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.

The application of remote sensing technology in water body detection has become increasingly widespread, offering significant value for environmental monitoring, hydrological research, and disaster early warning. However, the existing methods face challenges in multi-scene and multi-temporal water body detection, including the diverse variations in water body shapes and sizes that complicate detection; the complexity of land cover types, which easily leads to false positives and missed detections; the high cost of acquiring high-resolution images, limiting long-term applications; and the lack of effective handling of multi-temporal data, making it difficult to capture the dynamic changes in water bodies. To address these challenges, this study proposes a novel network for multi-scene and multi-temporal water body detection based on spatiotemporal feature extraction, named TSAE-UNet. TSAE-UNet integrates convolutional neural networks (CNN), depthwise separable convolutions, ConvLSTM, and attention mechanisms, significantly improving the accuracy and robustness of water body detection by capturing multi-scale features and establishing long-term dependencies. The Otsu method was employed to quickly process Sentinel-1A and Sentinel-2 images, generating a high-quality training dataset. In the first experiment, five rectangular areas of approximately 37.5 km² each were selected to validate the water body detection performance of the TSAE-UNet model across different scenes. The second experiment focused on Jining City, Shandong Province, China, analyzing the monthly water body changes from 2020 to 2022 and the quarterly changes in 2022. The experimental results demonstrate that TSAE-UNet excels in multi-scene and long-term water body detection, achieving a precision of 0.989, a recall of 0.983, an F1 score of 0.986, and an IoU of 0.974, significantly outperforming FCN, PSPNet, DeepLabV3+, ADCNN, and MECNet.

The breakage of massive thick weakly cemented rock layer is likely to cause strong mine earthquakes, which threatens the safe and sustainable production of the mine. In order to reveal the breakage law of high-level giant-thickness weakly cemented overburden rock and prevent the occurrence of mine earthquakes, we took the 2201 and 2202 working faces of Yingpanhao Coal Mine as the research object, established the mechanical calculation model of breakage of the high-level giant-thickness weakly cemented overburden, and used the methods of medium-thickness plate and short-beam function to solve the breakage law of high-level giant-thickness weakly cemented overburden rock. The findings indicate that during initial mining operations of high-level giant-thickness weakly cemented overburden rock, the applied force remains well below its bearing capacity. This condition ensures the stability of the overburden, effectively suppressing energy release events and minimizing surface subsidence. However, as mining progresses and approaches its operational limits, the overburden experiences both tensile and shear failures. This results in substantial increases in surface subsidence and the occurrence of frequent high-energy events. Finally, the model is verified against the surface-measured data and microseismic data of Yingpanhao Coal Mine, which proves the reliability of the model. The research results have important practical significance for mine earthquake prevention and safe and sustainable mining in similar geological conditions.

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.

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.

The contradiction between coal mining and urban construction in coal resource‐based cities is prominent, which greatly limits the sustainable development of these cities. Pan’an New City is a key mining‐induced subsidence area in Xuzhou City, which presents significant challenges to the construction of the new city. Therefore, in order to ensure the safe construction of Pan’an New City, the residual deformation and stability of the goaf sites must be monitored and evaluated. Under such background, based on the measured leveling data of the mining‐induced surface deformation in a coal mine near Pan’an New City, this paper first analyzed the accuracy of InSAR monitoring of surface deformation in coal mining subsidence area by SBAS‐InSAR technology. Then, the SBAS‐InSAR technology was used to monitor the surface subsidence rate and cumulative subsidence in the coal mining subsidence area of Pan’an New City, based on the 29 scene SAR data during Dec. 2020 and Jan. 2022. The results showed that the goaf site in the north and northwest of Pan’an New City is unstable, while the other areas are stable. Finally, according to the monitoring results, the suggestions have been put forward for the construction of Pan’an New Town on the goaf site. The research results have important theoretical and practical significance for the reuse of goaf sites in Pan’an New City and similar areas in Xuzhou.