To objectively evaluate the accuracy of TPXO series ocean tide models for the eastern China sea(ECS), we use the latest FES2014b and EOT20 as reference models. The assessment is performed by computing the root mean square error(RMSE) of the M₂ vertical ocean tide loading(OTL) displacements and the root sum square(RSS) of the 8 principal tidal constituents based on the GPS-observed M₂ vertical OTL displacements and tide gauge observations from the evenly selected 22 GPS sites and 65 tide gauges in the coastal areas, respectively. The results show that: 1) In the whole ECS, EOT20 is the most accurate model with RMSE of 0.41 mm and RSS of 11.1 cm, while the accuracy of FES2014b is slightly lower with RMSE of 0.43 mm and RSS of 11.4 cm. 2) Along the coast of eastern China and the western Korean Peninsula, there are large differences between TPXO models and FES2014b, and the RSS with tide gauge observations are 2-6 times as much as that of FES2014b and EOT20. 3) Both TPXO9_Atlas and TPXO8_Atlas agree better than other models with the M₂ vertical OTL observations in the Ryukyu Islands, and the two models are suggested for the local correction of OTL displacements.

In response to the imprecise localization of moving radiation sources in complex scenarios and issues related to the accuracy of the observation station's position and the geometric relationship with the target using the two stage weighted least squares(TSWLS) algorithm, we propose a passive radar centroid passive localization algorithm for moving targets based on Taylor expansion and the joint application of TSWLS. This method utilizes a TSWLS algorithm to determine the target's position and velocity. The obtained target parameters serve as initial values for Taylor expansion, constructing the positioning error equation. Through iterative optimization, the algorithm refines and solves for optimal target parameters. The proposed algorithm and TSWLS independently provide estimations, with the final result obtained through centroid localization. Simulation experiments show that, regardless of target speed, in comparison with traditional methods, the presented algorithm significantly improves robustness and positioning accuracy while reducing sensitivity to geometric relationships between the observation station and target.

We comprehensively use InSAR and machine learning technology to evaluate landslide susceptibility in Jinyang county, Sichuan province. The data set is updated by interpreting landslides. Based on 12 evaluation factors, we use three models including random forest(RF), support vector machine(SVM) and extreme gradient boosting(XGBoost) for training in Python environment to complete landslide susceptibility mapping. We use ROC curve to verify prediction performance. We optimize the negative samples, obtain the landslide susceptibility evaluation results after sample optimization using machine learning and update the landslide susceptibility results using surface LOS deformation rate. The results show that the three machine learning models have good zoning effect, and the mapping effect of XGBoost model is best. The accuracy of XGBoost model after sample optimization is highest, and the AUC value reaches 0.95. The surface deformation rate obtained by SBAS-InSAR can reduce zoning errors and give timeliness to landslide susceptibility evaluation.

We propose a method for ground deformation monitoring based on distributed scatterer InSAR(DS-InSAR). The method employs the HTCI algorithm for homogenous pixel identification, utilizes eigenvalue decomposition for phase optimization, determines distributed scatterer(DS) based on spatiotemporal coherence, and establishes a relationship model between DS phase and deformation for time series surface deformation inversion. We use 50 scenes of Sentinel-1A images to obtain spatiotemporal distribution information of ground deformation in Jiangbei region of Nanjing from October 2021 to October 2023. The results show that: 1) The correlation coefficient between DS-InSAR results and PS-InSAR results is 0.731, and the point density increases by 6.22 times. DS-InSAR can more completely reflect the spatiotemporal surface deformation of the study area. 2) DS-InSAR is in good agreement with the measured leveling data, with a maximum error of 5.6 mm, an average error of 2.6 mm, and a root mean square error(RMSE) of 3.0 mm. 3) The geological structures, urban development, and rainfall are the main influencing factors causing ground subsidence.

The water level change series of lakes in the Qinghai-Xizang plateau from 2003 to 2022 are extracted using the data of ICESat, Cryosat-2 and ICESat-2. Taking Qinghai lake as an example, the three retracking algorithms of Cryosat-2 LRM(Ocean-CFI, UCL Land-ice and OCOG) are compared and analyzed, and the results show that OCOG algorithm is the best. The accuracy of the time series of lake water level changes extracted from satellite altimetry data is evaluated by using the hydrological station data of Qinghai lake, Nam Co, Mêmar Co and Lumaqangdong Co, and the results show that the RMS of the difference between the water level change time series of Qinghai lake and the measured results of Xiashe station is only 0.092 m, and the correlation coefficient between the water level change time series and the measured results of Nam Co is 0.989, and the accuracy analysis of different measurement techniques in the same time period shows that the accuracy of the Cryosat-2 SARIn measurement mode is slightly higher than that of ICESat-2. The water level changes of 106 lakes in the Qinghai-Xizang plateau from 2003 to 2022 are extracted, and the water level change trend is analyzed, and the results show that the water level of 19 of the 106 lakes show a downward trend, and the water level of 83 lakes show an upward trend.

Based on the mobile Beidou deformation monitoring system at Xiluodu hydropower station, we per- form the horizontal deformation simulation experiment. The static differential positioning, post-processed kinematic positioning and precise point positioning methods are employed to compute actual displacements and evaluate accuracy. The results indicate that the accuracy of single BDS and GPS+BDS dual systems is consistent. The static differential method has the highest accuracy, with the external coincidence accuracy of dual systems and single BDS better than 0. 5 mm and 0. 7 mm, respectively. The baseline differential method can effectively eliminate interference from dam area water vapor, ionospheric and multipath errors through 24 hours long-term observations. The maximum errors of PPK for dual systems and single BDS are 0.5 mm and 1 mm, respectively, while under dynamic PPP are 1. 7 mm and 1 mm, respectively. This shows thatPPK and PPP can also achieve deformation monitoring results close to 1 mm, demonstrating significant application potential.

Based on the Sentinel-1A image data covering the epicenter, we use D-InSAR to calculate the surface coseismic deformation field of Xinjiang Wushi M_W7.0 earthquake. We use the quadtree method to downsample the coseismic deformation field, and optimize the geometric and physical parameters of the fault by using the downsampling data as constraint conditions. The results show that the maximum uplift values of ascending and descending orbits are 72 cm and 39 cm, respectively. The seismogenic fault is within the Maidan-Shayilamu fault, which is dominated by thrust and has a small amount of left-lateral strike-slip. The fault strike is 230°, the dip is 67°, and the slip angle is about 45°. The maximum slip on the fault plane is 3.53 m, the seismic moment is 3.78×10¹⁹Nm, and the calculated magnitude is M_W7.02. The results of Coulomb stress show that the Tuoshigan fault, Kuokeshale fault, Dashixia fault, Kurukeyuzumu fault, and northern Kuqi fault are all in the stress loading region, on which we need to pay more attention in the future.

In this paper, we collect 1 Hz high-frequency data from multiple GNSS ground reference stations near the epicenter of the Jishishan earthquake on December 18, 2023, and use PRIDE PPP-AR and TRACK software for dynamic PPP calculation and high-precision static data processing. The coseismic deformation displacement fluctuation and permanent deformation displacement of the GNSS reference stations are obtained. Then, we analyze the deformation impact of the earthquake on the GNSS ground reference station at the epicenter. The results show that PRIDE PPP-AR and TRACK software can both calculate the coseismic deformation displacement impact of the earthquake on the reference station. The static calculation results show that the earthquake caused a permanent deformation displacement of about 18 mm in the horizontal direction at LXJS station (5.3 km away from the epicenter), and the impact range of this earthquake on the stability of the GNSS reference station is about 50 km. The rapid deformation analysis method and results can provide reference and data support for disaster prevention and mitigation, as well as real-time navigation and positioning services.

To study the activity characteristics and patterns of the landslide, this paper uses stacked interferometric synthetic aperture radar (InSAR) based on ascending and descending track C-band Sentinel-1A data to obtain the deformation rates of the Cheyuping landslide from January 2020 to December 2021 along the slope direction and in the vertical direction. We obtain the landslide displacement time series evolution results. The results show that the maximum deformation rate of Cheyuping landslide is more than -90 mm/a along the slope direction and more than -40 mm/a in the vertical direction, and the deformation of the landslide is larger at the leading edge and middle part of the landslide, and smaller at the trailing edge, which is characteristic of the slip-type landslide. According to the analysis of rainfall data and time series results, rainfall has a certain influence on the landslide movement, showing seasonal accelerated deformation characteristics. At the same time, the geological and stratigraphic lithology of the landslide area and the change of the water level of the Lancang river also accelerated the landslide activities to a certain extent. This study can provide technical reference for the interpretation of landslide deformation mechanism and disaster monitoring and early warning in the canyon reservoir area.

Addressing the under-quantified crustal deformation characteristics and the unclear stress-strain coupling relationship of the Ordos block, this study employs a spherical least squares collocation model to calculate the characteristic strain field of the study area's crust and inverts the principal stress distribution using seismic source mechanism data. The results indicate that crustal deformation is primarily concentrated along plate boundaries and within fault basins, with less deformation within the Ordos block itself. Notably, at the junction of the northeastern Tibetan plateau and the Alxa block, the crustal deformation is particularly intense, characterized by strong compression along the plate boundary, with the main deformation mechanism being a coexistence of NW-SE compression and NE-SW extension, with a maximum principal compressive strain rate exceeding 0.05 μstrain. The crust in the study area is under a relatively uniform tensile stress environment, with the direction of the maximum principal stress being nearly horizontal and pointing towards SEE, aligning with the direction of the principal strain in the corresponding area. The minimum principal stress direction is closer to the direction of the principal compressive strain.

On the basis of effectively handling the gross errors of landslide monitoring data and fully considering the characteristics of landslide monitoring data, we develop a deep learning landslide displacement prediction model combining time series decomposition and similar component reorganization. First, we deal with the significant gross errors of landslide time series monitoring data using the isolation forest algorithm, and then comprehensively analyze its smoothness, autocorrelation, and normality to determine the optimal length of input feature sequence. Second, the non-stationary landslide monitoring data are decomposed into multiple smooth time series using the ensemble empirical mode decomposition(EEMD) method, which is then classified into three types combining the sample entropy and K-means algorithm, namely high, medium, and low frequency. Finally, comparing the prediction accuracy of different neural networks, the prediction models suitable for three types of time components are constructed respectively, and then the prediction results are superimposed to realize the high-precision prediction of landslide displacement. The testing results of Beidou/GNSS monitoring data of typical landslide body in experimental area show that the combination prediction model proposed in this paper has a better applicability to the landslide monitoring data containing significant gross errors, and can significantly improve the prediction accuracy of landslide displacement compared with single and existing combination models.

We focus on the complex characteristics of groundwater level data, including nonlinear trends, seasonal fluctuations, and random disturbances, and introduce the Prophet time series prediction model developed by Facebook. The aim is to use its nonlinear trend capture, seasonal fluctuation analysis, and flexible response ability to outliers and data missing to significantly improve the accuracy of groundwater level anomaly identification. Through observation data from Beilin district seismic station in Suihua city, Heilongjiang province, it is shown that the Prophet model performs well in capturing dynamic characteristics of time series data and can effectively identify anomalies. The high fitting accuracy and predictive ability of adjusted model have been confirmed, with low prediction error and high determination coefficient. In addition, the model identifies water level anomalies related to earthquakes in earthquake prediction, providing a new perspective for earthquake precursor research. This study demonstrates the effectiveness of Prophet model in processing complex time series data, providing a new tool for earthquake prediction.

We lay a shallow seismic exploration line L1 with a length of 10.77 km extended southward across Serteng mountain piedmont fault. The results show that the Sertengshan piedmont fault is clearly visible on the profile, and the main fault has bifurcations in the upper part, consisting of faults F_P1.1 and F_P1, both of which are normal faults with an apparent inclination towards the south. Combined with geological and geomorphological surveys, the latest active age of the F_P1.1 fault is Holocene. We carry out a composite drilling geological section for the fault F_P1 of the Serteng mountain piedmont fault and distinguish two faults, Fa and Fb. Based on the results of optical stimulated luminescence dating, the faults Fa and Fb are Holocene faults and the latest active age is later than 2.5±0.6 ka BP. The Serteng mountain piedmont fault has a forked structure near the surface, which reflects the multiple activities of the fault in the late Quaternary.

Early research on broadband seismometer azimuth mainly based on the P-wave particle polarization principle and calculates the angle corresponding to the minimum value of P-wave energy on the lateral component to obtain the angle between the north-south direction of seismometer and the geographic north pole. In addition, the elliptical motion characteristics of Rayleigh waves can be used to estimate the seismometer azimuth of stations with azimuth deviation. We estimate the seismometer azimuth deviation of 20 stations of CENC based on the two different methods. The results show that the azimuth deviation calculated by the two methods is in good agreement, indicating that Rayleigh wave analysis can be appropriately added in future seismometer azimuth detection.

Based on the seismic ambient noise cross-correlation method, we calculate the relative velocity variation within the underground medium. We utilize continuous waveform data from the CCH and BDS stations at Dongtan mine, spanning from 2 a.m. on February 8, 2022, to 7 a.m. on March 8, 2023. The analysis focuses on the correlation between these velocity changes and both the mining operation and seismic activity at the 63^up06 working face. The findings reveal a notable impact of the mining operation at the 63^up06 working face on the wave velocity within the underground medium. Additionally, a clear correlation exists between the changes in wave velocity and the incidence of mine earthquakes, highlighting the interplay between variations in underground stress levels and seismic events. Furthermore, the release of underground stress during mine earthquakes can lead to slight fluctuations in wave velocity.

Large earthquakes can simultaneously excite numerous normal modes of Earth's free oscillations. Due to the Earth's ellipticity, rotation, and internal anisotropy, these normal modes undergo splitting, with frequencies of splitting modes becoming very close(only a few μHz apart). This imposes higher demands on the detection of Earth's free oscillation modes. This article derives and verifies a new method for detecting free oscillation modes based on normal time-frequency transform(NTFT). Taking the detection of ₃S₁ splitting modes as an example, compared with the classical FT spectrum method and the latest OSE method, our method has higher frequency resolution for detecting free oscillation modes.

On August 23, 2023, a M4.6 earthquake hit the Pulandian, Liaoning province. To describe the source characteristics of this earthquake, we explore its incubation and seismogenic mechanisms. In this paper we determined the focal depth of the Pulandian M4.6 earthquake, and calculate the focal mechanism solution, moment tensor solution and centroid depth of the earthquake, obtain the center of focal mechanisms of the earthquake. In the same, we analyze the relationship between focal mechanism and tectonic stress field, and fit the fault plane according to the results of small earthquake relocation. We preliminarily determine the seismogenic fault plane of this earthquake. The results show that the initial rupture depth of the Pulandian M4.6 earthquake is 12.0 km. The focal mechanism solution is strike: 50°, dip: 75° and rake: －169° for nodal planeⅠ, strike: 317°, dip: 80°and rake: －15° for nodal plane Ⅱ, the moment magnitude is M_W4.8, while the centroid is 12 km. The seismic moment M₀ is 1.796×10¹⁶ Nm, the moment tensor solution is M_rr: －0.004, M_tt: 0.946, M_pp: －0.942, M_rt: 0.017, M_rp: －0.305, M_tp: －0.125. The center of focal mechanisms solution is strike: 47.03°, dip: 79.04° and rake: －168.15° for nodal planeⅠ, strike: 314.75°, dip: 78.37°and rake: －11.19°for nodal plane Ⅱ. The relative shear stress and relative normal stress generated by the tectonic stress field acting on the nodal plane Ⅰ are 0.877 and －0.544; on nodal plane Ⅱ they are 0.911 and 0.161. The fitted seismogenic fault plane is strike: 148.91°, dip: 89.85° and rake: 26.47°. The analysis shows that the Pulandian M4.6 earthquake occurred on the Pulandian-Changhai NW tectonic belt. It is a natural earthquake that after stress accumulation occurs along the optimal nodal plane of the stress field and takes left-lateral strike-slip as the dislocation mode.

We use the precise clock offset data released by IGS to analyze the key performance indicators of six GPS-Ⅲ satellite clocks that have been launched and operated in orbit, including frequency accuracy, frequency drift rate, and frequency stability, and compare them with the previous GPS clocks. The results show that rubidium clocks carried by GPS-Ⅲ are generally stable in operation and have good data quality, which is related to the improvement of anti-interference ability. There is no significant change or difference in time frequency performance, which is basically consistent with the previous generation Block ⅡF rubidium clocks. The frequency stability of GPS-Ⅲ rubidium clocks are better, which helps to improve the accuracy of PNT services. The research can provide reference and guidance for the upgrading and transformation of other GNSS system satellites.

By utilizing satellite time-monitoring data to mitigate the impact of satellite-side and transmission path errors in standard single-point positioning, the timing stability and positioning accuracy at the user end are improved. At the same time, the timing deviation of the user relative to the satellite navigation system is corrected to be relative to the national standard time. Taking GPS as an example for a short baseline experiment, the results show that in terms of timing, the mean timing deviation of the L1 frequency band was improved from -42.8 ns to 5.4 ns, and from -53.6 ns to 2.8 ns for the L2 frequency band; in terms of positioning, the mean positioning errors in the X, Y, and Z directions for the L1 frequency band were improved by more than 55%, and by more than 77% for the L2 frequency band. This proves that by using satellite time-monitoring data, users of the satellite navigation system can not only enhance the pseudo-code positioning accuracy to the sub-meter level but also obtain the deviation relative to the national standard time, achieving ns-level timekeeping of the national standard time.

GNSS technology is widely used in landslide monitoring, but it is difficult to install monitoring equipment in complex high-risk landslide environment. UAV-dropped is expected to realize unmanned deployment of monitoring equipment. In view of the problems of UAV-dropped endurance and terrain threat in complex environment, the path planning of UAV-dropped is particularly critical as the basic mission. In this paper, the path planning problem of UAV-dropped in complex mountainous area is studied in three aspects: 3D real map construction of geological disaster area, cost function design and flight path planning algorithm. In addition, whale optimization algorithm based on adaptive weight and Levy flight strategy is applied to the flight path planning of UAV-dropped. Taking Hongyanzi landslide in Hanyuan county, Sichuan province as the study area, the path planning of UAV-dropped GNSS monitoring equipment was realized.

The road in the open-pit mining area is narrow, with many curves, rubbles, and bumpy roads. The special surface closed loop leads to system model anomalies and measurement noise uncertainty problems during positioning of transport vehicles. To address the above issues, we propose an SVD-AUKF algorithm with a noise estimator. The algorithm suppresses the pathological matrix that occurs during the filtering process by singular value decomposition of the covariance matrix, estimates the measurement noise using the Sage-Husa estimator, reduces the system state error, and introduces an adaptive factor to correct the system model error in real time to suppress the filtering divergence. We apply and test the proposed SVD-AUKF algorithm to the GPS/INS integrated navigation system model on a vehicle in a mining environment. The results show that the new algorithm has good applicability, with position accuracy improvements of approximately 50.69%, 51.20%, and 48.54% compared to the AUKF algorithm; speed accuracy improvements of approximately 31.24%, 33.83%, and 31.30% in E, N, U directions, which can effectively improve the filtering performance and increase system positioning accuracy.

By establishing a BDS transmission reference control surveying model, the paper compares and analyzes the high-precision control surveying positioning performance differences between BDS-3/BDS-2 and BDS-3+BDS-2. Nine first-order control points of a project in Beijing and three IGS tracking stations in China and its surrounding areas were selected to establish a high-precision control surveying positioning network that supports both GPS and BDS-3/BDS-2 signals. Continuous observation data from three time periods(days 153-155 of 2023) were selected, and four schemes were designed for data processing. The positioning performance was compared and analyzed from two aspects: the transmission results of the initial reference and the adjustment results of control surveying. The results show that compared with GPS, BDS-3 and BDS-3+BDS-2 have only millimeter-level differences in N, E, U, horizontal, and point directions, while BDS-2 has differences ranging from millimeters to centimeters in the U and point directions, and only millimeter-level differences in the N, E, and horizontal directions. The measurement accuracy of BDS-3 is basically equivalent to that of BDS-3+BDS-2, while BDS-2 has lower accuracy than the other two, especially in the U direction.

Based on the observation data from 163 GPS continuous stations in Qinghai-Xizang plateau and adjacent areas from 2010 to 2020, we solve the GPS vertical coordinate time series. Using the global loading grid data provided by GFZ, the environmental loading displacements are calculated by double cubic linear interpolation method, and the GPS vertical time series are corrected. The results show that the hydrological loading has the greatest influence on GPS station displacement in the study area, which is larger in the southern stations, with a RMS of 9.45 mm(MUET site). By comparing the changes of weighted root mean square(WRMS), annual term amplitude and velocity uncertainty before and after environmental loading correction, it is found that the environmental loading correction is able to weaken the nonlinear variations of vertical time series of most GPS stations in Qinghai-Xizang plateau and adjacent areas and it can accurately improve the velocity accuracy of most GPS stations.

We calculate local terrain and isostatic correction using spectral combination and nested different topographic resolutions and determine the free air gravity anomaly grid by the gravity reduction method. The results show that the accuracy of terrain correction calculated by the spectral combination method is the best in the test area, and the accuracy of isostatic correction calculated by nested different resolutions in the central and edge regions is best. When the integral radius is 1° and 2°, there is little difference between the local terrain correction, and the isostatic correction results differ significantly. The accuracy of free air gravity anomaly grid results obtained from the complete Bouguer and isostatic gravity anomalies by gravity reduction is the same, and the accuracy of the results is much higher than that obtained from the direct grid of free air gravity anomalies at ground points.

Aiming the issue of reduced accuracy in sea surface height inversion using global navigation satellite system interferometric reflectometry (GNSS-IR) due to elevation angle deviation, a correction method for elevation angle deviation based on the ionospheric region is proposed. To verify the reliability of the correction method for elevation angle deviation, a comparison is made between the inversion results before and after applying the correction to the elevation angle deviation. The results indicate that the inversion results, after correction for elevation angle deviation, exhibit higher accuracy. The method for correcting elevation angle deviation can effectively rectify the deviation caused by the ionosphere.

Tonga volcano violently erupted at 12:26:30 on January 15, 2022. The atmospheric pressure observation station of Yunnan Geophysical Network clearly recorded the intense pressure change process caused by the eruption. The atmospheric pressure change forms and characteristics of all observation stations were highly consistent, with the average peak amplitude of 2.16 hPa and the average propagation velocity of 314.30 m/s. The wave train has the properties of Lamb wave propagation, and the atmospheric pressure observation data are basically reliable. The water level observation in Yunnan deep well clearly recorded the water level change caused by the short-time pressure change. The static pressure efficiency of the observation well was estimated by the ratio of peak water level change to the amplitude of pressure change. We analyze by regression the short-time pressure change and well water level data, and take the determination coefficient, regression line slope and static pressure efficiency as indicators. Then we analyze the bearing property and stress-strain sensitivity of the borehole water-bearing system. The results show that the water level and pressure observation data of 25 wells, such as Kunming station, have high goodness of fit, small lag time, and good permeability of aquifer. The atmospheric pressure efficiency of 7 wells in Kunming station, Daguanxijiagou, Tengchong station, Ludianciyuan, Nanhua, Shiping and Zhaotong SK3 is slightly higher, and the sensitivity of well water level to stress and strain may be low. The larger the coefficient of determination for an observation well, the higher the probability that its monitoring and forecasting performance will be assessed as qualified.

We propose an adaptive stochastic model for low-cost receiver precise point positioning(PPP) in complex environments. The model improves PPP performance by adaptively adjusting the weight ratio of pseudorange observations and carrier phase observations of all GNSS satellites in a single epoch. We take the Hexinxingtong UM982 low-cost GNSS receiver to conduct PPP tests in three complex environments, including tree shade, high buildings and glass walls. The static PPP results show that the positioning accuracy of adaptive stochastic model is improved by 24%, 45% and 50% in three complex environments, respectively, and the convergence time is shortened by 49%, 27% and 24%, respectively, compared with the traditional empirical weighted stochastic model. The kinematic PPP results show that the positioning accuracy of adaptive stochastic model is improved by 35% in complex environment.

Aiming at the problem of missing data and gross errors in GRACE Level1B observations, we propose an optimization method to complete the missing data of SCA1B. At the same time, we use an optimization strategy to eliminate gross errors for KBR1B and kinematic orbit data, then use these data sets to invert the time-varying gravity field model. In addition, based on synthesized data and real data respectively, we analyze the impact of the error from ACC1B in the Y direction on the inversion results and propose an optimization correction strategy. The optimized completion method can recover the missing data of SCA1B effectively, and fully considers the variational characteristics of the observations throughout the arc. The accuracy of the result derived from ACC1B data after correction using the optimization strategy is 3.7 mm, which is higher than the result derived from the uncorrected data. It can be seen from the results that the overall accuracy of the gravity field model solved by the data processed using the optimization method is like that of the three official centers. However, the detailed signals performance of the results from different institutions in regionals are different. It shows that the optimized data preprocessing strategy is effective and feasible.

To reduce the positioning errors of global navigation satellite system(GNSS) in complex environments, we propose a method that combines high-precision points with distance intersection to accurately estimate the coordinates of an undetermined point. The observation equation is constructed as a nonlinear Gauss-Helmert model. To address the nonlinearity within this model, we introduce a back-propagation(BP) neural network for auxiliary processing. Compared with the traditional linearization methods, the BP neural network can effectively fit complex nonlinear functional relationships. The simulation experiments and actual measurement results show that this method can significantly reduce the impact of complex environments on positioning accuracy, and improve the positioning accuracy in E, N and U directions by 78.1%, 72.8%, and 79.2%, respectively.

We focus on the apparent resistivity method in earthquake precursor observation, develop a detection device suitable for its observation system, and conduct theoretical simulation and on-site testing on the device. First, we analyze the apparent resistivity measurement system, and point out the shortcomings of existing detection systems. Second, according to the demand analysis of observation system, the corresponding detection index and circuit are designed, and the detection accuracy of instrument is designed to 0.001 level to improve the performance. Finally, the developed device is theoretically simulated using Simulink, and the impact of interference on observation results is compared and studied. Random earthquake observation stations in Gansu region are selected for on-site testing. The results show that the developed device can not only visually display the working status of apparent resistivity measurement system, but also accurately evaluate the real-time superiority and inferiority of apparent resistivity observation system, and fill the gap in detection device of apparent resistivity observation system and provide great convenience for frontline earthquake workers.

Based on the measured ground motion data of the M_S4.5 earthquake in Luding, Sichuan in 2023, we conduct an asymmetric slope time-frequency domain data analysis. The results show that: 1) The PGA amplification factor at monitoring point 1^# on the right bank is highest, with horizontal and vertical values of 3.16 to 15.08 and 4.37, respectively. 2) The maximum Arias intensity on the right bank is 2.24 cm/s, and the maximum Arias intensity on the left bank is 0.79 cm/s, indicating that the seismic energy on the right bank is stronger. 3) The dominant frequency distribution of the monitoring points on the right bank of the slope is 2 to 9 Hz, while the dominant frequency on the left bank is 2 to 17 Hz. The energy presented by the seismic amplitude is more concentrated in the horizontal than in the vertical direction. On the right bank of the asymmetric high steep valley slope, the protruding peninsula shaped slope terrain has a stronger response than the straight shaped slope.

Using the BIC_tp noise model identification criterion, we analyzed the temporal noise characteristics of sea surface height (SSH) and their influencing factors at 350 virtual coastal satellite altimetry stations worldwide. Furthermore, we explored the impact of environmental loads on SSH time series and the characteristics of sea level changes across different time spans. The results indicate that the assumptions of FN+WN, FN+RW+WN, and PL+WN noise models can lead to errors in estimating the velocity parameters of SSH time series. Furthermore, there is a diversity of SSH time series noise models, and the velocity uncertainties under different noise models vary significantly. Identifying their noise characteristics is crucial for accurately obtaining sea level change parameters. The average global sea level rise rates for the different time spans of 1993 to 2006, 2007 to 2020, and 1993 to 2020 are 2.59 mm/a, 4.41 mm/a, and 3.08 mm/a, respectively, indicating that sea level is accelerating its rise. The optimal noise models and velocity changes for the SSH time series before and after environmental loading correction are not significant, indicating that the load effect has a relatively minor impact on the characteristics of SSH time series.

We use ascending and descending orbit data of Sentinel-1A satellite to obtain high-precision coseismic deformation fields of Jishishan M_S6.2 earthquake using differential interferometry technology, and employ SBAS-InSAR technique to acquire cumulative deformation time series in the area near epicenter over the past six months. The results show that surface deformation is primarily uplift, with the maximum LOS coseismic displacements of about 6.8 cm and 7.3 cm for ascending and descending orbits, respectively. The rupture mainly occurred at depths ranging from 7 to 15 km underground, with a maximum slip of approximately 0.55 m. Within two months after the earthquake, there is an expanding trend of uplift deformation in the near-field and northwest side of the epicenter. We conclude that the Jishishan earthquake trended northwest, dipping northeast, and the seismogenic fault belongs to the hidden branch fault on southeast side of the southern margin of Lajishan fault zone. The large-scale sudden deformation after the earthquake may be related to pore pressure release, and the surface stability needs further observation.

We use the ESPAC method to detect the geological structure below the Mogao Grottoes of Dunhuang using observed data obtained by linear array. The detection results show that the layers determined by microtremor survey method are clearly visible, with significant fluctuations of each layer on the two-dimensional microtremor apparent S-wave velocity profile. The covering layer is thick at both ends and thin in the middle. There is a bedrock intrusion in the middle of profile, and there is a solitary stone with a width of 30 m×40 m. Using geological survey data calibration of the relevant region, we show that the thickness of Quaternary cover layer represented by mixed backfill, fine sand, and gravel sand is about 8 m, and the burial depth of complete bedrock surface is about 30 m. The research results can provide geophysical basis for the protection of cultural relics in the Mogao Grottoes.

This paper uses the time series of the geocentric motion provided by the Center for Space Research at the University of Texas at Austin as experimental data. First, empirical mode decomposition (EMD) is applied to the data for noise reduction. Then, fast Fourier transform (FFT) and continuous wavelet transform (CWT) are utilized to perform frequency domain transformation, power spectrum analysis, and extraction of periodic terms. Finally, an autoregressive integrated moving average (ARIMA) model and exponential smoothing method are employed to forecast the geocentric motion for the next 20 months. The results indicate that the amplitude and phase of the annual term extracted using FFT are quite close to previous studies on the geocentric motion. The ARIMA model provides better predictions for the Y-direction motion of the geocentric motion over the next 20 months, while exponential smoothing method offers more accurate predictions for the X and Z directions.

Current robust fitting algorithms are prone to failure in point cloud data with a high contamination rate. To solve this problem, we introduce a weight matrix in the plane fitting method based on eigenvalue decomposition, where the initial weight is determined based on the normal vector information of point cloud, thus reducing the influence of outliers. We further use the scale-adaptive method to enhance the robustness. The scale factor of this method gradually decreases as the number of iterations increases, effectively solving the problem of low breakdown ration of traditional M estimation. The experimental results show that the breakdown ratio of the proposed algorithm is higher than that of existing robust point cloud fitting methods, and it can accurately estimate plane parameters in the point cloud data with a high outlier ratio.

This paper applies generative adversarial networks (GAN) to the denoising of GPS coordinate time series. The generator produces time series data, which is then distinguished from clean time series data by the discriminator. During training, the generator is iteratively optimized until the discriminator can no longer differentiate between the generated data and the real clean data, thereby achieving the denoising effect. Simulation results show that, compared with traditional methods such as WNNM, BiLSTM, and SSA, the GAN method achieves the closest correlation with the original noise in key indicators such as residual amplitude, residual spectral index, and velocity uncertainty, demonstrating more efficient denoising performance. Experimental results using real data further validate the effectiveness of the proposed algorithm in denoising actual GPS coordinate time series.

In order to investigate the tectonic characteristics, planar distribution, and active period of the buried fault (Hetan-Guotan buried fault) in the northern margin of Zhongwei basin, Ningxia, we apply the method of shallow seismic exploration to the attitude, character, position and burial depths of uppermost aspects of this buried fault. Furthermore, the composite drilling section explores the exact location and more reliable uppermost point, and we identify the latest active period of this buried fault by a sample dating test. The results show that the Hetan-Guotan buried fault is a normal fault trending near EW and dipping S, extending for approximately 12.5 km. The uppermost point of this fault is buried at a depth of more than 38 m, and the apparent dip of the fault is 75°. The Hetan-Guotan buried fault is not an active fault, because its latest active period was the middle-late middle Pleistocene, and it has been inactive at the end middle Pleistocene and since the late Pleistocene.

The dynamic evolution characteristics of regional gravity field at different time scales before the Wushi M_S7.1 earthquake are obtained by using the mobile gravity observation data of southern Tianshan area from 2020 to 2023, and the approximate field source depth corresponding to each order wavelet gravity detail is obtained by power spectrum analysis method. The results show that: 1)Before the Wushi M_S7.1 earthquake, the gravity changes in Wuqia-Bachu and Aksu areas show an obvious four-quadrant distribution, and the epicenter is located at the edge of four quadrants and near the zero line. 2)The wavelet transform results of gravity field in southern Tianshan area from 2020 to 2023 show that before the Wushi M_S7.1 earthquake, the gravity changes in Wuqia-Bachu area shows obvious four-quadrant distribution, and the epicenter is located at the edge of four quadrants and near the zero line.

In response to the problems of premature convergence and easily falling into local optima when using traditional global optimization algorithms such as particle swarm optimization and simulated annealing for dispersion curve inversion, we introduce the bald eagle search (BES) algorithm into the study of dispersion curve inversion. Through inversion analysis of multiple typical geological theoretical models and measured data from the Wyoming region of the United States, the results show that the BES algorithm has strong applicability, stability, and noise resistance in the inversion of Rayleigh wave dispersion curves.