The collapse of open-pit coal mine slopes is a kind of severe geological hazard that may cause resource waste, economic loss, and casualties. On 22 February 2023, a large-scale collapse occurred at the Xinjing Open-Pit Mine in Inner Mongolia, China, leading to the loss of 53 lives. Thus, monitoring of the slope stability is important for preventing similar potential damage. It is difficult to fully obtain the temporal and spatial information of the whole mining area using conventional ground monitoring technologies. Therefore, in this study, multi-source remote sensing methods, combined with local geological conditions, are employed to monitor the open-pit mine and analyze the causes of the accident. Firstly, based on GF-2 data, remote sensing interpretation methods are used to locate and analyze the collapse area. The results indicate that high-resolution remote sensing can delineate the collapse boundary, supporting the post-disaster rescue. Subsequently, multi-temporal Radarsat-2 and Sentinel-1A satellite data, covering the period from mining to collapse, are integrated with D-InSAR and DS-InSAR technologies to monitor the deformation of both the collapse areas and the potential risk to dump slopes. The D-InSAR result suggests that high-intensity open-pit mining may be the dominant factor affecting deformation. Furthermore, the boundary between the collapse trailing edge and the non-collapse area could be found in the DS-InSAR result. Moreover, various data sources, including DEM and geological data, are combined to analyze the causes and trends of the deformation. The results suggest that the dump slopes are stable. Meanwhile, the deformation trends of the collapse slope indicate that there may be faults or joint surfaces of the collapse trailing edge boundary. The slope angle exceeding the designed value during the mining is the main cause of the collapse. In addition, the thawing of soil moisture caused by the increase in temperature and the reduction in the mechanical properties of the rock and soil due to underground voids and coal fires also contributed to the accident. This study demonstrates that multi-source remote sensing technologies can quickly and accurately identify potential high-risk areas, which is of great significance for pre-disaster warning and post-disaster rescue.

With the launch of the Sentinel-1 satellites, it becomes easy to obtain long time-series dual-pol (i.e., VV and VH channels) SAR images over most areas of the world. By combining the information from both VV and VH channels, the polarimetric persistent scatterer interferometry (PolPSI) techniques is supposed to achieve better ground deformation monitoring results than conventional PSI techniques (using only VV channel) with Sentinel-1 data. According to the quality metric used for polarimetric optimizations, the most commonly used PolPSI techniques can be categorized into three main categories. They are PolPSI-ADI (amplitude dispersion index as the phase quality metric), PolPSI-COH (coherence as the phase quality metric), and PolPSI-AOS (taking adaptive optimization strategies). Different categories of PolPSI techniques are suitable for different study areas and with different performances. However, the study that simultaneously applies all the three types of PolPSI techniques on Sentinel-1 PolSAR images is rare. Moreover, there has been little discussion about different characteristics of the three types of PolPSI techniques and how to use them with Sentinel-1 data. To this end, in this study, three data sets in China have been used to evaluate the three types of PolPSI techniques’ performances. Based on results obtained, the different characteristics of PolPSI techniques have been discussed. The results show that all three PolPSI techniques can improve the phase quality of interferograms. Thus, more qualified pixels can be used for ground deformation estimation by PolPSI methods with respect to the PSI technique. Specifically, this pixel density improvement is 50%, 12%, and 348% for the PolPSI-ADI, PolPSI-COH, and POlPSI-AOS, respectively. PolPSI-ADI is the most efficient method, and it is the first choice for the area with abundant deterministic scatterers (e.g., urban areas). Benefitting from its adaptive optimization strategy, PolPSI-AOS has the best performances at the price of highest computation cost, which is suitable for rural area applications. On the other hand, limited by the medium resolution of Sentinel-1 PolSAR images, PolPSI-COH’s improvement with respect to conventional PSI is relatively insignificant.

Accurately locating goafs is critical for identifying illegal mining, preventing mining-related geohazards, and facilitating the development and utilization of underground spaces. Conventional methods for locating goafs with InSAR techniques primarily rely on the Probability Integral Model (PIM), which tends to overestimate the ground deformation under subcritical extraction. On the other hand, the number of subcritical extraction working faces significantly rises with mining depth. Under these circumstances, accurately locating subcritical underground goafs using existing methods becomes challenging. To this end, a novel method, which incorporates the improved probability integral model (IPIM) and InSAR technique for locating subcritical goafs, is proposed, named the locating goaf method based on IPIM (LGM-IPIM). Firstly, based on the IPIM, a model between the subcritical goaf parameters and InSAR-derived deformation is built. Then, to reduce the influence of surrounding mining, the goaf azimuth angle is determined with textures and patterns of the InSAR-derived deformation time series. Finally, the genetic algorithm-particle swarm optimization (GA-PSO) is employed to determine the goafs’ parameters. The effectiveness of the proposed algorithm has been verified by simulation and real data. The results demonstrate that the proposed LGM-IPIM outperforms conventional methods, presenting the best performance and the highest accuracy. Specifically, compared to the locating goaf method based on PIM (LGM-PIM), the proposed LGM-IPIM improves the location accuracy of goaf boundary points by 28.90% and 86.23% in Areas A and B, respectively. In addition, the proposed LGM-IPIM has robustness against minor errors within the deformation monitoring and IPIM parameters.

Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas.

In recent years, the prolonged exploitation of coal resources has led to the depletion of coal reserves in some mining areas, resulting in the closure of certain mines worldwide. After mine closures, the fractured rock masses in abandoned mine cavities undergo weathering and degradation due to factors such as stress and groundwater, leading to reduced strength. This change alters the stress distribution and load-bearing capacity of the fractured rock within the abandoned voids, resulting in secondary or multiple deformations on the surface, which pose significant potential threats to surface infrastructure and public safety. Research into the mechanisms, patterns, and predictive methods of secondary surface subsidence in closed mines is thus of great theoretical and practical significance. Based on a literature review and practical monitoring experience in closed mine sites, this study systematically examines and analyzes the current state of secondary surface subsidence monitoring methods, formation mechanisms, spatiotemporal distribution patterns, and prediction methods in closed mines, as well as existing challenges. Initially, we compare the advantages and limitations of conventional surface deformation monitoring techniques with remote sensing techniques, emphasizing the benefits and issues of using InSAR technology for monitoring surface subsidence in closed mines. Next, by reviewing extensive data, we analyze the formation mechanisms and spatiotemporal evolution of secondary surface subsidence in closed mines. Building on this analysis, we discuss numerical and analytical methods for predicting secondary surface subsidence mechanisms in closed mines, evaluating the strengths and weaknesses of each approach. Predictive models for surface subsidence and uplift phases in the longwall collapse method are presented based on the constitutive relationships of fractured rock masses. Finally, the study highlights that the mechanisms and patterns of surface subsidence in closed mines represent a highly complex physical–mechanical process involving geological mining environments, fractured rock structures, constitutive relations, deformation characteristics, hydro-mechanical interactions, and groundwater dynamics, underscoring the need for further in-depth research.

Illegal coal mining is prevalent worldwide, leading to extensive ground subsidence and land collapse. It is crucial to define the location and spatial dimensions of these areas for the efficient prevention of the induced hazards. Conventional methods for goaf locating using the InSAR technique are mostly based on the probability integral model (PIM). However, The PIM requires detailed mining information to preset model parameters and does not account for the layered structure of the coal overburden, making it challenging to detect underground goaves in cases of illegal mining. In response, a novel method based on the InSAR technique and the Stratified Optimal Okada Dislocation Model, named S-ODM, is proposed for locating goaves with basic geological information. Firstly, the S-ODM employs a numerical model to establish a nonlinear function between the goaf parameters and InSAR-derived ground deformation. Then, in order to mitigate the influence of nearby mining activities, the goaf azimuth angle is estimated using the textures and trends of the InSAR-derived deformation time series. Finally, the goaf’s dimensions and location are estimated by the genetic algorithm–particle swarm optimization (GA-PSO). The effectiveness of the proposed method is validated using both simulation and real data, demonstrating average relative errors of 6.29% and 7.37%, respectively. Compared with the PIM and ODM, the proposed S-ODM shows improvements of 19.48% and 52.46% in geometric parameters. Additionally, the errors introduced by GA-PSO and the influence of ground deformation monitoring errors are discussed in this study.