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The main functions of a three-dimensional test device for simulating rock formations and surface movement affected by underground coal mining were described in detail, and a series of similar related tests were carried out. The device consisted of an outer frame, a pressurization unit, a pulling unit, and a coal seam simulation portion. Using this test device, supported by monitoring methods such as the three-dimensional laser scanner method, a model test study on the surface subsidence characteristics caused by coal seam mining was carried out. Combined with the field measurements, the transfer law of surface subsidence caused by coal seam mining was revealed, and the whole surface subsidence response process was analyzed. The experimental results show that the subsidence caused by mining disturbances below the coal seam accounts for 79.3% of the total subsidence, which is the dominant factor of the total surface subsidence. After long-term surface observations, surface subsidence can be divided into four stages after coal mining, and the settlement value of the obvious settlement stage accounts for more than 60% of the total settlement value. The above test results fully reflect the feasibility and practicality of the three-dimensional test device to simulate rock strata and surface movement and provide a new experimental research tool that can be used to further study the surface subsidence characteristics and control caused by coal mining.

The surface subsidence duration and the maximum subsidence velocity are critical indicators to evaluate the stability and severity of surface damage. Precisely predicting them is important for guiding engineering design and protecting ground infrastructure. Traditional manual measurement methods are time-consuming and laborious, and the existing empirical formulas have low accuracy and poor applicability. Therefore, a new prediction method was established in this paper. Measured data from 30 mining areas were used for verification. The results show that the predicted surface subsidence duration is basically consistent with the measured value. The standard deviation of the two is 61 d, and the relative standard deviation is 6.6%. The predicted surface maximum subsidence velocity is basically consistent with the measured value. The standard deviation of the two is 10.0 mm/d, and the relative standard deviation is 1.6%. The surface subsidence duration and the maximum subsidence velocity are positively correlated with the coal seam thickness, negatively and positively correlated with the mining speed, and positively and negatively correlated with the mining depth. The mining speed and mining depth have the same sensitivity to the two indicators, and the coal seam thickness is more sensitive to the surface subsidence duration. Furthermore, construction within the subsidence basin may further contribute to surface subsidence. Therefore, land reuse measures should be implemented following the predicted surface subsidence duration in this paper. This study addresses the knowledge gap in this field by deriving theoretical formulas for surface subsidence duration and maximum subsidence velocity. In the absence of sufficient measured data, engineers can calculate predicted values in combination with geological mining conditions and develop appropriate mining plans based on the extent of surface subsidence.

The maximum surface subsidence velocity (MSSV) is a key parameter for the evaluation of the severity of surface movement and deformation caused by coal mining. In this paper, a new model assumption is proposed based on the classical Knothe time model assumption, and an improved Knothe time model, which only contains one model parameter and can accurately describe the dynamic subsidence law of the surface, has been developed. On this basis, according to the extreme value principle, a prediction model of the MSSV was established. The accuracy and rationality of the MSSV prediction model were verified by using the monitoring values of the MSSV caused by the mining of 30 mining areas. The results showed that the predicted value of the MSSV based on the improved Knothe time model is highly consistent with the monitoring values. However, the predicted value of the MSSV based on the Knothe time model differs greatly from the measured value. The comparison results verified the accuracy and rationality of the prediction model of MSSV. From the theoretical expression of the model, it can be seen that the MSSV can be reduced by reducing the coal mining speed, so as to achieve the purpose of reducing damage to surface structures.

Based on the results of the forecast of the earth’s surface deformations in the zone of influence of underground mining, measures are being developed to reduce the degree of negative man-made influence on undermined buildings, structures and natural objects. Changes in mining conditions affect the decrease in the accuracy of existing forecasting techniques. For example, under the conditions of Kuzbass, the actual earth’s surface subsidence can in a great measure (by 15-25%) exceed the predicted values. Research has shown that one of the factors affecting the source of error is the depth of mining. As a result of the research, a relationship is obtained between the value of the error in calculating the maximum earth’s surface subsidence and the average depth of mining, and a method for adjusting the calculation method is proposed.

The accuracy of InSAR in monitoring mining surface subsidence is always a matter of concern for surveyors. Taking a mining area in Shandong Province, China, as the study area, D-InSAR and SBAS-InSAR were used to obtain the cumulative subsidence of a mining area over a multi-period, which was compared with the mining progress of working faces. Then dividing the mining area into regions with different magnitudes of subsidence according to the actual mining situation, the D-InSAR-, SBAS-InSAR- and leveling-monitored results of different subsidence magnitudes were compared and the Pearson correlation coefficients between them were calculated. The results show that InSAR can accurately detect the location, range, spatial change trend, and basin edge information of the mining subsidence. However, InSAR has insufficient capability to detect the subsidence center, having high displacement rates, and its monitored results are quite different from those of leveling. To solve this problem, the distance from each leveling point to the subsidence center was calculated according to the layout of the rock movement observation line. Besides, the InSAR-monitored error at each leveling point was also calculated. Then, according to the internal relationship between these distances and corresponding InSAR-monitored errors, a correction model of InSAR-monitored results was established. Using this relationship to correct the InSAR-monitored results, results consistent with the actual situation were obtained. This method effectively makes up for the deficiency of InSAR in monitoring the subsidence center of a mining area.

Coal mine surface subsidence detection determines the damage degree of coal mining, which is of great importance for the mitigation of hazards and property loss. Therefore, it is very important to detect deformation during coal mining. Currently, there are many methods used to detect deformations in coal mining areas. However, with most of them, the accuracy is difficult to guarantee in mountainous areas, especially for shallow seam mining, which has the characteristics of active, rapid, and high-intensity surface subsidence. In response to these problems, we made a digital subsidence model (DSuM) for deformation detection in coal mining areas based on airborne light detection and ranging (LiDAR). First, the entire point cloud of the study area was obtained by coarse to fine registration. Second, noise points were removed by multi-scale morphological filtering, and the progressive triangulation filtering classification (PTFC) algorithm was used to obtain the ground point cloud. Third, the DEM was generated from the clean ground point cloud, and an accurate DSuM was obtained through multiple periods of DEM difference calculations. Then, data mining was conducted based on the DSuM to obtain parameters such as the maximum surface subsidence value, a subsidence contour map, the subsidence area, and the subsidence boundary angle. Finally, the accuracy of the DSuM was analyzed through a comparison with ground checkpoints (GCPs). The results show that the proposed method can achieve centimeter-level accuracy, which makes the data a good reference for mining safety considerations and subsequent restoration of the ecological environment.

The mining-induced strata movement and surface subsidence are closely related to the dip angle of coal seam. However, most surface subsidence prediction methods are empirical, and only suitable for nearly flat coal seam mining. In this paper, a new theoretical method is proposed to predict the strata movement boundary and surface subsidence caused by inclined coal seam mining, which considers the influence of key strata, rock quality and coal seam dip angle. The strata movement caused by inclined coal seam mining is generalized and described by three models: analogous hyperbola model (AHM), analogous hyperbola-funnel model (AHFM), and analogous funnel model (AFM). Considering the rock quality of roof and floor strata, the rock mass rating system is adopted to calculate the surface maximum subsidence and its location. Additionally, the distinct element method was used to assess the performance of the theoretical models. The numerical simulation results match well with theoretical predictions of strata movement boundary and surface subsidence. It is discovered that the appearance of surface subsidence troughs is obviously asymmetric. As the dip angle increases, the surface maximum subsidence decreases and its location is laterally displaced. When the dip angle is greater than 50°, the double subsidence troughs can be visualized clearly. Furthermore, the theoretical predictions of surface subsidence are verified by field measurements of two cases. As a result, the theoretical predictions of surface subsidence are greatly improved by comparing with the empirical method.

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.

Groundwater variation can cause land-surface movement, which in turn can cause significant and recurrent harm to infrastructure and the water storage capacity of aquifers. The capital cities in the England (London) and India (Delhi) are witnessing an ever-increasing population that has resulted in excess pressure on groundwater resources. Thus, monitoring groundwater-induced land movement in both these cities is very important in terms of understanding the risk posed to assets. Here, Sentinel-1 C-band radar images and the persistent scatterer interferometric synthetic aperture radar (PSInSAR) methodology are used to study land movement for London and National Capital Territory (NCT)-Delhi from October 2016 to December 2020. The land movement velocities were found to vary between −24 and +24 mm/year for London and between −18 and +30 mm/year for NCT-Delhi. This land movement was compared with observed groundwater levels, and spatio-temporal variation of groundwater and land movement was studied in conjunction. It was broadly observed that the extraction of a large quantity of groundwater leads to land subsidence, whereas groundwater recharge leads to uplift. A mathematical model was used to quantify land subsidence/uplift which occurred due to groundwater depletion/rebound. This is the first study that compares C-band PSInSAR-derived land subsidence response to observed groundwater change for London and NCT-Delhi during this time-period. The results of this study could be helpful to examine the potential implications of ground-level movement on the resource management, safety, and economics of both these cities.