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.

Coordinated and sustainable development of production-living-ecological space (PLES) is directly related to the global energy security and quality of life in coal areas. However, the current surface subsidence control methods have some problems, such as low resource recovery rate, high cost, insufficient materials, which are difficult to meet the requirements for the PLES sustainable development in coal areas. Under this background, based on characteristics of surface subsidence and deformation due to sub-critical extraction, the large protection area, and high deformation tolerance in PLES of mining areas, the new method of regional mining subsidence control was proposed, with the combination of source control and ground rehabilitation. The effectiveness of the method was verified by numerical simulation results and practical applications, and the application principles and implementation methods were proposed. The research results could provide technical support for the sustainable development of coal areas in major coal producing countries of the world.

Mining-induced subsidence boundaries, i.e., the surface areas affected by underground mining, play an important role in surface damage assessment and illegal mining identification. Traditional boundary delineation methods rely on field surveys, which restrict their applicability in regions with limited ground observations. Interferometric Synthetic Aperture Radar (InSAR) technology provides a cost-effective and non-contact solution for delineating subsidence boundaries. However, existing InSAR-based methods for subsidence boundary delineation are susceptible to observation noise and other deformation sources, which reduce the accuracy of boundary identification. To this end, this study proposes a novel method for delineating mining-induced subsidence boundaries by integrating both the magnitude and direction of InSAR-derived deformation gradients, referred to as DMSB-DG. First, time-series line-of-sight (LOS) deformation is obtained based on InSAR technology over mining areas. Then, the Roberts operator is employed to compute the magnitude and direction of the deformation gradients, which serve as the basis for boundary delineation. Finally, the ISODATA clustering algorithm is used, incorporating both the magnitude and direction of the deformation gradients as dual constraints to achieve accurate delineation of mining-affected boundaries. The combination of the two features effectively enhances the completeness and accuracy of boundary delineation. The performance of the proposed DMSB-DG method has been verified by simulation and field data. Specifically, compared with the adaptive mining subsidence boundary delimitation (ASBD) method, the proposed method achieved Kappa coefficients of 91.96% and 87.28%, representing improvements of 21.23% and 27.14% in two field tests, respectively. Furthermore, the influence of ascending and descending SAR images, as well as observational noise, on the performance of the proposed algorithm is also evaluated. The results demonstrate that the proposed method effectively suppresses InSAR noise and other interfering deformations, enabling high-precision delineation of mining impact boundaries.

The mining of shallow coal seams with hard roofs poses a threat to surface structures. In order to ensure the protection of these buildings, backfill mining is increasingly used in these types of coal seams. However, due to the lack of appropriate surface subsidence prediction methods, there are concerns about whether backfill mining can meet the requirements of building protection. In this study, through numerical simulation and physical experiments, the movement characteristics of the strata and surface were studied in the backfill mining of a shallow coal seam with a hard roof. Our results indicate that the backfilling ratio significantly influences strata movement and surface subsidence. As the backfilling ratio increases, the surface deformation in the backfill under the hard roof of the shallow coal seam transitions from discontinuous to continuous. When the backfilling ratio exceeds 60%, the deformation characteristics of the overburden and surface align with the probability integral method model. Consequently, a novel surface subsidence prediction method for backfill mining in shallow coal seams under hard roofs is proposed. This method was successfully applied at Yungang Mine, validating its effectiveness. These research findings have significant practical implications for the design of backfill mining in shallow coal seams under hard roofs.

Illegal mining operations induce cascading ecosystem degradation by causing extensive ground subsidence, necessitating accurate underground goaf localization for effectively induced-hazard mitigation. The conventional locating method applied the synthetic aperture radar interferometry (InSAR) technique to obtain ground deformation to estimate underground goaf parameters, and the locating accuracy was crucially contingent upon the appropriateness of nonlinear deformation function models selection and the precision of geological parameters acquisition. However, conventional model-driven underground goaf locating frameworks often fail to sufficiently integrate prior geological information during the model selection process, potentially leading to increased positioning errors. In order to enhance the operational efficiency and locating accuracy of underground goaf, deformation model selection must be aligned with site-specific geological conditions under varying cases of prior information. To address these challenges, this study categorizes prior geological information into three different hierarchical levels (detailed, moderate, and limited) to systematically investigate the correlations between model selection and prior information. Subsequently, field validation was carried out by applying two different non-linear deformation function models, Probability Integral Model (PIM) and Okada Dislocation Model (ODM), with three different prior geological information conditions. The quantitative performance results indicate that, (1) under a detailed prior information condition, PIM achieves enhanced dimensional parameter estimation accuracy with 6.9% reduction in maximum relative error; (2) in a moderate prior information condition, both models demonstrate comparable estimation performance; and (3) for a limited prior information condition, ODM exhibits superior parameter estimation capability showing 3.4% decrease in maximum relative error. Furthermore, this investigation discusses the influence of deformation spatial resolution, the impacts of azimuth determination methodologies, and performance comparisons between non-hybrid and hybrid optimization algorithms. This study demonstrates that aligning the selection of deformation models with different types of prior geological information significantly improves the accuracy of underground goaf detection. The findings offer practical guidelines for selecting optimal models based on varying information scenarios, thereby enhancing the reliability of disaster evaluation and mitigation strategies related to illegal mining.

The present study explores a three-dimensional deformation monitoring method for the better delineation of the surface subsidence range in coal mining by combining the mining subsidence law with the geometries of SAR imaging. The mining surface subsidence of the filling working face in Shandong, China, from March 2018 to June 2021, was obtained with 97 elements of Sentinel-1A data, the small baseline subset (SBAS) technique, and the proposed method, respectively. By comparison with the ground leveling of 46 observation stations, it is shown that the average standard deviation of the SBAS monitoring results is 10.3 mm; with this deviation, it is difficult to satisfy the requirements for the delimitation of the mining impact area. Meanwhile, the average standard deviation of the vertical deformation obtained by the proposed method is 6.2 mm. Compared to the SBAS monitoring accuracy, the monitoring accuracy of the proposed method is increased by 39.8%; thus, it meets the requirements for the precise delineation of the surface subsidence range for backfill mining.