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.

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.

This paper is based on the AR products from CODE, GFZ, CNES, PRIDE Lab, and in-house AR products for the performance assessment of PPP-AR in terms of convergence time, time to first fix and positioning accuracy. The experiment uses seven-day observation data from 40 IGS stations in 2022 and precise products of each AR product. The results show that under the static solution mode with confidence level of 95%, five products have the most obvious improvement in positioning accuracy at the time of 1 h, which are 46.58%(3.4 cm), 41.10%(3.0 cm), 45.21%(3.3 cm), 34.25%(2.5 cm), and 41.10%(3.0 cm), respectively. In the simulation-kinematic solution mode, the positioning accuracy of the five products in the E and N directions can reach the millimeter level, and the accuracy of the U direction is slightly improved than that of the floating point solution, among which the accuracy of GBM product is the least improved, and 72.73%(2.4 cm), 47.37%(0.9 cm) and 5.41%(0.2 cm) were increased in E, N and U directions. WUM and COM products had better improvement effect. They were 81.82%(2.7 cm), 63.16%(1.2 cm), 24.32%(1.1 cm) and 81.82%(2.7 cm), 63.16%(1.2 cm), 15.58%(0.8 cm).

We propose a gait recognition model that fuses long short-term memory(LSTM) and convolutional neural network(CNN), the model can automatically extract activity features and classify them using a small number of model parameters, and we utilize this model for user identification.  The experimental results show that the recognition accuracy of the model is about 97.68 % and the loss value is about 0.05, which significantly improves the recognition rate compared to other models.

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.

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.

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.

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.

By analyzing the problems existing in the cycle slip detection method of wide-lane phase minus narrow-lane pseudorange and geometry-free combination, we propose a cycle slip detection method that considers the GNSS satellite elevation factor and is suitable for universal GNSS observation data. After detecting the epoch position where the cycle slip occurs, we use the method based on spatial search and minimum criterion of objective function to repair cycle slip. We then validate the proposed cycle slip processing method by selecting satellite data of different elevations observed from the universal GNSS receiver located at a landslide hazard near the Linxia Highway in Gansu Province. The results show that the proposed method is effective. the cycle slip detection and repair method can realize the accurate detection and repair of cycle slips in carrier phase data with different elevations and can reduce the influence of GNSS data observation error on the cycle slip test. It is feasible for the cycle slip detection and repair in universal GNSS observation data.

On August 23, 2023, the seismic instrument system of the Hongyanhe Nuclear Power Plant successfully recorded a M4.6 earthquake in Pulandian, Dalian. This is the first complete strong earthquake record around the nuclear power plant area in China. We analyze the seismic data recorded by the system, and the data playback shows that the seismic recorded data was clear and complete, and the peak value of the free field monitoring data is consistent with the calculation result of the seismic attenuation empirical formula. Comparing the peak acceleration values of seven monitoring points data, the result shows that the nuclear power plant building has an amplification effect on seismic acceleration signals, and the amplification coefficient is positively correlated with building elevation. The conclusion of spectral analysis of peak acceleration data shows that predominant frequency points of seismic response data in nuclear power plant buildings are mainly concentrated in the range of 10 to 20 Hz, and seismic acceleration signals in the range of 10 to 20 Hz also have certain destructive effects on the building. This conclusion is inconsistent with the seismic triggering filtering range (1 to 10 Hz) specified in NB/T 20076-2012 seismic instrument criteria, due to the fact that the seismic trigger recognition algorithm of the seismic instrument system is set in a filtering range of 1 to 10 Hz. This defect will reduce the performance of the seismic instrument system’s depth safety defense.

Based on the digital seismic observation report recorded by the Inner Mongolia seismological network, using Pg and Sg wave data of 3 398 earthquakes with M≥1.0, we deploy the double-difference tomography method to study the underground structure in the northwest margin of the Ordos block(37°~43°N, 104°~110°E) and conduct the earthquake relocation. At the same time, we analyze in detail the velocity structure of the upper crust in the area. The results show, from the velocity structure of Pg wave, there is a continuous irregular high-speed body from northeast to southwest at 5 to 10 km, and the high-speed body at 15 to 20 km is no longer continuous but scattered. From the velocity structure of the Sg wave, the velocity structure at 5 km and 15 km is basically consistent with that of the Pg wave. There are more low-speed areas at 10 km and 20 km, and earthquakes mostly occured at the junction of high and low velocity bodies and around faults, which proves the fragility of the crust in this area.

Based on the mobile geomagnetic observation data of four consecutive periods from 2020 to 2023 in the border area of Hebei and Shandong, this paper analyzes the temporal and spatial variation characteristics of the regional lithospheric magnetic field and its relationship with the Pingyuan M_S5.5
 earthquake on August 6, 2023. Combined with the laboratory results of rock magnetism and the theory of fault meta-instability, it discusses the meta-instability characteristics before the plain earthquake and the seismogenic mechanism.The results show that:1) The spatial and temporal variation characteristics of the lithospheric magnetic field in the study area have obvious anomalies before the Pingyuan M_S5.5 earthquake. There is always a weakening area of the horizontal vector near the epicentre from 2020 to 2023, and with the passage of time, the weakening area with horizontal vector gradually approaches the epicentre; there is always a 0 value line of the total geomagnetic intensity, magnetic declination and vertical component near the epicentre. 2)The time series variation of the observation points near the epicentre in 2020-2021 is highly consistent, which better reflects the synergistic activity state of the faults near the observation points, and may be used as one of the characteristics to identify the sub-instability stage of the faults.3) By analyzing the relationship between stress and magnetic field intensity, it can be seen that the total intensity of geomagnetic field in the middle and eastern segment of Linnan fault and the north-eastern segment of Liaocheng-Lankao fault increases, and the crustal stress should be in a release state; the total intensity of geomagnetic field in the western segment of Linnan fault and Tangyi fault decreases, and the crustal stress should be in an accumulation state. The stress release of the former can promote the stress accumulation of the latter, which may lead to the instantaneous increase of the shear stress of the latter and instability when it exceeds its strength, thus triggering the plain earthquake.

We use the catalog of modern earthquakes in the Beijing-Tianjin-Hebei region from 1980 to 2022 as a statistical sample. To grid the study area, we use the incompletely centralized Voronoi segmentation method based on spatial segmentation. Based on the Poisson model, the probability model of earthquake risk is established, and we conduct the probability prediction of small and medium earthquakes at the medium-term scale of the study area. The results show that the calculated probability of relatively high value region has a certain correlation with the occurrence of earthquakes with M_S≥3.0, which can provide a reference for medium and long term earthquake prediction.

At present, the seismic stations need to use two sets of acquisition systems to monitor and record weak signals and large seismic signals. To address this problem, this paper proposes a data acquisition method that automatically switches acquisition modes. When monitoring weak signals under normal conditions, the acquisition system uses small-signal channel ADCs for graded sampling, and when large earthquakes and giant earthquakes occur, the system automatically switches to use large-signal channel ADCs for sampling. Experiments show that the dynamic range of the acquisition system reaches 128 dB for single AD data acquisition at 100 Hz sampling, and 158 dB for double AD data acquisition at 100 Hz sampling.The improved data acquisition method effectively improves the dynamic range of seismic signal acquisition, and improves the performance of the data acquisition system to a certain extent without increasing the hardware cost.

To monitor land subsidence in Suzhou city during the rapid construction of subway, this paper focuses on the temporal analysis of ground deformation in Suzhou central area with PS-InSAR, using TerraSAR-X and Sentinel-1 data in 2010-2020 combined with leveling observation data. The results show that the ancient city area of Suzhou is stable relatively, and the maximum settlement points are in Yangshan with the settlement rate exceeding -20 mm/a. The subsidence trend has slowed down in Suzhou central area, with a significant reduction in the subsidence range, and several small uplifted areas have appeared particularly in the east of Caohu, and the north of Taiyang road. The settlement of buildings nearby the subway accelerate significantly with the influence of subway construction, especially for new buildings, whose own short-term sedimentary settlement is superimposed with the settlement of subway construction, resulting in the subsidence rate exceeding -10 mm/a.

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.

Based on the observation data of the reservoir network in the lower reaches of the Jinsha River, we utilize the PhaseNet model, a deep neural network, to pick up seismic phase arrival times. By combining the methods of group triggering and isochronous octree searching, we associate seismic phases and use the NLLoc non-linear location method to automatically locate the microseismic sequence in Yuanbaoshan, thereby quickly constructing a high-precision machine learning catalog. The seismic numbers provided by the machine learning catalog are 3.89 times that of the manual catalog, with a matching rate of 94.76%. 89.8% of the events have a time deviation of less than 0.5 seconds, 98.7% of the events have an epicenter location deviation of less than 3km, 90.9% of the events have a source depth deviation of less than 5km, and 91.5% of the events have a magnitude deviation of less than 0.2. Based on the focal mechanism solution and the seismic distribution characteristics provided by the machine learning catalog, we infer that the seismic events of this sequence are caused by the sub-branch fault of the Ebian-Jinyang fault. The earthquake catalog construction method of this study can provide rapid data support for determining earthquake trends in the reservoir area downstream of the Jinsha River, precise aftershock locating, determination of focal mechanism nodal planes, detection of hidden faults, seismic event structures, and body wave travel time tomography imaging.

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.

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.

In view of the non-linearity and non-stationary characteristics of satellite clock bias(SCB) time series, as well as the interference between trend and noise components that may affect the accuracy of prediction, this paper proposes a SCB prediction model(SSA-ANFIS) based on singular spectrum analysis(SSA) and adaptive neuro-fuzzy inference system(ANFIS). This paper first uses SSA to decompose and reconstruct the first-order difference sequence of clock bias, obtaining the trend component and the residual component. Then, it uses the ANFIS model to predict the reconstructed components, and superimposes and restores the predicted results to obtain the final predicted clock bias value. Finally, through experiments, this paper compares the proposed model with GM, QP, LSTM and ANFIS models. The results show that SSA-ANFIS model can effectively improve the prediction accuracy of the single model. Compared with the LSTM and ANFIS models, its prediction accuracy increased by 25.7%-40.7% and 39.4%-45.7%, respectively.

Based on the mechanism of deformation anomaly, we analyze the correlation of several anomalies before the earthquake. Combined with observation facts and experiments, we explore the possible organic connections between them. The explanations are given uniformly from the mechanism for the anomalies before and after the earthquake, such as the severe drought before the earthquake, the heavy rain after the earthquake, the sand jetting and ground collapse during the earthquake, as well as ground sound and light, the ionosphere anomaly before the earthquake, the atmospheric electrostatic anomaly before the earthquake, and the electromagnetic wave anomaly before and during the earthquake. We consider that these anomalies are all related to the formation, expansion and rapid disappearance of micro-crack expansion bodies near the seismic source before the earthquake.

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.

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.

In view of the problem that GNSS observation data in complex environments such as landslide is affected by factors such as high occlusion and multipath, it is easy for the success rate of ambiguity resolution to decrease and the positioning reliability to deteriorate. We propose an improved algorithm of GNSS partial ambiguity resolution based on IGGⅢ robust estimation. The robust estimation algorithm is introduced to weaken the influence of gross error on parameter estimation and improve the accuracy of floating-point ambiguity. In addition, to avoid the influence of residual error on the subsequent integer ambiguity resolution, and to further improve the success rate of ambiguity resolution, we adopt a partial ambiguity resolution improvement strategy based on conditional variance matrix method. The experimental results show that compared with the full ambiguity resolution method, the algorithm can significantly improve ambiguity resolution rate, and the results of three stations are increased by 5.9%, 52.7% and 48.0%, respectively. At the same time, compared with the traditional Kalman filter algorithm, the improved robust estimation algorithm significantly improves the situation of incorrect ambiguity resolution and greatly improves the GNSS positioning accuracy. The horizontal RMS is better than 1 cm and the vertical RMS is better than 2 cm.

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.

In recent years, high-precision time-varying gravity observation technology on land has developed rapidly. In addition to the continuous improvement of absolute gravity equipment such as quantum gravimeters, the layout of gravity observation network in Chinese mainland has also become increasingly perfect. A microgal-level time-varying gravity field observation network with a resolution better than 100 km has been built in the main active tectonic regions within Chinese mainland. This paper begins with the technology and method of terrestrial time-varying gravity observation. We first introduce the concept of land time-varying gravity observation, the classification of gravity instruments, and the construction and development of the high-precision gravity observation system in Chinese mainland, as well as the problems and optimization suggestions for this system. Then, we introduce the time-varying gravity technology methods and model products, and their applications in various fields. Finally, we discuss issues related to the data products and applications of land time-varying gravity and propose a design and layout plan for the next-generation land gravity observation system that aims to address the scientific questions of the China seismic experiment site and deep earth science. As a crucial part of the national geophysical monitoring network, terrestrial time-varying gravity observation generates high-precision model products that can not only aid in the study of deep crustal dynamics but also provide crucial data support for geophysical navigation and national defense.

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.

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.

For the GPS modernization by adding L₅ frequency observable values, we propose a new algorithm of GPS single epoch double-difference integer ambiguity resolution called GPS Main and Auxiliary Frequency Correlation Algorithm (MAXCOM). We use only GPS single epoch data, including pseudo-range observation values and phase observation values of main-frequency and auxiliary-frequency for the algorithm. We set L₁ is set as the main-frequency and L₂ or L₅ as the auxiliary-frequency by expanding the idea of dual frequency correlation method to establish an internal-correlation model between double-difference phase observation values of GPS main-frequency and auxiliary-frequency. We conduct a cross-search between the adjustment value-domain of observation values and the value-domain of ambiguity, and then use the ratio significance test method to determine the double-difference integer ambiguity of GPS main-frequency. The example shows that for the three original phase observations of L₁, L₂ and L₅ frequency after GPS modernization, selecting L₅ as auxiliary-frequency of the algorithm is slightly better than L₂ in terms of efficiency and success-rate of ambiguity resolution. The proposed algorithm is effective.

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.

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.

Based on the data of fault location, velocity structure and 292 focal mechanisms in Tangshan area, we carry out the spatial grid division of stress field inversion, and retrieve the whole and major fault stress fields in Tangshan area by using grid search method. The results show that: 1) Before and after the 2011 M_W9.0 Tohoku earthquake, the overall regional tectonic stress field in Tangshan area rotated slightly clockwise, and the stress state changed from strike-slip to positive strike-slip, which may be mainly caused by the spatial location change of the focal mechanism solution. 2) The Tohoku earthquake in Japan made the plunge angle of σ₁-axis in some areas of Tangshan change from steep to horizontal, and the stress field has a small clockwise rotation. 3) At present, the overall regional stress field in Tangshan area is characterized by σ₁-axis NEE-SWW direction, σ₃-axis near NS direction, and R value is 0, which is consistent with previous research results. It shows that the stress field in Tangshan area has been adjusted to the state before the 1976 Tangshan M_W7.8 earthquake, and the stress state is partial tensile. 4) At present, 6-20 km of Weishan-Fengnan, Douhe, Luanxian-Leting fault sections and 11-20 km of Douhe fault section are strike-slip stress states; The stress state of 0-10 km of Douhe fault section is positive strike-slip. 6-20 km of Leizhuang, Chenguantun and Lulong fault segments, 0-5 km of Weishan-Fengnan fault segments are positive fault type stress states. The σ1-axis directions of the above faults are consistent with the principal strain direction and the fast wave polarization direction of shear wave splitting in this region. 5) The Tangshan area is in a relatively unified NNW-SSE tensile action, but the Guye and Luanxian areas are in NEE-SWW compressive action.

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.

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.

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.

This paper proposes a noise reduction algorithm combining adaptive variational mode decomposition and KSVD dictionary learning. In this method, we fully retain the effective information in the monitoring sequence by denoising the sub-sequences after the decomposition of the monitoring sequence, and consider the features in the residual sequence. We take the deformation monitoring data of a dam as an example. The results show that the proposed method can effectively retain the effective information in the monitoring sequence, and is more suitable for dam deformation prediction under complex conditions than the traditional noise reduction method, and can further improve the generalization ability of the prediction model.

This article is based on the detection results of the Jiaocheng fault zone in different projects, and through systematic analysis and organization, uses three methods: geological exploration trough profile method, empirical statistics method, and numerical simulation method, to evaluate the risk of surface rupture and the width of the coseismic surface rupture zone in the northern section of the fault zone during the occurrence of the maximum potential magnitude M_S7.2 earthquake. We analyze the reliability of the results obtained by the three methods was analyzed, and a weighted comprehensive average method was proposed to calculate the width of the coseismic surface rupture zone of the fault. On the basis of considering the accuracy of detection methods, the range of avoidance zones for the Jiaocheng fault zone during project site selection is determined to be on both sides of the fault zone, with an extension of 15.0 m on the west side of the F¹₁ trace to 86.9 m on the east side of the F²₁ trace. This result has certain reference significance for land planning and utilization along the Jiaocheng fault zone. The method can provide reference for similar research.

This article introduces the method of factor analysis. It conducts comprehensive analyses on 16 seismic activity parameters of the small and medium-sized earthquake sequences in the Pingyuan earthquake area. The results show that the current comprehensive factor W was in the highest value range of this quiet episode. Further research of the single factors reveals that the underground medium in the Pingyuan earthquake area might have undergone some changes. In the future, seismic activity in north China may increase.