This study utilizes the environmental loading products derived from different data sources provided by GFZ and GGFC, to correct the atmospheric loading, non-tidal ocean loading and hydrological loading of 50 GNSS stations in Beijing and its surrounding areas. The RMS reduction rate of the GNSS coordinate time series after environmental loading corrections is used to evaluate the performance of different products. The results show that the impact of atmospheric loading is more significant than that of hydrological loading, and the impact of non-tidal ocean loading can be ignored. The RMS values of all the GNSS height time series except that of LNJZ station are reduced after the correction of atmospheric loading products provided by GFZ and GGFC, and the average RMS reduction rate is more than 15%, indicating that the impact of atmospheric loading on the vertical motion of GNSS stations in the study area is nonnegligible. The correction effect of different atmospheric loading products provided by GFZ and GGFC are similar, the correction effect of non-tidal ocean loading products derived from MPIOM provided by GFZ is better, and the correction effect of hydrological loading products derived from GLDAS provided by GGFC is better.

The wavelet method is used to calculate the coherence spectrum of the ground resistivity and the theoretical value of surface strain Earth tide at Changli station during 1981 to 2020. The results show that the 12 h and 24 h period spectra of the calculated results are significantly correlated, with a frequency of about 1 to 4 times/month, each lasting about 2 to 3 d, but the time is irregular. The positive and negative correlations of each periodic component are chaotic and unstable in time domain. The results of daily value calculation mainly have two types of cycle components, one is the short cycle of 8 d, 10 d, 15 d and 30 d within 8 to 30 d, and the other is the longer half-year and annual cycle. The continuity of short-period components is poor, the frequency is about 3 to 5 times/year, and the positive and negative correlations are chaotic in the time domain. The correlation between the half-year and annual scales is relatively significant, the duration is more continuous and the change is relatively stable, and the annual cycle component appears longer than the half-year cycle. The correlation of annual cycle is the most stable and negative correlation, and the correlation of half-year cycle is slightly worse, with both positive and negative correlation. The daily wavelet coherence spectrum also shows the existence of long and short period gaps, which are related to the seismic conditions, mainly reflecting the stress field changes in the area and the modulation effect of far-field large earthquakes.

The surface deformation results in and around Longyao ground fissure, Hebei province from 2017-03 to 2022-10 were obtained based on StaMPS and SBAS-InSAR. The results show that the monitoring results of the two techniques are consistent, indicating that the results are reliable. The land subsidence around Julu county is serious, and a large scale subsidence area has been formed, with the maximum deformation rate exceeding-30 mm/a. The temporal results of extraction showed periodic changes, and were consistent with groundwater level and precipitation. The maximum deformation rate difference and the maximum shape variable difference between the two sides of the Longyao ground fissure are over 40 mm/a and over 200 mm respectively. The field investigation shows that the surface differential deformation caused by the ground fissure has a great impact on the local area.

Using precise leveling measurement data and a modified dip-slip displacement model, we systematic analyze the vertical movement characteristics of each section of the northern margin fault of western Qinling mountains. The results show that the northern margin fault of western Qinling mountains is undergoing a composite deformation mechanism of left-lateral strike-slip and thrust compression, and the activity varies among different sections. The vertical dip-slip rate increase from 0.24 mm/a in the western section to a maximum of 2.31 mm/a in the eastern section, and then decrease to 1.51 mm/a. The inverted locking depths are consistent with the relocation depths of small earthquakes, which indicates that there are different locking states in each section. Combining seismic activity, the Tianshui-Baoji section has the highest earthquake risk.

At 10 measurement points along the same survey line, observations were conducted using various array types such as double circular array, cross shaped array, and linear array. Then, we extracted dispersion curves, plotted apparent S-wave velocity profiles. The study obtained the following two results. Firstly, the apparent S-wave velocity profiles obtained from cross shaped array are basically consistent with those obtained from double circular array, and the delineated interfaces of geological layers are also generally consistent, but there are differences in the positions of velocity anomaly bodies delineated by the two types of arrays. Secondly, there are significant differences between the apparent S-wave velocity profiles of the linear array and the double circular array, and there are significant differences in the apparent S-wave velocity profiles obtained from the linear array with different orientations, as well as in the division of the soil rock interface. When conducting microtremor exploration, double circular array should be prioritized, cross shaped array should be used depending on the situation, and linear array should be used with caution.

Using a combined method of audio-frequency magnetotellurics(AMT) and high-density resistivity, resistivity observations were conducted in the deep well of Xinchengzi, Liaoning, to investigate the distribution of underground electrical structures. Through detailed exploration, critical parameters such as the spatial location, range, and morphology of the aquifer zone were delineated, providing valuable references for the selection and design of multi-pole distance multi-layer earth resistivity sites.

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 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 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.

We combine the latest horizontal and vertical GPS velocity fields in the southeastern margin of Qinghai-Xizang plateau, based on the strain solving method of spherical wavelet multi-scale analysis, consider the weight relationship between regional seismic moment and strain distribution, obtain the three-dimensional crustal strain characteristics of the southeastern margin of Qinghai-Xizang plateau, and analyze the relationship between regional deformation characteristics and seismicity. The results show that: 1) There is a consistency between surface strain and vertical strain in the study area. The high surface strain and low vertical strain are mainly concentrated in the central Qinghai-Xizang plateau and northern Jinshajiang regions, while the low surface strain and high vertical strain are mainly concentrated in the Eastern Himalayan Syntaxis and Longmenshan regions. The spatial inverse distribution relationship between surface strain and vertical strain shows that the horizontal velocity and vertical velocity obtained by GPS observation in this region are coupled, which indicates that vertical motion is also an indispensable consideration for the study of crustal deformation and seismic risk in this region. There is a good agreement between surface strain and vertical strain in terms of magnitude, which further indicates that the crustal deformation in this region can be approximated as a continuous and homogeneous elastic behavior. 2) There is a consistency between the type of strain rate and focal mechanism solution. The strain types obtained from GPS data in the southeastern margin of Qinghai-Xizang plateau can be well matched with the focal mechanism solution in this region. The source mechanism of normal fault is mainly distributed in Jinshajiang region, thrust fault is mainly concentrated in Longmenshan and Eastern Himalayan Syntaxis regions, and strike-slip fault is mainly distributed near the fault zone with significant strike-slip shear such as Xianshuihe-Xiaojiang fault, indicating that the strain characteristics obtained from GPS observation can better describe the tectonic stress field characteristics in this region. Combined with the spatial distribution characteristics of stress and strain in this region and seismic tomography results, we believe that the Longmenshan fault and Eastern Himalayan Syntaxis are in compressive strain state.