The exploitation of underground coal resources has stepped up local economic and social development significantly. However, it was inevitable that time-dependent surface settlement would occur above the mined-out voids. Subsidence associated with local geo-mining can last from several months to scores of years and can seriously impact infrastructure, city planning, and underground space utilization. This paper addresses the problems in predicting progressive residual surface subsidence. The subsidence process was divided into three phases: a duration period, a residual subsidence period, and a long-term subsidence period. Then, a novel mathematical model calculating surface progressive residual subsidence was proposed based on the logistic time function. After the duration period, the residual subsidence period was extrapolated according to the threshold of the surface sinking rate. The validation for the proposed model was estimated in light of observed in situ data. The results demonstrate that the logistic time function is an ideal time function reflecting surface subsidence features from downward movement, subsidence rate, and sinking acceleration. The surface residual subsidence coefficient, which plays a crucial role in calculating surface settling, varies directly with model parameters and inversely with time. The influence of the amount of in situ data on predicted values is pronounced. Observation time for surface subsidence must extend beyond the active period. Thus back-calculated parameters with in situ measurement data can be reliable. Conversely, the deviation between predictive values and field-based ones is significant. The conclusions in this study can guide the project design of surface subsidence measurement resulting from longwall coal operation. The study affords insights valuable to land reutilization, city planning, and stabilization estimation of foundation above an abandoned workface.

Jiaozuo, located in the northwest of Henan Province, is one of the six major anthracite production bases in China. It is susceptible to land subsidence due to over a hundred years of mining history, continuous urbanization, frequent human activities, etc., which poses a great threat to urban infrastructure construction and people’s production and lives. However, traditional leveling techniques are not sufficient for monitoring large areas of land subsidence due to the time-consuming, labor-intensive, and expensive nature of the process. Furthermore, the results of conventional methods may not be timely, rendering them ineffective for monitoring purposes. With the continuous advancement of urbanization, land subsidence caused by groundwater extraction, ground load, and other factors in daily life poses a great threat to urban infrastructure construction and people’s production and lives. In order to monitor the land subsidence in the area of Jiaozuo city, this article uses the Sentienl-1A satellite data covering the city from March 2017 to March 2021 to obtain the accumulated land subsidence and the average land subsidence rate based on the Small Baselines Subset InSAR (SBAS-InSAR) technology. The results indicate that the surface of Jiaozuo area is generally stable, and there has been no large-scale settlement. The settlement rate is roughly between −1 mm/a and 2.2 mm/a, and the areas with obvious land subsidence are mainly located in the southeast and east of Jiaozuo city center. After field investigation, it was found that the land subsidence is mainly caused by two reasons: groundwater excessive mining and excessive surface load. In the northeast of Jiaozuo city, there is a certain uplift area. After on-site investigation, it was found that the area is connected to a tailings pond of an aluminum mine, constantly accumulating abandoned rock masses and sediment, causing an annual uplift rate of +6~+ 24 mm/a. The large-scale extraction of groundwater from farmland in the urban–rural integration area for irrigation of wheat has led to the settlement of buildings in the area with a rate of −11–−74 mm/a.

The geological environment damage caused by coal mining subsidence has become an important factor affecting the sustainable development of mining areas. Reconstruction of the Coal Mining Subsidence Field (CMSF) is the key to preventing geological disasters, and the needs of CMSF reconstruction cannot be met by solely relying on a single remote sensing technology. The combination of Unmanned Aerial Vehicle (UAV) and Synthetic Aperture Radar (SAR) has complementary advantages; however, the data fusion strategy by refining the SAR deformation field through UAV still needs to be updated constantly. This paper proposed a Prior Weighting (PW) method based on Satellite Aerial (SA) heterogeneous remote sensing. The method can be used to fuse SAR and UAV Light Detection and Ranging (LiDAR) data for ground subsidence parameter inversion. Firstly, the subsidence boundary of Differential Interferometric SAR (DInSAR) combined with the large gradient subsidence of Pixel Offset Tracking (POT) was developed to initialize the SAR preliminary CMSF. Secondly, the SAR preliminary CMSF was refined by UAV LiDAR data; the weights of SAR and UAV LiDAR data are 0.4 and 0.6 iteratively. After the data fusion, the subsidence field was reconstructed. The results showed that the overall CMSF accuracy improved from ±144 mm to ±51 mm. The relative errors of the surface subsidence factor and main influence angle tangent calculated by the physical model and in situ measured data are 1.3% and 1.7%. It shows that the proposed SAR/UAV fusion method has significant advantages in the reconstruction of CMSF, and the PW method contributes to the prevention and control of mining subsidence.

Mining can lead to overburden failure and ground damage, which are more severe in mining shallow buried thick coal seams (SBTCS) under thick aeolian sand (TAS). We attempted to discover characteristics of mining in this particular geological condition through theoretical derivation and numerical simulation, and field monitoring. Theoretical methods, combined with numerical simulation and field monitoring methods, reveal the essence of the development and distribution of surface cracks caused by mining SBTCS and depth to thickness ratio (DTR) to be 13.43, less than 15. The findings show that, when mining SBTCS, the overburden breaks down periodically, the initial collapse distance is greater than the collapse step, approximately 55 m on average, and the collapse step is approximately 45 m, on average, in the Daliuta Coal Mine. The collapsed blocks are stacked into goaf and form “masonry beams”, and many cracks and pores are generated between the blocks. The weak stress of the aeolian sand layer causes the movement angle in the aeolian sand layer to be smaller than that in the bedrock, and leads to much sheer, tension and compression failure on the ground, and the main forms of cracks are compression uplift, tensile cracking, shear step.

As complex and heterogeneous materials, the mechanical properties of rocks are still in need of further investigation regarding the mechanisms of the effects of water. In engineering projects such as goaf foundation treatment and ecological restoration, it is particularly important to describe the fracturing process of non-uniform water-containing sandstone media. The study utilized the theory of continuum mechanics to adopt an elastoplastic strain-softening constitutive relationship and develop a numerical model for analyzing the uniaxial compressive strength and failure characteristics of non-uniform water-containing sandstone. The results indicate that, compared with the reference rock sample, the shorter the capillary path of water entering the rock sample’s internal pores or the larger the contact area with water, the shorter the time required for the rock sample to be saturated. Increasing the water content causes a rapid decline in the rock sample’s elastic modulus and intensifies its brittleness. Group D2 and D3 samples exhibited a decrease in average peak strength to 70.4% and 62.1%, respectively, along with a corresponding decrease in the elastic modulus to 90.78% and 76.55%, indicating significant strain softening. While the failure mode of the rock sample remains consistent across different water contents, the homogeneity of failure shows significant variation. Increasing volumetric water content raises the likelihood of interconnecting cracks between rock samples, resulting in a progressive decline in macroscopic mechanical properties such as peak strength, critical strain, and elastic modulus. This research is significant in advancing the theory and construction technology for ecological restoration in goaf areas.

In Africa, the distribution of color steel tile roof (CSTR) can reflect the living standard of residents, and the analysis of its temporal and spatial changes can reflect the local changes in local living conditions. It is helpful to analyze the change of the local economic level. By using the satellite remote sensing image processing method to obtain the temporal and spatial change characteristics of CSTR and to analyze the changes in residents’ living conditions in Munyaka, Eldoret, Kenya, Africa, the model of multifeature decision tree method (DTM) extraction was established. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index (NDBI) were used to remove farmland from the difference of the CSTR. The Normalized Difference Surface Index (NCSI) was constructed, and the texture features were analyzed to eliminate wasteland and bare land, respectively. The research results show that the Kappa coefficient is 0.9223, and the user precision and mapping precision are 97.79% and 91.10%, respectively. At the same time, combined with the Erdoret municipal road project, the changes of CSTR before and after the project in 2016–2020 are studied. Compared the area change of CSTR in 2016–2018 with that in 2018–2020, the annual growth rate before the construction of the municipal road project is about 3.47%. After the completion of the project, the annual growth rate is 7.29%, more than twice the rate before the construction. This method can realize the dynamic monitoring of CSTR, reflect the changes of the residents’ living environment in the region, help analyze the improvement of poverty in Africa, and help understand the changes of African economic conditions.