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.

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).

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.

Based on the analysis of the daily occultation data provided by FY-3E and its global distribution characteristics, we evaluate the accuracy of foF2, hmF2, and TEC derived from FY-3E using the critical frequency(foF2) and peak height(hmF2) obtained by the ionosonde and the total electron content(TEC) provided by the global ionospheric map(GIM) and the international reference ionosphere model(IRI2016). The results show that the correlation coefficients of foF2 and hmF2 between ionosonde and FY-3E are 0.86 and 0.75, and the root mean square errors are 0.99 MHz and 19.26 km, respectively. Taking the GIM TEC data as a reference, the accuracy of TEC retrieved from FY-3E GPS and BDS data is approximately equivalent. Moreover, the accuracy of FY-3E TEC is negatively correlated with solar activity. The correlation coefficient between FY-3E TEC and GIM TEC is 0.90, and the root mean square error is 5.23 TECu. The correlation coefficient between FY-3E TEC and IRI2016 TEC in the same height range is 0.88 and the root mean square error is 3.70 TECu.

Because of the complex climate change and variable water vapor conditions in China, the existing precipitable water vapor(PWV) vertical correction models mainly use simple empirical correction models or regional models, which fail to consider the fine time-varying characteristics of water vapor. Therefore, using Gaussian function with the ERA5 reanalysis data from 2016 to 2018, we construct a time-varying PWV vertical profile model(C-GPWV model) for the Chinese region considering the annual cycle, semiannual cycle, daily cycle, and semidiurnal cycle. We validate the accuracy of the model by using the ERA5 PWV profile of 2019 with the surface PWV at the radiosonde stations of 2019 and compare it with the GPT3 model for accuracy. The experimental results show that the accuracy of the C-GPWV model, which considers the fine time-varying properties of water vapor, is higher than that of the GPT3 model for both the ERA5 PWV profiles and the radiosonde station PWV.

We select the Ganzi-Yushu and Xianshuihe faults in western Sichuan as the study area. We use the LiCSBAS method to obtain the ascending and descending line-of-sight deformation rate based on the LiCSAR products provided by the COMET organization. By combining GNSS results, we carry out the reference correction and three-dimensional deformation field decomposition of the faults. The results show that the Ganzi-Yushu and Xianshuihe faults exhibit obvious left-lateral slip characteristics, and the maximum horizontal deformation rate exceeding 20 mm/a. The root mean square error(RMSE) between InSAR and GNSS is 1.4 mm/a, and the RMSE of direction is 4.8°. In addition, we found that the maximum shear rate is mainly concentrated in the vicinity along Xianshuihe fault, with the maximum value more than 150×10^-9/a. The result can provide a reference for the study of seismic development, seismic risk assessment and distribution pattern of tectonic landslides in this region.

This paper uses small baseline subset InSAR(SBAS-InSAR) technology to carry out long-term monitoring of landslide body deformation, and studies the optimization of gated recurrent unit(GRU), long short-term memory network(LSTM), support vector machine(SVM), differential autoregressive moving average model(ARIMA) prediction periodic and trend deformation through long-term monitoring data. Based on the results of the optimization analysis, this paper designs a landslide body displacement prediction model based on the time series InSAR inversion results and the GRU-ARIMA combination. Taking a landslide body in Zhaotong, Yunnan province as an example, the paper verifies and analyzes the prediction model. The results show that the combined displacement prediction model can take into account the accuracy of the results of the periodic term and the trend term. The error between the predicted value of landslide displacement and the root mean square of InSAR inversion data is 1.15 mm and R2 is 0.996 mm. The experimental results showed that the combined prediction method had high consistency with the actual observation.

This paper proposes an approach of rapid deformation identifying in geological disaster monitoring with the GNSS time-differenced carrier phase (TDCP) algorithm. We first obtain the between-epoch displacement with the TDCP approach, and then adjust in real time the process noise of Kalman filter. Consequently, the filtered relative positioning results can quickly and accurately reflect the rapid deformation of the monitoring body. The results of simulated experiments show that this method not only reflects the rapid deformation of more than 1 cm between the epoch of the monitoring body in time and accurately, but also maintains the monitoring accuracy of mm level.

We propose an epicenter location method based on selecting weight iteration. This method aims to enhance the accuracy of epicenter location in the presence of misjudgments in P-wave arrival times. The approach first employs the STA/LTA+AIC method to extract seismic P-wave arrival times. Then, it utilizes selection weight iteration to calculate the epicenter location, effectively mitigating potential gross errors in the P-wave arrival time information. We validate the method through experiments involving six seismic events and compare the results with those obtained using the traditional Geiger method. The average deviation and root mean square error(RMSE) of epicenter location are 22.48 km and 19.44 km for the traditional Geiger method, respectively. The average deviation and RMSE of the origin time are 4.1 s and 4 s, respectively. For the proposed method based on selection weight iteration, the average deviation and RMSE of epicenter location are 12.59 km and 11.00 km, respectively. The average deviation and RMSE of the origin time are 2.3 s and 2 s, respectively. The findings demonstrate that the new method improves the accuracy of epicenter location by 43% and the accuracy of origin time by 50%. This improved method effectively reduces the impact of gross errors in P-wave arrival times on the calculation results, thereby enhancing the accuracy of the calculated epicenter location.

We use a real-time intelligent seismic processing (RISP) system to process the 2012-06-30 Xinyuan-Hejing M_S6.6 earthquake sequence in Xinjiang and compare the results with manual cataloging results. We draw the following conclusions: the location results of the two methods are relatively consistent: the difference of the onset time is mainly concentrated within ±2 s, the epicenter deviation range is mainly concentrated within 10 km, the depth difference is mainly concentrated within 25 km, mostly within 20 km, and the magnitude deviation range is between ±M_L0.4, mostly between ±M_L0.2. The arrival time difference of the same earthquake co-phase detected by RISP system is mainly concentrated within ±0.25 s, and the recognition rate of Pg phase is higher than that of Sg phase.

By using the time-space scanning method, based on the unified earthquake catalog of China Earthquake Network Center, we analyze the temporal and spatial characteristics of earth tide modulation ratio before MS≥4 earthquakes in Heilongjiang province since 2009. The results show that among the 7 cases of MS≥4 earthquakes, there are 6 cases of abnormal modulation ratio before the earthquake, the corresponding rate of earthquake occurrence is about 86%. The abnormal areas are distributed within 150 km from the epicenter. The anomalies usually appear around a year and a half before the main earthquake, presenting high value anomalies, most of which disappear or weaken within 5 months before the earthquake. Therefore, the method of earth tide modulation ratio analysis has a certain indicative significance for the earthquake prediction of MS≥4 in Heilongjiang Province.

Based on seismic waveform data since 2009 in Tangshan area, the Brune model was employed to determine the apparent stresses of 105 earthquakes with ML≥3.0. The calibration relationships of source parameters indicates that the source parameters are positively correlated with the magnitude. The apparent stress value is between 0.29×10⁵ to 18.97×10⁵ Pa, and the average value is 3.08×10⁵ Pa. The temporal and spatial variation of apparent stress shows that the apparent stress increased before the moderate-to-strong earthquake and decreased after it, which accurately reflects the state of the stress field in the hypocenter region. The Fengnan M_S4.5 and Tangshan M_S5.1 earthquakes not only occurred at the edge of the high apparent stress area, but also near the boundary high and low normalized apparent stress. We speculate that the transition zone between high and low apparent stress may be one of the places where moderate-to-strong earthquakes occur.

Using the mobile geomagnetic vector data from May 2015 to May 2021, combined with earthquakes(M_S≥3.0), we study the evolution characteristics and seismomagnetic characteristics of the annual variation of total intensity in Zhangjiakou-Bohai seismic belt and its adjacent areas. The results show that: 1) The absolute value of the total intensity change in 73.3% of the earthquake cases ranges from 0 to 4.0 nT, and the epicentral variation is small; 70.0% of the total intensity gradient values at the epicenter of earthquakes are 0 to 5.0 nT/°; and the gradient value of the epicenter is relatively small. 2) From May 2015 to May 2021, the annual variation of the total intensity of the Zhangjiakou-Bohai Sea seismic belt shows an evolution trend of positive anomaly-positive anomaly-positive anomaly-positive anomaly-negative anomaly-positive and negative anomalies. Among them, the variation value from May 2018 to May 2019 is relatively small, and 10 earthquakes(M_S≥3.0) occurred from May 2019 to May 2020. The evolution trend of May 2019 to May 2020 changed from positive anomaly in May 2015 to May 2019 to negative anomaly, and Tangshan M_S5.1 earthquake occurred on July 12, 2020. In May 2020 to May 2021 Zhangjiakou-Bohai seismic belt is characterized by alternating distribution of positive and negative anomalies, and 8 earthquakes(M_S≥3.0) occurred from May 2021 to May 2022. The change of annual variation evolution trend of total intensity in Zhangjiakou-Bohai seismic belt may be related to the change and concentration of local stress under the background of regional stress field and may be closely related to the seismogenic process of this seismic belt. The change of lithospheric magnetic field trend contains important seismomagnetic anomaly information, to which should be paid more attention.

The perturbation potential coefficient is one of the key parameters in using the Runcorn model to determine the global mantle convective stress field. In previous studies, there was a lack of quantitative criteria for selecting the order of this parameter. This paper introduces the relationship equation between the depth of the field source and the order of the disturbance potential coefficient in the point mass source method, and preliminarily limits the cutoff order in the Runcorn model. Based on this idea, the mantle convective stress field with a source depth of 30 km should be calculated using the 2 to 213 orders perturbation potential coefficients. This article uses the EGM2008 Earth Gravity Field Model(2 to 213 orders) to calculate the global mantle convective stress field and compares it with the epicenter distribution of earthquakes with a magnitude of 5.5 or above in the past 8 years. The results indicate that high stress areas that coincide with plate boundaries have a good correspondence with the distribution of earthquake epicenters, indicating that mantle convection may control the occurrence of earthquakes in the region by controlling inter plate convergence or tension. In addition, this article analyzes the mantle convective stress and its tectonic background in three typical regions with high frequency of earthquakes(such as the Eurasian Philippine Plate), and the results are consistent with previous research. Finally, by comparing with previous results(2 to 8 orders), the results obtained in this paper(2 to 213 orders) reveal more details of high mantle convection stress at plate boundaries with larger earthquakes occurring at the southwestern edge of the Pacific Plate. This paper suggests that the occurrence of these earthquakes may be controlled by small-scale mantle convection.

Using wavelet crossover and coherence calculation and unitary linear regression method, we analyze and discuss the pressure effect before and after groundwater overmining interference in Changli well. The results show that there is a negative correlation between the water level and the daily variation of pressure in Changli well, and the correlation coefficient is low. However, regardless of the presence of overproduction interference, the phase of water level is about 2 to 3 h ahead of the pressure phase, but the pressure coefficient changes slightly with the degree of interference. The 12 h and 24 h cycles are significant. In the multi-year cycle analysis, the 1-year scale cycle is obvious, but the phase of water level changes from advance to lag due to overmining.

The M_S6.4 earthquake occurred at Menyuan county, Qinghai province, on 21 January 2016. Both the high-speed railway strong motion stations and national strong motion stations recorded the destructive earthquake data. We analyze the two types of strong motion stations, compare construction method, station observation environment, and acceleration, velocity, displacement waves, cross spectrums and Fourier amplitude spectrums, with the decrease of the high frequency component. The results show that the similarity of the displacement time history is higher than that of the velocity time history, and the similarity of the velocity time history is higher than that of the acceleration time history. In 0.1 to 10 Hz frequency band, which is closely related to earthquake damage, the vertical earthquake records that both high-speed railway strong motion stations and national strong motion stations are less affected by site conditions. In addition, we calculate the early earthquake warning magnitude and seismic instrument intensity by the records of the high-speed railway strong motion stations and the national strong motion stations. The results show that the high-speed railway strong motion stations can meet the requirements of early earthquake warning and intensity rapid reporting and the combination of the two types of stations can effectively improve the accuracy of early earthquake warning and seismic intensity rapid reporting.

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 use seismic events of 1 063 M≥2.5 earthquakes as research data, which were recorded by the Fujian seismic network from 2018 to 2022. Taking three kinds of networks: seismometer, strong seismometer and intensity meter, as the observation basis, we obtain the theoretical estimation of the observation range of three types of sensors by calculating different magnitudes. We evaluate 1063 earthquakes using the seismic location accuracy standard method, divided by region(Fujian, Taiwan Strait, Taiwan) and compare the positioning accuracy results of the seismic network and the triple play network. The results show that the number of earthquakes with class Ⅰ accuracy of the triple play network has increased from 1.51% to 3.01%, class Ⅱ accuracy has increased from 5.83% to 25.31%, class Ⅲ accuracy has increased from 16.27% to 63.78%, class Ⅳ accuracy has decreased from 76.39% to 7.90%. Obviously, the three network integration dramatically improves the location of the Fujian seismic network, and the positioning accuracy in Fujian, Taiwan Strait and Taiwan regions has also been improved to varying degrees. The research results can provide significant reference and evaluation value for seismic location strategies in applying three network integrations in earthquake quick report and earthquake cataloging.