Taking Hebei province as an example, this paper conducts research on precipitation threshold models by integrating GNSS PWV and the Fengyun-4 meteorological satellite(FY-4A) lightning data. By comparing precipitation, we determine the threshold values for judging the impact indicators. We set a range of candidate threshold values, and use the critical success index(CSI) to obtain the threshold values for each impact indicator. We add FY-4A lightning data and obtain the optimal threshold selection model. By applying the optimal threshold selection model to three additional sites, we validate the effectiveness of the research model. The results show that the precipitation threshold model, which integrates GNSS PWV and FY-4A lightning data, has an accuracy rate of approximately 60% to 80%, with a false alarm rate controlled at around 20% to 40%.

In order to solve the problem of insufficient accuracy of the GPT3 model in simulating atmospheric profile, this paper analyzes its vertical correction method and then proposes an improved GPT3 model, namely GPT3v model, by introducing new temperature decline rate and pressure vertical correction algorithm. The new model is validated using sounding data, NCEP reanalysis data and GNSS data validation. The results show that in the vertical direction, the average RMSE values of temperature, pressure, and total zenith delay estimated by the GPT3v model are 6.0 K, 7.9 hPa, and 23.0 mm, respectively. Its accuracy is improved by 46%, 58%, and 52%, respectively, compared with the GPT3 model’s 11.1 K, 19.0 hPa, and 47.8 mm. The compared result of GNSS ZTDs shows that when there is a significant height difference between the station height and the surrounding four model grid points, the GPT3 model has a significant tropospheric delay estimation error, and the GPT3v model effectively overcomes this problem. These results demonstrate that the GPT3v model is superior to GPT3 in high-altitude and complex terrain areas.

Using GNSS observation data to study the topographic deformation mode in the Karst area has certain reference significance for studying the laws of Karst geological deformation. We use GAMIT/GLOBK10.71 software to solve the data of 25 reference stations of GZCORS and obtain the single day coordinate time series of each station. We employ a spatial filtering algorithm based on principal component analysis to remove the common mode errors from the daily position time series. To analyze the characteristics of common model errors we use the spectral analysis method, and to determine the optimal noise model we use the maximum likelihood estimation method and motion speed of the station. The results show that the common model errors of GZCORS coordinate series contain obvious periodic terms, and the maximum amplitude periods in the north direction appear in 0.2 week/a, 1.2 week/a, 3.2 week/a and 4.2 week/a respectively; the optimal noise model of GZCORS station is dominated by WN+FN and WN+GM. After removing the common model errors, 36% of the station component noise characteristics have changed; after removing the common errors, the noise level of the coordinate time series is significantly reduced, and the estimation accuracy of the velocity of each coordinate component is significantly improved, in which N, E and U components are increased by 52%, 56% and 50% respectively.

Aiming at the problem of missing CORS coordinate time series, we propose a time series interpolation method based on GNSS network quasi-stable adjustment. We select relatively stable stations in the region, construct a baseline network, and use the quasi-stable adjustment method to repair the missing sequence based on the trend changes of stable stations. Through simulation experiments, we find that the quasi-stable adjustment method has the best repair effect, with an average error of within 5 mm, retaining the motion trend of the original data. We use the quasi-stable adjustment method to simulate the 365 d coordinate series of the point to be solved, and we compare it with the real series. The trend changes of the simulation series and the real series are basically consistent. The average error in the E and N directions is within 5 mm, and the average error in the U direction is within 10 mm.

This paper comprehensively analyzes the performance of high accuracy service(HAS) from the aspects of service correction availability, track accuracy, clock correction accuracy, and so on. At the same time, we analyze the performance of HAS PPP using MGEX stations in different regions of the world. The results show that the availability of most satellite corrections is at least 80%. Signal-in-space ranging error(SISRE) of GPS and Galileo are 0.163 m and 0.097 m, respectively. The accuracy of MGEX stations is less than 0.2 m in the horizontal direction and 0.4 m in the vertical direction.

Limited by low-cost signal reception and processing units, the accuracy of single point positioning and relative positioning of low-cost terminals cannot be guaranteed. Based on this, we propose an anti-differential velocity constrained RTD model with additional Doppler observations and an RTK adaptive switching model. Using the Doppler velocimetry constrained position solution, the anti-multipath capability of multi-frequency signals is fully utilized, and the pseudo-range noise is effectively smoothed to ensure positioning accuracy and stability. By means of RTD/RTK adaptive switching, we ensure the stable positioning accuracy under complex observation conditions. The measured positioning results based on Mi 8 and M8 terminals show that: the accuracy of both terminals is better than 1.5 m in east, north and elevation directions under static positioning; the plane accuracy of Mi 8 terminal is improved by 54%/44%  and that of M8 terminal is improved by 51%/26% under dynamic mode without occlusion/with partial occlusion; the positioning of M8 terminal is better than 0.5 m in east and north directions under dynamic on-board experiment, and the ambiguity fixation rate is improved by about 30 percent points.

Firstly, from ITRF2020 we screen the stations that can receive Beidou signal. Then, we analyze the site observation data from aspects such as data integrity, satellite visibility and multipath error, and the corresponding criteria are set for screening. Finally, based on grid method, we propose a station selection method considering both uniformity and data quality as well as station stability, by which we select 72 stations evenly distributed around the globe as IGS stations in Beidou coordinate frame solution.

Based on the Helmert transformation model, we detect and eliminate possible gross errors in the prior coordinates of the starting points. Then, we construct the minimum constraint conditions based on the similarity transformation of the remaining starting points coordinates. Finally, we determine the entire coordinate framework. We select the weekly observation data of IGS stations and GNSS reference stations in and around China, and use ITRF2014 coordinates of IGS stations to construct parameter weighted constraints, minimum constraints and Helmert model constraints, considering prior coordinate error of IGS stations to obtain coordinate results of the whole network. The results show that Helmert model method can effectively weaken the influence of gross coordinate errors of starting points on the mesh distortion, and improve the accuracy and reliability of the regional reference frame to a certain extent.

Robot calibration needs to realize the registration correlation between the measuring coordinate system and the robot coordinate system, and get the conversion relationship between them. In order to overcome the shortcomings of traditional axis vector coordinate system conversion method, which is complex in operation and low in fitting accuracy, this paper proposes a solution method which combines tool coordinate system calibration and common point conversion. Firstly, based on the distance constraint, we calibrate the position parameters of the optical target ball center relative to the end flange coordinate system. Then, the influence of measurement error on coordinate transformation is reduced by the barycentric configuration. Finally, we use the three-dimensional seven-parameter model to solve the homogeneous matrix of coordinate transformation based on the common point transformation. The experimental results show that the comprehensive MAE error of this method is reduced by 35.44%, compared with the traditional fitting method. This method is simple and has higher coordinate conversion accuracy, which is suitable for industrial field use.

In response to the M_W6.7 magnitude earthquake in Menyuan county, Qinghai province, China, on January 8, 2022, to provide a time-sensitive and multi-topic map product for earthquake disaster rescue, and a basis for decision making, we propose an emergency mapping technology process that integrates multi-source satellite remote sensing data and multiple data processing techniques to rapidly decode earthquake-related information.

We obtain the velocity field of present-day crustal motion of the central segment of the Red river fault and adjacent region by using the encrypted GNSS stations and previously published velocity field data. The three-dimensional finite element model of the study area is constructed based on the distribution of active fault in the Sichuan-Yunnan region. We obtain the fault slip rate of different sections of the central segment of Red river fault and strain rate field of study area constrained by GNSS velocities. The results show that the Midu-Yuanjiang segment of Red river fault experiences a right-lateral slip rate of 1.2±0.6 mm/a and a shortening rate of 0.6±0.5 mm/a, and the Yuanjiang-Yuanyang segment of Red river fault experiences right-lateral slip rate of 1.8±0.7 mm/a and a shortening rate of 1.5±0.6 mm/a. The strain rate results show that shear deformation dominates in the central segment of the Red river fault and adjacent region. The maximum shear strain rate appears in the Xiaojiang fault zone with a rate of about 62×10^-9/a; the shear deformation is relatively weak along the Red river fault. The dilatation strain rate shows that compressive deformation is significant along the Yuanjiang-Yuanyang segment of Red river fault with the rate of about 10×10^-9/a. There is strong coupling along the Yuanjiang-Yuanyang segment, and the seismic hazard of the Yuanjiang-Yuanyang segment deserves more attention.

In order to study the evolution characteristics of geological activity of the Red river fault zone in recent years, and analyze the seismic risk in the region, we use the GNSS velocity field data of 1999-2007 and 1999-2017. The negative dislocation-block model is used to invert fault locking and slip deficit velocity of the Red river fault zone. The results show that the locking degree of the Red river fault zone is characterized by strong ends and weak middle. The locking depth of the southern and northern segments are 20 km and the locking degree is relatively high. The locking depth of the middle segments are 5 km and the locking degree is relatively low. In terms of slip deficit, the northern segment is 2.5-5 mm/a, with a faster rate of energy accumulation. The middle segment is 0.4-1.8 mm/a, and the southern segment is 1.5-2 mm/a. The middle and southern segments are relatively stable. After 2008, the locking degree of the Red river fault zone maintained the previous characteristic. The deep locking state of the northern and southern segments began to spread to the middle, and the overall locking degree of the Red river fault zone deepened. At the same time, the slip deficit rate increased. The seismic risk of the northern segment continued to increase, while the active state of the southern segment became unstable.

Based on the historical seismicity of the southeast segment of the Xianshuihe fault zone and distribution of the earthquakes around the 2022 Luding M_S6.8 earthquake in the past decade, using GNSS observation data, we give the velocity and strain rate field in the regional and surrounding areas, and identify the deformation characteristics before the earthquake. The GNSS velocity field shows that the strike slip rate of the southeast section of the Xianshuihe fault zone is about 10 mm/a, and the regional movement is in the ES direction, the same results with the regional structure. The strain rate field show that there is still a strong shear strain rate in the seismogenic fault. With the continuous release of strain energy after the Wenchuan earthquake, the unloading effect of the Longmenshan fault zone indirectly affects the southeast section of the Xianshuihe fault zone. The fault has been in a transition zone with high strain accumulation for a long time, the degree of fault blocking increased today. The trend of seismic activity and seismic risk in this area deserve further attention and research.

This paper presents a comprehensive analysis of the anomalous fault activity and post-earthquake changes around the epicenter of three earthquakes, based on the cross-fault short baseline and short level data accumulated in Sichuan for many years, through the analysis of the original observation curves, the quantitative calculation of the near-field 3D activity of the fault and the regional prediction effectiveness index. The results show that there were more short-term anomalies across the fault before the three earthquakes of M_S6.0 and above, and the main characteristics are significant sudden jumps and huge anomalies. The three moderate-to-strong earthquakes occurred in the interior of the Bayan Har block, the boundary fault zone and the eastern boundary of the adjacent Sichuan-Yunnan rhombic block, which are the result of the joint action of stress transfer, tectonic activity and block motion. Therefore, the spatial and temporal overlap of the sites where anomalies occur is high, and strict differentiation and stripping is not possible. In addition, we observe the pre-slip phenomenon before the Luding M_S6.8 earthquake before the viscous slip destabilization. One month after the earthquake, cross-fault deformation began to show adjustment recovery changes.

This study focuses on the Jianshanying landslide in Fa’er town, Guizhou province and utilizes Google Earth historical images, Sentinel-1 SAR images, and Sentinel-2 optical images to monitor the two-dimensional deformation and time series of the landslide through visual interpretation, SBAS-InSAR, and optical offset techniques. The comparison results of multiple periods of historical Google Earth images indicate a year-by-year increase in the slope area of the landslide, with faster growth observed after 2018. The SBAS-InSAR monitoring shows an average annual deformation rate of -150 mm/a for Jianshanying landslide, with most of the deformation occurring at the slope’s front. The cumulative deformation before sliding is -108 mm. The optical offset tracking technology using Sentinel-2 images revealed the 2D deformation time series of a landslide from 2016 to 2021, providing additional information on the landslide body’s deformation that SBAS-InSAR technology could not capture. Results show a maximum cumulative shape change of 32 m in the east-west direction and -52 m in the north-south direction. By integrating the two-dimensional time series results of Jianshanying landslide with geological exploration data, rainfall patterns and other relevant factors, we determine that the deformation of the landslide is closely linked to recurrent mining activities as well as precipitation events.

By reviewing survey network changes and the summary of earthquake cases, we objectively expound the development process of Sichuan mobile gravity observation and its application efficiency in earthquake trend determination. The results show that the time-varying abnormal signals of gravity field such as high gradient zone, four quadrant distribution and zero line turning are observed by using mobile gravity data, and the Wenchuan, Lushan, Jiuzhaigou and Luding earthquakes in Sichuan-Yunnan prismatic block and boundary zone are accurately predicted in the medium term, especially in the determination of strong earthquake location, with an accuracy rate of 60%.

Applying adaptive noise-complete ensemble empirical modal decomposition(CEEMDAN) and wavelet denoising, we propose a random noise suppression method for DSQ water pipe tiltmeter signals. Firstly, the signals are decomposed by CEEMDAN to obtain several eigen mode functions(IMFs), and the number of decompositions varies dynamically with different signal noises. Then we calculate the correlation coefficient values of each IMF and the original signal, and calculate the original signal. By wavelet transform we process the IMFs within the threshold of the coefficient value. Finally, we perform the linear reconstruction to obtain the denoised signal. The simulation and actual denoising experimental results show that the random noise suppression effect of this method is obvious, and the retention ratio of the effective components of the signal is higher and better than other similar methods.

In this paper, linear superposition algorithm and zero-delay multiplication algorithm are used in transient signal detection experiments in synthetic data respectively, and their effects in transient signal recognition are compared. The results show that the zero-delay multiplication of multi-channel on the same station and multi-channel on multi-station data can suppress the interference and noise more effectively, magnify the quasi-synchronous transient signal, and realize the preliminary detection and recognition of transient abnormal signal. On this basis, the zero-delay multiplication algorithm is used to process the fixed-point deformation observation data of Yunnan from 2002 to 2022, and 11 sets of transient short-duration signals are identified from the data, and the spatio-temporal correlation between the signals and the earthquakes above M_S5.0 in Yunnan is further analyzed.

We screen 42 historical earthquake cases, and conduct principal components analysis(PCA) on seven impact factors, such as earthquake magnitude, source depth, epicenter intensity, seismic intensity, difference between epicenter intensity and seismic intensity (ΔL), population density, and occurrence moment, and construct an earthquake death toll prediction model based on particle swarm optimization(PSO) extreme learning machine(ELM). We pre-process and train the data of 37 earthquake cases, and test the accuracy of the model using the data of 5 earthquake cases. The experimental results show that the average error rate of the proposed combined PCA-PSO-ELM model is 10.87%, which is 8.70 percent points and 18.38 percent points lower than that of the PCA-ELM model and ELM model, respectively. Therefore, the combined PCA-PSO-ELM model is feasible for earthquake death toll prediction.