The variational mode decomposition(VMD) exhibits superior performance, and the setting of K and alpha parameters significantly influences its decomposition result. We optimize the selection of these two parameters based on sparse index, enabling the adaptive determination of the optimal K and alpha values. On this basic, VMD method is applied to seismic signal data observed by four types of second-sampling instruments at Yichang station, including BBVS-60 seismometers, DZW gravimeters, VP-type tiltmeters, and water level gauges, and time-frequency analysis is conducted combining with Hilbert transform. The results show that the optimal K and alpha values differ for each of four instruments. The K value represents the type of coseismic signal frequencies that can be decomposed, suggesting that the four instruments respond differently to the same seismic signal.

By collecting achievements in the medium- to long-term prediction of strong earthquakes, an in-depth analysis of the commonalities and differences between vertical deformations observed by the Global Navigation Satellite System(GNSS) and leveling measurements was conducted. The related issues of abnormal uplifts before major earthquakes were explored, including the abnormal fluctuations of leveling and their differences from GNSS observation results. Through the analysis of years of precise leveling data, the abnormal phenomenon of ground uplift before major earthquakes was revealed. Combining this with research findings on gravity anomalies before major earthquakes, a theoretical deduction and analysis of the relationship between the two was conducted. The vertical deformation data obtained from leveling measurements not only includes the effects of point-wise vertical geometric deformation but also contains information about the dynamic bending of the local gravity level surface, with magnitudes reaching 2 mm or more. Based on this, the concepts of physical deformation and geometric deformation were proposed. The research in this paper is of significant theoretical and practical importance for correctly understanding the differences in deformation mechanisms, the medium- to long-term prediction of strong earthquakes, and the formulation of deformation anomaly monitoring plans and decision-making.

By dividing the seismic cycles of M_S≥7.8 earthquakes in China's mainland and adjacent regions and studying the migration of main active zones, combined with the acceleration-deceleration changes of Earth's rotation speed, this study explores the spatiotemporal patterns and driving mechanisms of M8.0 seismic activity in the study area. The main conclusions are as follows: 1) Since 1879, the study area has experienced 6 active periods of M8.0 earthquakes. The second active period mainly occurred in the Pamir-Baikal seismic belt, the third in the northeastern margin of the Qinghai-Xizang plateau, the fourth in the southeastern margin, the fifth in Yunnan and north China, and the sixth around the Bayan Har block within the Qinghai-Xizang plateau. The region may currently be in the initial stage of a new active period. 2) Since 1867, Earth's length of day(LOD) variation curve shows 4 acceleration-deceleration cycles and 16 acceleration-deceleration phases. The superposition of S tectonic stress fields with deceleration changes favors M8.0 earthquakes in the Pamir-Baikal belt, while NE-oriented stress fields combined with acceleration changes facilitate M8.0 earthquakes along the Qinghai-Xizang plateau margins and interior. The transition from acceleration to deceleration phases causes the tectonic stress field around the Qinghai-Xizang plateau to shift from NE-dominated to locally restored NS orientation, serving as the main driver for the third, fourth, and sixth active periods. 3) The tectonic stress field in China's mainland and adjacent regions has experienced clockwise rotation from NS to NE and then to NEE orientations over the past century, potentially related to Earth's rotation changes involving significant deceleration before 1913 followed by overall acceleration. This process remains ongoing, with no current signs of concentrated M8.0 earthquakes similar to the second active period. However, vigilance is needed regarding possible continued NEE rotation of the Qinghai-Xizang plateau's stress field, and future emergence of new main active zones cannot be ruled out.

Based on continuous GNSS observation data, a relatively comprehensive three-dimensional GNSS velocity field for the northeastern margin of the Qinghai-Xizang plateau was established by fully considering random noise and regional environmental loading. This study characterizes crustal strain and vertical motion patterns in the research area. Results show that the region is dominated by compressive strain, with significant differences in principal strain rates between its southwestern and northeastern sectors. Notable distributions of maximum shear strain and principal strain are observed along the east Kunlun fault and Xianshuihe fault. Vertical uplift predominates across the northeastern Qinghai-Xizang plateau, suggesting that vertical tectonic forces may originate from intra-block compressional shortening or obstruction of deep lithospheric material flow. However, significant surface subsidence near the Lanzhou area is likely attributed to collapsible loess and soil erosion. Finally, estimates based on an incompressible Earth model indicate minimal vertical deformation caused by horizontal compression, implying that large-scale uplift in the northeastern Qinghai-Xizang plateau may primarily result from vertical tectonic factors such as lower crustal flow and lithospheric convective thinning, rather than horizontal crustal shortening.

Taking the hilly open-pit mining area in central Shandong as the study region, this research analyzes the impacts of different DEMs(SRTM-1, SRTM-3, ALOS-DEM, ASTER-GDEM, and LiDAR-DEM) and their elevation accuracy/spatial resolution on SBAS-InSAR deformation monitoring performance and geometric distortion identification. Results demonstrate that compared to spatial resolution, DEM elevation accuracy plays a more critical role in deformation monitoring and significantly affects measurement precision. Both DEM elevation accuracy and spatial resolution substantially influence the identification of shadow and layover areas. These findings provide guidance for DEM data selection in subsequent time-series deformation monitoring in complex terrains.

Based on observation data from 77 GNSS stations worldwide, this study systematically evaluates the global applicability of four tropospheric delay mapping function models(GMF, NMF, VMF1, VMF3) and their impact on the accuracy of precise point positioning(PPP). The zenith tropospheric delay(ZTD) for each station was obtained using the PRIDE PPP-AR software. With the ZTD products released by the Center for Orbit Determination in Europe(CODE) as a reference, a comparative analysis of model performance was conducted across geographical zones(low/medium/high latitudes, land/ocean, and altitude gradients). The results indicate that the existing tropospheric delay mapping function models exhibit poorer accuracy in low-latitude, low-altitude, and oceanic regions. The VMF series mapping function models perform better in addressing differences arising from changes in altitude and latitude, and both the GMF and VMF series models show significant correction effects for oceanic stations. In terms of positioning accuracy in the U direction, the GMF, VMF3, and NMF mapping function models perform best in low-latitude, medium-latitude, and high-latitude regions, respectively. Overall, the GMF and VMF series mapping function models demonstrate superior performance in global PPP data processing.

A wideband signal extraction method based on normal time-frequency transform(NTFT-WSE) is proposed based on standard time-frequency theory. First, the standard time-frequency transform is used to extract and model the periodic signals of sea level. Then, the NTFT-WSE method is employed to extract, model, and predict the components at different time scales, achieving accurate prediction of the periodic low-frequency signals of sea level changes. Compared with the standard Morlet wavelet transform, the NTFT-WSE method reduces the root mean square error(RMSE) and mean absolute error(MAE) in periodic signal extraction by 26.03% and 22.24%, respectively, significantly improving the extraction accuracy of mid- and low-frequency signals. Finally, based on satellite altimetry sea level anomaly data from June 1993 to May 2008, the NTFT-WSE method was used to predict the sea level changes in the South China Sea from June 2008 to May 2023. The prediction results are consistent with the overall trend of actual observations.

This paper utilizes the ERA5 multi-element dataset to predict ERA5 water vapor by integrating the fast Fourier transform(FFT) and convolutional long short-term memory(ConvLSTM) methods. Firstly, the common periods of various meteorological elements are extracted using FFT. Subsequently, multiple meteorological elements with different common periods and individual water vapor data are selected as model inputs, and the ConvLSTM method is employed to train the ERA5 water vapor and construct the prediction model. Finally, the prediction performance of the model for point and spatial water vapor is evaluated using GNSS water vapor and actual ERA5 water vapor. The results indicate that the ERA5 water vapor prediction method based on FFT and ConvLSTM can effectively predict water vapor for the next 120 hours, with high accuracy in both single-point and spatial areas, providing a data foundation for short-term precipitation warnings.

Based on nearly one year of GNSS-R data from the GNSS occultation sounder Ⅱ(GNOS-Ⅱ) onboard the Fengyun-3E(FY-3E) satellite, this study conducted global soil moisture inversion modeling and evaluated the inversion results from different satellite systems and their combined observations. The results indicate that there are systematic biases in the reflectivity distributions calibrated by the reflected signals of BDS, GPS, and Galileo. However, the inversion results based on different systems have similar accuracy. The predicted soil moisture values show good consistency with the level 3 soil moisture products provided by the SMAP satellite, with a correlation coefficient of 0.855 and an unbiased root mean square error of 0.063 cm³/cm³. When combining different systems for inversion, the cumulative distribution function matching method was used to recalibrate the effective reflectivity of each satellite system's observations, which further improved the accuracy of surface soil moisture inversion.

To enhance the filtering performance of the maximum correntropy Kalman filter(MCKF) method in complex environments, an adaptive maximum correntropy Kalman filter algorithm(AMCKF) for GNSS/SINS integrated navigation systems is proposed. Firstly, based on the relationship between the filtering fault detection function values at the current and the past (l-1) time steps and the detection threshold, two mapping relationships of 0 and 1 are formed. Then, a window of length l is constructed using the mapped values, and a kernel width adaptive adjustment algorithm is proposed. Finally, this kernel width adaptive adjustment algorithm is applied to MCKF to form the AMCKF algorithm for the integrated navigation system. Experiments are conducted on a GNSS/SINS integrated navigation system under measurement environments with Gaussian white noise and heavy-tailed impulsive noise. The results indicate that AMCKF can adaptively adjust the kernel width value according to different measurement noise environments, thereby improving the filtering accuracy of the integrated navigation system. Compared with KF and MCKF, AMCKF can improve position accuracy by approximately 26.5% and 16.4%, respectively, and improve velocity accuracy by approximately 15.5% and 6.4%, respectively.