The mining subsidence in mining area could cause large-gradient deformation in a short period of time. When the deformation gradient exceeds the threshold value of the Differential Interferometry Synthetic Aperture Radar (D-InSAR) technology monitoring gradient, D-InSAR technology is likely to cause the failure of InSAR phase unwrapping algorithm. At this time, the InSAR technology is unable to monitor the 3D surface deformation. Aiming at these problems, an dynamic probability integral method (DPIM)-based InSAR phase unwrapping model and a method of extracting 3D surface deformation were proposed. The phase unwrapping model firstly used the empirical parameters of the probability integral of the mining face to predict the line of sight (LOS) direction deformation phase of the mining subsidence surface. Secondly, the phase of differential interferogram was unwrapped with the assist of the predicted LOS deformation phase under the constraint of DPIM, and the true LOS deformation phase was obtained, then the true LOS deformation phase transformed into LOS deformation. Finally, according to the geometric projection relationship between the LOS deformation and 3D deformation of mining subsidence surface, the probability integral prior model was brought into the equation of the geometric projection relationship. On the basis of relevant boundary conditions, the 3D surface deformation was extracted from the LOS direction deformation field of mining subsidence. The feasibility of the method was verified by the simulation experiment results. The differential interferogram of the subsidence basin was obtained by the differential interference processing of image data of Sentinel-1A on Nov. 16, 2017 and Dec. 10, 2017 of 1613 working face of Guqiao South Mine. By using the DPIM-based phase unwrapping model, the phase of differential interferogram was unwrapped and the 3D surface deformation during this period as well as the deformation extraction method were developed. The results showed that the maximum fitting error value of subsidence was 79 mm, about 8.33% of the maximum value of subsidence, and the fitting error of mean square of subsidence was ±33.5 mm. The results showed that the DPIM-based phase unwrapping model and the method of extracting 3D surface deformation proposed in this paper have certain engineering application values.

Surface deformation of mining area caused by mining subsidence can be divided into three stages: the initial stage, the active stage and the decline stage. At the active stage, D-InSAR technology could be easily affected by the incoherence of large-gradient deformation, thus leading to the failure of conventional D-InSAR technology in monitoring mining subsidence.In order to solve the problem, this paper developed a 3D surface deformation prediction method of mining subsidence by integrating dynamic Boltzmann prediction model with D-InSAR. It was firstly to monitor the surface movement and deformation by D-InSAR technology during the decline stage and obtain the surface LOS deformation field data. Then, on the basis of Boltzmann model, the geometric equation between LOS deformation by D-InSAR monitored and 3D deformation was established. Next, the geometric equation was solved based on the GA theory, and the parameters of Boltzmann prior model were obtained. On the basis, the 3D deformation of mining area were obtained, proving that D-InSAR technology was effective to get 3D deformation of mining subsidence. Through simulation experiments, the effectiveness of the proposed method is verified. The surface deformation of 1613 working face in Guqiaonan coal mine was monitored by D-InSAR during the decline stage, and the LOS deformation values were obtained. The 3D deformation was obtained from the initial stage to the decline stage by using the method of DB-InSAR. The monitoring results showed that the fitting errors in the LOS deformation were mostly within 3 mm, the RMSES was ±2.37 mm, shown that the fitting accuracy was high. The RMSE of subsidence were ±120 mm. The results showed that the method of DB-InSAR has engineering application values.

When 3D laser scanning technology is used to monitor the surface deformation of mining subsidence in mining area, the surface of the working face is covered with a large number of vegetation, and the surface water accumulates above the working face, which makes the point cloud data obtained by the 3D laser scanning difficult to denoise, or even missing. At this time, the conventional 3D laser scanning technology can not obtain the surface deformation field of mining subsidence. Aiming at the above problems, the 3D laser scanning monitoring method for mining subsidence based on the auxiliary for PIM proposed (3DLS-PIM). Firstly, this paper introduces the PIM prediction model. Secondly, the mining subsidence observation equation based on 3DLS-PIM is constructed, and then the prediction parameters of PIM are solved based on quantum particle swarm optimization (QPSO). Finally, according to the PIM and its parameters, the mining subsidence surface deformation basin is predicted and obtain the surface deformation field of mining subsidence. Robust experiments show that QPSO has a certain ability to resist random errors and gross errors. The results of engineering application show that the mining area 3D deformation monitoring method proposed in this paper has certain engineering application value.

The linear combination of multi-frequency carrier-phase and pseudorange observations can form the combined observations with special properties. The type and number of combined frequencies will directly affect the characteristics of the combined observations. BDS-2 and BDS-3 broadcast three and five signals, respectively, and the study of their linear combination is of great significance for precision positioning. In this contribution, the linear combination form of multi-frequency carrier-phase observations in cycles and meters is sorted out. Seven frequency combination modes are formed, and some special combinations for positioning are searched. Then, based on the principle of minimum combined noise, a simpler search method for the optimal real coefficients of ionosphere-free (IF) combination based on the least squares (LS) principle is proposed. The general analytical expressions of optimal real coefficients for multi-frequency geometry-based and ionosphere-free (GBIF), geometry-free and ionosphere-free (GFIF), and pseudorange multipath (PMP) combinations with the first-order ionosphere delay taken into account are derived. And the expression derivation process is given when both the first-order and second-order ionospheric delays are eliminated. Based on this, the characteristics of the optimal real coefficient combination in various modes are compared and discussed. The various combinations reflect that the accuracy of the combined observations from dual-frequency (DF) to five-frequency (FF) is gradually improving. The combination coefficient becomes significantly larger after taking the second-order ionospheric delay into account. In addition, the combined accuracy of BDS-3 is better than that of BDS-2. When only the first-order ionosphere is considered, the combination attribute of (B1C, B1I, B2a) is the best among the triple-frequency (TF) combinations of BDS-3. When both the first-order and second-order ionospheric delays are considered, the (B1C, B3I, B2a) combination is recommended.

The technical theories of monitoring and preventing mining subsidence have long been key challenges and research priorities in the mining field. The rapid advancements in remote sensing technology and deep learning algorithms have enabled significant breakthroughs in monitoring and accurately identifying mining subsidence. In this paper, a novel automatic detection method for mining subsidence is proposed using Interferometric Synthetic Aperture Radar (InSAR) wrapped interferograms. First, this study designs a VGG-UNet model enhanced by an attention mechanism module to learn and detect mining subsidence areas. This enhancement improves the feature representation and perception capabilities of the model. Second, to address the scarcity of real InSAR data in the training set, an efficient dataset simulation strategy is established. This strategy incorporates the realistic scenarios of monitoring environment to improve the effect of model training. Finally, a complete workflow for model training and detection application is developed. The results demonstrate that the detection model achieves a precision of 92.55%, a recall of 90.43%, an accuracy of 93.37%, an F1-score of 91.46%, and an IoU of 85.05% on the validation set. The model was applied to mining subsidence detection in the Huaibei-Yongcheng mining area, China, from June 2017 to July 2024. A total of 103 mining subsidence sites were identified, and their long time series characteristics and the spatial distribution pattern of subsidence accumulation duration were analyzed. The findings offer critical technical support for sustainable mining management and land resource protection at the regional scale.

3D surface deformation monitoring methods based on the current single-track Interferometric Synthetic Aperture Radar (InSAR) technology are constructed by integrating the deformation laws of mining horizontal or gently inclined coal seams in plain areas, which are not suitable for monitoring the 3D deformation of mining in mountainous areas. Therefore, we developed a new method of extracting 3D deformation of mining in mountainous areas by using single-track InSAR technology. Firstly, the Line of Sight (LOS) deformation equations were established based on geometric relations between the InSAR monitored LOS deformation and 3D surface deformation. Secondly, they were fused with basic principles of surface deformation and movement of mining in mountainous areas. Then they were solved based on relevant boundary conditions. Simulation results of this novel method showed that the accuracy values of extracted deformation along vertical section, East-West (EW) and North-South (NS) were better than 8.86 mm, 8.29 mm and 18.01 mm, respectively. Compared with Wang method, this method is suitable for surface deformation monitoring of mining subsidence in mountainous areas. Finally, the proposed method was successfully used to monitor the 3D deformation of mining in the mountainous area of Tangjiahui Coal Mine in Ordos, Inner Mongolia, China.