to the center of Earth are 7 m and 0.02 mm/s for DE405, 1.5 m and 0.004 mm/s for DE421, 1.3 m and 0.003 5 mm/s for DE430. For the coordinate and velocity errors on the conversion from lunar centered inertial system to the lunar fixed system, the DE405 is 30 m and 3 cm/s, the DE421 is 1.3 m and 1.2 mm/s, and the DE430 is 1 m and 0.9 mm/s. The influence of ephemeris on the conversion of reference system is meter level and we recommend using DE430 or DE440 for lunar probe navigation and other lunar missions.

Based on summarizing the existing matrix maximum eigenvalue method(MMEM) of RAIM availability evaluation, we improve the mathematical model in the existing RAIM availability method under double-satellite faults conditions, and propose the maxima method(MM) of RAIM availability evaluation. Based on the given integrity risk parameters and the BDS measured data of 8 tracking stations in China provided by the international GNSS monitoring and assessment system(IGMAS) on September 6, 2020, we use the two methods to calculate the horizontal protection level(HPL) under double-satellite faults conditions to compare and analyze the RAIM availability performance of BDS. The results show that the MMEM-based RAIM availability is lower than that of the MM in non-precision approach(NPA) in China, the overall MMEM-based RAIM availability is higher than that of MM, but the MM method consumes less calculation time. The MM-based RAIM availability can fully meet the route and ocean stages, and the MMEM-based RAIM availability can fully meet the terminal, route and ocean stages. The MM-based RAIM availability has rigorous theory, a simple mathematical model, and is easy to program. It is another RAIM availability evaluation method besides MMEM.

We analyze the static data collected by the geodetic receiver and smartphone by setting up two different occlusion environments. The results show that the smartphone receives some NLOS signals, and the satellite signals are obviously interfered by multipath effects. According to the characteristics of the NLOS signal and original GNSS measurement information of the smartphone, we design a scheme for screening the original GNSS data of the smartphone. The results of static positioning experiments show that the application of data screening scheme can significantly improve the positioning accuracy of smartphones in urban environment. The positioning accuracy of the smartphone SPP algorithm and the RTK positioning algorithm in the plane direction can be improved by 20%-40%, and the positioning accuracy in the elevation direction can be improved by 30%-60%.

Using the regional ionospheric grid data and spectral analysis method, we study the ionospheric period difference and extreme value difference in different regions of China. Moreover, using the ionospheric gradient calculation method, we analyze the small-scale spatiotemporal variation characteristics of ionosphere in different regions. The results show that compared with the northern region, the ionospheric periodicity is more obvious in the southern region. When the longitude changes by 5°, most of the TEC changes are between -10 and 10 TECu. When latitude changes by 2.5°, the TEC varies from -20 to 20 TECu, and there is a great difference between the north and south region, but little difference between the east and west. When the time interval is 1 hour, most ionospheric TEC changes in China are between 5 and 25 TECu, and there is a great difference in the time variation characteristics between the north and south region, but little difference between the east and west. However, with the increase of the time interval, the difference in the time variation characteristics between the east and west is gradually more obvious. The conclusions can provide theoretical reference for real-time ionospheric modeling in China and have certain referential significance for ionospheric spatiotemporal changes and ionospheric magnetic storm monitoring in China.

In view of the problems that the existing regional zenith tropospheric delay(ZTD) model belongs to function or grid type, the parameters are fixed, and it is difficult to express the characteristics of rapid spatio-temporal change of ZTD, we propose a new combined prediction model based on wavelet transform, Fourier series fitting, AR and SVR. In the time domain, the ZTD sequence is decomposed into low-frequency and high-frequency sequences by wavelet transform. The low-frequency sequence is fitted with Fourier series as a function of time, and the high-frequency sequence is predicted by AR. The mapping of position parameters to Fourier series parameters is established by SVR in spatial domain. Inputting time and location information in the model, the corresponding ZTD prediction value can be obtained. The two-year ZTD data of 94 GNSS stations are used for modeling, and one-year ZTD data of 24 GNSS stations are used for model prediction. The results show that the bias and RMSE between the measured value and prediction value is -2.02 mm and 3.07 cm, which is better than most of regional ZTD models. The model can significantly improve the positioning accuracy in pseudorange single point positioning. The experiments show that the combined model has high prediction accuracy and reliability, and that it has certain application value.

Based on the three-component broadband waveform data and seismic phase reports from the digital seismic networks in Sichuan, Guizhou and Chongqing, we use Geiger positioning method and ISOLA method to relocate the Changning M_S6.0 earthquake sequence and invert the focal mechanism of five M_S≥5.0 earthquakes in the sequence. The relocation results show that the earthquake sequence is mainly distributed in the axis of Baixiangyan-Shizitan anticline, along the direction of 65°NW. Along this direction, the focal depth features are deep in the northwest and shallow in the southeast. Inversion results of focal mechanism show that the rupture of main shock is characterized by high dip angle and sinistral strike-slip. The strike/dip/rake of nodal plane Ⅰ is 20°/72°/151°, the strike/dip/rake of nodal plane Ⅱ is 120°/62°/21°, which is basically consistent with the axial strike of Baixiangyan-Shizitan anticline. The content of pure double-couple(DC) of the five earthquakes is less than 70%, and the centroid depth is less than 10 km. Comprehensive analysis shows that the seismogenic fault of Changning M_S6.0 earthquake is a sinistral strike-slip fault developed in Baixiangyan-Shizitan anticline. We speculate that the Changning earthquake was caused by fluid injection and complex geological structure activities.

Using the InSAR deformation monitoring data, we calculate the coseismic deformation field of the Dingri M_W5.7 earthquake in Tibet in 2015, and inverse the geometric parameters and slip distribution of seismogenic faults. Then, we analyze the static Coulomb stress triggering effect of the Nepal M_W7.8 main earthquake on Dingri earthquake. Based on the comprehensive analysis of seismic slip mechanism and structural characteristics, we consider that Dingri fault is a buried west-dipping fault. The inversion results show that the rupture is relatively concentrated and mainly distributed in the depth range of 6~9 km with a predominantly normal fault mechanism. The strike and dip of the seismogenic fault of Dingri earthquake are about 178° and 48°, respectively. The length and width of the rupture zones are approximately 5 km and 5 km, respectively, and the maximum slip is about 0.2 m. The released seismic moment is estimated to be about 3.7×10¹⁷ N·m, corresponding to a magnitude of M_W5.6. The coseismic Coulomb stress of Nepal earthquake is about 0.2 bar at the focal point of the Dingri earthquake; it caused the strain loading of the Xainza-Dinggye rift in southern Tibet.

Aiming at the big model error of least squares support vector machine(LSSVM) in regional quasi-geoid fitting, we establish the combination model of LSSVM and Shepard interpolation model for GPS elevation conversion.  We use LSSVM to fit the medium-long wave term in abnormal height, and use Shepard interpolation model to generalize and remove the residual of medium-long wave term. Combined with engineering examples in plain area and plateau mountainous area, we use the quadric surface model, the LSSVM, the Shepard interpolation model, the quadratic-Shepard model, and the LSSVM-Shepard model for elevation conversion and accuracy comparison. The results show that the accuracy of new combined model is higher than that of single model. Moreover, the conversion effect of LSSVM-Shepard model in the plain area is basically consistent with the quadratic-Shepard model, and the fitting effect is better than the quadratic-Shepard model in the plateau mountainous area.

In this paper, we finely process a broadband magnetotelluric profile passing through the middle section of Langshan piedmont fault and carry out two-dimensional inversion. We obtain the deep electrical structural characteristics of the middle section of the fault and the blocks on both sides. The results show that the Langshan piedmont fault presents an obvious high and low electrical resistance boundary zone. The upper crust of Langshan uplift on the west side of the fault shows relatively complete high electrical resistance characteristics. The middle and lower crust is dominated by low resistivity bodies. There is a 5 km thick low resistivity sedimentary layer in the shallow part of Linhe basin on the east side of Langshan piedmont fault, and there may be a high resistivity basement below the sedimentary layer. These distribution characteristics indicate that Langshan piedmont fault may be a deep and large fault cutting the lithosphere scale, and it has a risk of developing strong earthquakes.

Due to the large amount of environmental noise in seismic signals, we establish the initial data set based on natural earthquake events and artificial blasting events, decompose and reduces the noise in waveform signals using the ensemble empirical mode decomposition(EEMD) technique, extract the purer intrinsic mode function(IMF) components of each order, and then calculate the distribution entropy for the first 10 order components separately to establish the neural network input matrix. The whale optimization algorithm(WOA) is applied to optimize the self-organizing feature mapping(SOM) neural network parameters(competitive layer dimensions and number of network training) to find the corresponding optimal parameter values for different training samples to improve the stability of pattern recognition, thus improving the seismic recognition rate. The results show that the EEMD multiscale distribution entropy combined with WOA-SOM model can effectively identify natural earthquakes and artificial blasting events.

In view of the synchronous trend change of NS and EWcomponents of Tonghai vertical pendulum since April 2019, we calculate the M₂ wave tidal factor of NS and EW components from 2017 to 2020. Combined with the structural data of underground media such as monitoring wells, we construct three-dimensional finite element physical models of vertical stratification of Tonghai station. We quantitatively analyze the differential characteristics of the influence of building load on NS and EW displacement under different rock parameter models. The results show that: 1) Since April 2019, the N-tilt variation of NS component reaches about 2 750 ms, while the E-tilt variation of EW component reaches about 580 ms. The trend variation of NS component is significantly bigger than that of EW component. 2) From 2017 to 2020, the M₂ wave tidal factor of NS and EW components are relatively stable, in which the NS component fluctuates around 0.7 and the EW component fluctuates around 0.52. The trend break of data fails to cause synchronous change in the tidal frequency band. 3) The hospital building load located in the northeast of Tonghai station generates N-direction and E-direction displacement, which is consistent with the N-tilt and E-tilt trend break of vertical pendulum after April 2019. The simulated N-tilt displacement is about 3 times of E-tilt displacement.