Dynamic predictions of surface subsidence are crucial for assessing ground damage and protecting surface buildings. Based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology, a method for making dynamic predictions of large-scale surface subsidence in mining areas can be established; however, the problem of phase coherence loss in InSAR data makes it impossible to predict the complete dynamic subsidence basin. In this study, a method combining the WeiBull time function and the improved probabilistic integral method (IPIM) model was established based on the PIM model, and a method for predicting the dynamic subsidence basin in the mining area was proposed by integrating the IPIM and the combined WeiBull time function. Time-series subsidence data, obtained using SBAS-InSAR, were used as fitting data, and the parameters of the combined WeiBull function were inverted, pixel by pixel, to predict the dynamic subsidence of the working face in the study area. Based on the predicted surface subsidence results of a certain moment in the working face, the parameters of the IPIM model were inverted to predict the subsidence value in the incoherent region. The subsidence predictions of the combined WeiBull time function and the IPIM model were fused using inverse distance weighting (IDW) interpolation to restore the complete subsidence basin in the mining area. This method was tested at the Wannian Mine in Hebei, and the obtained complete subsidence basin was compared with the measured data, with an absolute error range of 0 to 10 mm. The results show that the dynamic subsidence basin prediction method for the SBAS-InSAR mining area, involving the combination of the IPIM model and the combined WeiBull model, can not only accurately fit the time series of surface observation points affected by mining but also accurately restore the subsidence data in the incoherent region to obtain complete subsidence basin information in the mining area.

The surface discontinuous deformation caused by coal mining has great damage to the ecological environment and threatens the safety of human lives. Focusing on the problem of discontinuous deformation (ground fissures and collapsed pits) in mining areas with a thick loess and thin bedrock, this paper uses a coal panel in southern Shanxi in China as research background, and uses field investigation, theoretical analysis and the particle flow code 2D (PFC2D) numerical simulation method to study the movement of overburden and discontinuous ground deformation of mining areas with a thick loess layer and a thin bedrock. The results show that with the continual advance of the working face, the failure of the overlying rock, the changing of force chain shape and the development of cracks under this geological and mining condition have their unique rules. This study analyzes the law of movement of overburden in coal seam mining, explains why discontinuous deformation of the surface occurs in case of a thick loess layer and thin bedrock, and provides reference for the prediction of fracture development under the same geological conditions and the application of the PFC2D in coal seam mining in different geological conditions.

Building extraction from high-resolution remote sensing images is the foundation of many fields, but the existing algorithms cannot extract building details from remote sensing images well because of complex background conditions and the occlusion of interfering objects. To solve the problems of low accuracy and incomplete boundary of traditional building extraction methods, ProCBAM, a parallel attention mechanism based on U-Net network, is adopted and added to the feature transmission step of U-Net. Through the representation of spatial dimension image feature map and channel dimension optimized feature map, we can learn more detailed image information and reduce the error of image recognition. Experiments are carried out on the Massachusetts building dataset, Wuhan University dataset, and IND.v2 dataset, and the experimental results show the effectiveness of this method in building extraction from remote sensing images.

Coal seam mining in the gully area easily causes ground cracks and even induces landslides, which endanger the safety of mining areas. In this paper, combined with the mining conditions of a mining area in southern Shanxi Province, China, ground crack mapping, crack width dynamic monitoring, and the numerical simulation method are used to study the static and dynamic evolution law and the formation mechanism of ground cracks in the gully area. The research shows that ground cracks mainly include dynamic in-plane cracks and boundary cracks. The dynamic in-plane cracks show the characteristics of “opening first and closing later”. The boundary cracks show the characteristics of “only opening and not closing”. It is found that the closure of the dynamic in-plane cracks will decrease (compared with plain areas). The development of ground cracks experiences three stages: the initial formation stage, the dynamic development stage, and the gradually stable stage. The “goaf–surface” structure model and force chain arch structure model are established to more intuitively analyze the formation mechanism of ground cracks. The research results have a specific reference value for preventing ground disasters caused by underground coal mining and land ecological restoration.