We investigate the deviation between IGRF13 model data and unmanned aerial vehicle(UAV) geomagnetic data in localized regions of China. By analyzing the differences in total geomagnetic field intensity, as well as the northward, eastward, vertical, and horizontal components of geomagnetic field between IGRF13 model data and aeromagnetic data in Weishui in Shaanxi province and Yuyao in Zhejiang province. The results show that the difference in total geomagnetic field intensity between IGRF13 model data and geomagnetic data in localized regions of China ranges from 100 to 200 nT. In the northwestern region of China, the difference is approximately 100 nT, while in the eastern coastal region, it is around 200 nT. After systematic calibration, the mean absolute error between the two datasets is less than 50 nT.

Using potential flow theory, we establish the Bernoulli hump, Kelvin wake, and internal wave models. Through simulation experiments, the impact of hydrodynamic wake produced by submarine on underwater gravity gradient navigation is quantitatively studied. The results show when the speed increases and depth decreases, the wake will generate significant disturbances in gravity gradient measurement. When the submarine travels at a depth of 40 m and a speed of 20 knots, the wake will produce gravity gradient disturbance on the order of 10^-9 s^-2, thereby affecting the accuracy of underwater gravity gradient navigation. In such cases, it is necessary to preprocess the measured signal to eliminate or reduce the impact of wake.

We use data from 9 CORS stations in Hong Kong from 2016 to 2021 to invert OTLD parameters, and evaluate the OTLD parameter inversion results based on the mean OTLD parameter values of EOT20, FES2014b, HAMTIDE11a, and TPXO9.2a ocean tide models. The results show that compared with GPS inversion results, the error of K₁, K₂, and S₂ tide components in multi-GNSS inversion can reduce by 15%-50%. In addition, comparing the changes of OTLD parameters in multi-GNSS inversion before and after mass loading correction, it can be seen that the improvement effect of OTLD parameters in vertical and east-west directions can reach 6.88% and 9.85%, respectively. However, due to the influence of internal interactions of mass loading, there is no significant improvement effect in north-south direction. Meanwhile, the improvement percentage of OTLD parameters in multi-GNSS inversion after mass loading correction is concentrated about 20%, which is positively correlated with OTLD.

We derive five spherical harmonic analysis algorithms in detail and develop a user-friendly and easy-to-operate practical software using the scientific computing language MATLAB. To validate the performance of five algorithms, we conduct a numerical analysis of the computational efficiency and accuracy of fast Fourier transform(FFT), least squares(LS), weighted least squares(WLS), approximate quadrature(AQ), and first Neumann method(FNM) algorithms at harmonic degrees of 180, 360, 720, 1 800 and 2 160, using the global topographic data DTM2006.0 as a case study. The results show that the AQ algorithm performs best in terms of computational efficiency, while the LS algorithm achieves the highest precision. The residuals of all five algorithms approximately follow a normal distribution, indicating that none of five algorithms exhibit systematic biases.

The seasonal microseismic signals at gravity stations are difficult to physically simulate and estimate due to the combined effects of randomness, intermittency, and periodicity. Based on the 1 Hz gravity solid tide data of Wushi continuous gravity observation station from 2008 to 2024, we extract the microseismic signals in DF frequency band using the apparent vertical displacement analysis method. An improved variational mode decomposition(IVMD) coupled with the long short-term memory(LSTM) neural network algorithm is proposed to establish a data model for seasonal gravity microseismic signals. The results show that the IVMD-LSTM model can better reflect the annual variation differences of seasonal signals compared to the LSTM model. Moreover, the correlation coefficient between model data using data-driven methods and the snow depth data from GLDAS and ERA5 models increased to 0.5. This study can provide a new processing method for model establishment using seasonal gravity microseismic signals and their application in earthquake analysis and forecasting.

Applying the structural-stratigraphic analysis method, the shallow seismic profile across the fault in southwestern Haizhou depression is interpreted through borehole constraints. On this basis, five important structural interfaces of Yongji subsection of Haizhou section of northern Zhongtiaoshan fault are traced, the activity patterns of fault and sedimentation rates of strata are quantitatively analyzed, and the neotectonic activity episodes in Yongji subsection of Haizhou section of northern Zhongtiaoshan fault are established.

In 2019, the Beiliu M5.2 and Jingxi M5.2 earthquakes occurred successively in different tectonic regions of Guangxi. To obtain the parameter characteristics and probability prediction results of aftershock sequences based on statistical seismology models, we use the ETAS model to calculate the model parameters of two earthquake sequences, and analyze the probability of occurrence and occurrence rate of aftershocks and evaluate the prediction efficiency. The results show that both Beiliu and Jingxi earthquake sequences exhibit high α-value in early stage, indicating weaker ability to trigger secondary aftershocks. The p-value shows that two earthquake sequences decay rapidly in early stage. The prediction results of probability of occurrence and occurrence rate are consistent with the actual low occurrence rate of aftershocks. The ETAS model performs better in predicting aftershocks of Jingxi earthquake sequence than Beiliu earthquake sequence. The strike-slip Beiliu earthquake sequence has lower α-value than the ultra-shallow thrust-type Jingxi earthquake sequence in early stage, which may be greatly affected by the high geothermal heat flow, but the p-value is relatively high. The μ-value of Jingxi earthquake sequence is significantly greater than that of Beiliu earthquake sequence in early stage, suggesting more significantly affected by fluid triggering.

In order to verify the feasibility of CPS software in the strain wave simulation, and examine whether CPS is useful for seismic source parameter inversion constrained by borehole strain waves. We use CPS and QSSP software to calculate the strain waves of virtual stations, respectively. The body wave and surface wave obtained from both methods exhibit a high degree of correlation. Taking the M_W7.0 earthquake in Kyushu Island, Japan on August 8, 2024 as an example, the strain waves from six stations with epicenter distance greater than 30° are simulated using CPS software. The results indicate that the simulated waves show good agreement with the observed S-waves, and the average correlation among the five stations is significant. Theoretically, S-wave combined with CPS software can be used to invert the seismic source rupture process.

We use the joint tomography of first-arrival and reflected waves based on the adjoint techniques and conventional optimization methods to compare the combination methods of three commonly used optimization methods (CG, CGDESCENT, L-BFGS) and line search methods (LNS and CWI), and test the six combination algorithms using anomaly body model and chessboard model. The results show that: 1) For high-speed mutation models, CGDESCENT+LNS has slightly higher accuracy than L-BFGS+CWI, but CGDESCENT did not converge in low-speed models, while both L-BFGS+LNS and L-BFGS+CWI perform well. CGDESCENT is more suitable for large-scale models. 2) Under the same conditions, the accuracy of CWI is slightly higher than that of LNS, but CWI is prone to local minimum and is very unstable.

A two-dimensional strain tensor model is established to analyze the spatio-temporal deformation characteristics of a landslide. The results indicate that the landslide deformation exhibits spatio-temporal heterogeneity. During the initial monitoring stage, the front edge of landslide was dominated by compressive deformation, while the middle and rear edges showed a combination of expansion and shear deformation, with intensity rapidly decreasing over time. After the reinforcement of retaining wall, a brief period of strong compressive deformation occurred at the front edge of landslide, with a maximum compressive strain rate of -6 μstrain. In April 2021, a localized landslide occurred at the rear edge of landslide, characterized by strong localized tensile and shear deformation, with a maximum shear strain rate of 150 μstrain and a surface expansion strain peak of 30.9 μstrain. After the sliding event, only the surface expansion intensity diminished rapidly. Moreover, strain extremes along the crack between PB03 and PB04 persisted over time, showing a polarity reversal six months before the landslide, revealing potential risks even during the macroscopically stable deformation stage.

A dam deformation prediction model is proposed that integrates secondary mode decomposition with butterfly optimization algorithm(BOA)-optimized light gradient boosting machine(LightGBM). First, the training set data is decomposed using complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN), and the composite entropy of decomposed subsequences is calculated. Second, the K-means clustering algorithm is used to categorize the decomposed subsequences into high and low frequency components, and variational mode decomposition(VMD) is applied to high frequency signals. Finally, the BOA-optimized LightGBM model is employed for prediction. The case studies demonstrate that this method effectively processes deformation data and enhances data stationarity, achieving significant accuracy improvements compared with conventional approaches. The nMAPE, MSE, and MAE indexes improve 16.2%-22.5%, 16.8%-28.1%, and 16.2%-22.5%, respectively.

To address the issue of reduced accuracy in antenna rotation inversion due to the noise in ground-based phase wind-up(GPWU), we propose a real-time yaw angle inversion model that combines low-pass filtering, an improved reset Kalman filtering, and elevation angle weighting. After epoch differencing and low-pass denoising of the geometry-free measurements, the model identifies the time points of yaw angle change based on the variations of epoch-differenced geometry-free observations. We then use the reset Kalman filtering to predict yaw rate in real-time. Finally, the model optimizes the station antenna rotation rate calculated from all satellites, weighted by elevation angles, to obtain the final antenna rotation rate. The results show that the proposed method achieves a yaw rate prediction accuracy of 0.587° before Kalman filtering convergence, which improves to 0.245° after convergence, with a time error of less than 1 second.

High-precision polar motion prediction is crucial for applications such as real-time satellite orbit determination and deep space probes navigation. We combine the singular spectrum analysis(SSA) and weighted least squares(WLS) models, which takes into account the temporal variability of periodic components of polar motion. Combining with the auto-regressive(AR) model, we prove the effectiveness of SSA+WLS+AR model in improving the accuracy of polar motion prediction. We analyze the impact of detrending, the length of basic sequence, and the weighting method on the final prediction accuracy of the SSA+WLS+AR model. The results show that in the X direction, the model combination of "no detrending+50-year basic sequence+inverse weighting" has the highest prediction accuracy, while in the Y direction, the model combination of "detrending +50-year basic sequence+inverse weighting" has the highest prediction accuracy. When the prediction span is from 150 to 360 days, the prediction accuracy of SSA+WLS+AR model is better than that of bulletin A provided by international Earth rotation service(IERS), with maximum improvements of 33% and 26% in the X and Y directions, respectively. The prediction results show that the first-day prediction accuracy of polar motion is significantly better using the C04 20 sequence compared to the C04 14 sequence.

To address the low accuracy of zenith tropospheric delay(ZTD) models and the scarcity of water vapor data in Antarctica, we use ZTD data from six global navigation satellite system(GNSS) stations in Antarctica, ECMWF reanalysis v5(ERA5) data, and radiosonde(RS) data from 2018 to 2021. First, the ZTD accuracy of UNB3m, EGNOS, and GPT3 models in Antarctica is analyzed. Then, the ZTD correction and PWV conversion models are constructed separately based on extreme gradient boosting(XGBoost). The results show that the average root mean square errors(RMSEs) of UNB3m-ZTD, EGNOS-ZTD, and GPT3-ZTD of six GNSS stations in Antarctica are 98.12 mm, 116.13 mm, and 24.31 mm, respectively. After correction using XGBoost, the average RMSEs of UNB3m-ZTD, EGNOS-ZTD, and GPT3-ZTD from 2020 to 2021 are 10.46 mm, 10.50 mm, and 10.60 mm, respectively, demonstrating higher accuracy and closeness between the models. The average RMSEs of PWV converted from the corrected UNB3m-ZTD, EGNOS-ZTD, and GPT3-ZTD using XGBoost are 1.71 mm, indicating high accuracy.

The convolution regression calculation of pressure effect on well water level is carried out using MATLAB, and the pressure step response function of well water level is obtained. By calculating the determination coefficient fitted by pressure step response function and comparing the AIC value, the time interval and maximum time delay parameters in calculation process can be determined. Combining with other existing research results, preliminary judgments and even quantitative calculations of aquifer parameters of observation well can be made.

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.