The water level changes in the estuarine area are influenced by various factors with different mechanisms and periodicities, including runoff, astronomical tides and storm surges, resulting in relatively low forecasting accuracy of the residual water level. To improve the forecast accuracy of residual water levels, an estuary-tidal residual water level forecasting method based on VMD-BPNN (variational mode decomposition and back propagation neural network) is proposed. By conducting tidal harmonic analysis on the long-term water level data of estuarine areas, astronomic water levels and residual water levels can be obtained. The residual water level is subjected to VMD, obtaining multiple intrinsic mode functions of the residual water level in the time series. Then, the BPNN is used to train each intrinsic mode function, and an accurate forecast of residual water levels in the estuary area is achieved through the forecast and superposition of each intrinsic mode function. Water level data from four typical tidal stations in estuarine areas of the United States and France were used for experimental analysis. The method was verified by using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency (NSE) as evaluation indicators, and the results showed that it had a good comprehensive performance, and high stability and accuracy in the forecasting of the residual water level. This study thereby provides a valuable foundation and insightful reference for future research into the complex mechanisms driving water level changes and the development of high-precision tidal forecasting systems in estuarine environments.

SBAS-InSAR technology has been widely researched and applied in the large-scale monitoring of mining areas, but due to the influence of spatial and temporal discorrelation, atmospheric delay and noise on the deformation results, as well as the requirements on image quantity, quality and spatial continuity, the SBAS-InSAR technology inferior to the levelling monitoring results in terms of measurement accuracy and reliability. Taking the Yuncheng coal mine as the research area, and after using SBAS InSAR to monitor the subsidence, the levelling monitoring data was used as the constraint to construct the subsidence error surface by polynomial surface fitting, and then the SBAS-InSAR subsidence monitoring results were be corrected. The experimental results show that the settlement difference is better than ±50mm/a, which effectively improves the overall accuracy of SBAS-InSAR settlement monitoring. The experimental results verify the feasibility and effectiveness of using the level monitoring results to correct the deformation monitoring results of SBAS InSAR, and provide effective reference information for improving the accuracy of the deformation monitoring results by multi-source fusion processing.

MSBAS InSAR (Multidimensional Small Baseline Subset Interferometric Synthetic Aperture Radar) is a multidimensional deformation time series inversion method that integrates multi-orbit SAR data. Due to plenty of observations and the quite strong multicollinearity between the column vectors of the coefficient matrix, the MSBAS InSAR deformation observation model has serious ill-posed problems. Tikhonov regularization method is a common method for solving the ill-posed problem of geodetic measurement. Although the regularization matrix constructed by the existing methods can achieve selective correction of singular value, the non-negative definite property of the matrix is often neglected. Therefore, an exponential function singular value correction regularization method is proposed to ensure the non-negative definite characteristic of the regularization matrix. In addition, an exponential function regulatory factor is introduced to ensure that the value of the regularization matrix is within a reasonable range. The rationality of the exponential function regulatory factor is analyzed theoretically, and a method of determining the regulatory factor in the sense of minimum mean square error is proposed. The exponential function singular value correction regularization method is applied to the MSBAS InSAR deformation inversion in a mining area, and the effectiveness of the method is verified by the leveling results in the study area. The results show that the proposed method can not only effectively improve the ill-posed problems of the MSBAS InSAR deformation inversion, but also reduce the visual ambiguity caused by the single line of sight, and improve the accuracy of the results by 28% compared with the results of widely used truncated singular value decomposition method.