The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data.

The Heze section of Rizhao–Lankao high-speed railway (RLHR-HZ) has been under construction since 2018 and will be in operation by the end of 2021. However, there is a concern that land subsidence in the Heze region may affect the regular operation of RLHR-HZ. In this study, we investigate the contemporary ground deformation in the region between 2015 and 2019 by using more than 350 C-band interferograms constructed from two tracks of Sentinel-1 data over the region. The small baseline subset (SBAS) technique is adopted to compile the time-series displacement. We find that the RLHR-HZ runs through two main subsidence areas: one is located east of the Heze region with rates ranging from −4 to −1 cm yr⁻¹, and another one is located in the coalfield with rates ranging from −8 to −2 cm yr⁻¹. A total length of 35 km of RLHR-HZ is affected by the two subsidence basins. Considering the previous investigation and the monthly precipitation, we infer that the subsidence bowl east of the Heze region is due to massive extraction of deep groundwater. Close inspections of the relative locations between the second subsidence area and the underground mining reveals that the subsidence there is probably caused by the groundwater outflow and fault instability due to mining, rather than being directly caused by mining. The InSAR-derived ground subsidence implies that it is necessary to continue monitoring the ground deformation along RLHR-HZ.

Most of the coal mines in Southwest China are located in mountainous areas with high vegetation coverage, and most activities are carried out under the mountains. The deformation monitoring and mechanical behavior analysis of the mining area helps reveal the typical mountain deformation and failure mechanism caused by underground mining activities and reduce the risk of mountain collapse in the mining area. In this manuscript, a research method for mountain stability in mining areas is proposed, which combines InSAR deformation monitoring with numerical analysis. Based on the high-precision deformation information obtained by DS-InSAR and the landslide range, a three-dimensional explicit finite difference numerical analysis method was used to reconstruct the landslide model. According to the layout of the coal mining working face, the variation mechanism of overlying stratum stress and the mountain slip in the coal mining process is inverted, and the mechanism of mountain failure and instability in the mining area is analysed. Based on the sentinel data, the experiment performed time series monitoring and inversion analysis of the mountain collapse in Nayong, Guizhou, China. The results show that mining activities a certain distance from the mountain will affect mountain stability, and there are specific mechanisms. From 2015 to 2017, the stress redistribution of overlying strata above the goaf area resulted in dense longitudinal cracks in the landslide body due to coal mining. The mountain is in a continuous damage state, and the supporting force to prevent collapse continues to decrease, resulting in a gradual decrease in landslide stability. Both the time series DS-InSAR monitoring results and numerical simulation results verify the actual occurrence and development of the on-site subsidence.

This paper introduces a framework for robustly estimating the parameters of the probability integral method (PIM). According to the framework, the initial robust estimates of the PIM parameters are firstly obtained by combining the cultural algorithm and rand particle swarm optimisation (CA-rPSO) with the LTS method. As a byproduct, an initial standard deviation can be calculated and used to determine the initial weights of the measurements according to the Institute of Geodesy and Geophysics (IGGIII) down-weighting scheme. Meanwhile, a modified CA-rPSO (referred to as CA-rPSO-IGGIII) is constructed, where the IGGIII scheme is introduced to alleviate the adverse influence of outliers. Then, the initial robust estimates and the standard deviation can act as a priori information for the CA-rPSO-IGGGIII to search for the optimal estimates. Experiments with simulated and real data demonstrate that the proposed method can robustly estimate the PIM parameters.