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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.

In coal mining areas, surface subsidence poses significant risks to human life and property. Fortunately, surface subsidence caused by coal mining can be monitored and predicted by using various methods, e.g., probability integral method and deep learning (DL) methods. Although DL methods show promise in predicting subsidence, they often lack accuracy due to insufficient consideration of spatial correlation and temporal nonlinearity. Considering this issue, we propose a novel DL-based approach for predicting mining surface subsidence. Our method employs K-means clustering to partition spatial data, allowing the application of a gate recurrent unit (GRU) model to capture nonlinear relationships in subsidence time series within each partition. Optimization using snake optimization (SO) further enhances model accuracy globally. Validation shows our method outperforms traditional Long Short-Term Memory (LSTM) and GRU models, achieving 99.1% of sample pixels with less than 8 mm absolute error.

Mining-induced problems in the coal field seriously threaten the normal operation of the mines and cause significant property losses and environmental disruption. Thus, high precision subsidence prediction is important on the processing of mining subsidence problems. In this paper, we analyzed the formation mechanism of skewed subsidence. The rock beam on the side of the gob and coal pillar presented different supporting reaction force, and the difference resulted in the asymmetric distribution of subsidence velocity, which further led to the formation of the surface skewed subsidence basin. The relationship between the wave curve and vibration curve was determined, and the skewed subsidence process of the surface point in the mining affected area was analyzed. The total duration of the initial and accelerated subsidence phases is smaller than that of the decelerated and end subsidence phases. Then, from the skewed subsidence characteristics, the skewed subsidence prediction model based on the lognormal function was built. An application example was selected to validate the feasibility and effectiveness of the proposed model. Results showed that the model has good prediction ability.

In order to alleviate the risk of landslides on high and steep slopes during excavation, slope protection coal pillars are commonly increased at the site to maintain slope stability, which causes a considerable waste of coal. In roof cutting for pressure relief at quarries, the movement of the overburden structure is artificially regulated by blasting. However, there is a lack of theoretical research on the impact on the slope movement. In order to explore how blasting roof cutting affects the deformation and fracture of slopes, a case study of the 10101 working face of Xinyuan Coal Mine was carried out. The particle flow code numerical simulation of the mining with different heights of roof cutting was performed to analyze the impact of the height of roof cutting on the movement of overlaying rock formation, the development of slope fractures, stress distribution, collapse angle, slope deformation and fracture, etc. The research results are as follows: the overlaying rock formation can be divided into the stable zone, the rotary zone and the subsidence area by displacement; a reasonable roof-cutting height allows the cutting and crushing of the overlaying rock formation, as a result of which the movement boundary is offset to cutting line and the slope is within the stable area; at the same time, the horizontal displacement of the rock formation in the rotary zone, the collapse angle and the stress at slope bottom are reduced, which controls the deformation and failure of slope by inhibiting the development of cracks at slope bottom and reducing the rotation of the rotary zone to the goaf zone. The research results provide certain references for controlling ground sedimentation and slopes in blasting roof cutting.

Surface subsidence induced by mineral exploitation poses significant risks to surrounding environments, lives, and property in mining areas. Accurate prediction of this subsidence is therefore critical for effective mitigation efforts, but remains challenging due to its complex spatio-temporal characteristics, which often exhibit both nonlinear dynamics and underlying trends. Existing single-model approaches may struggle to fully capture this complexity, potentially leading to reduced prediction accuracy. To address this, a surface subsidence prediction model called seasonal trend decomposition–extreme gradient boosting tree (STL-XGBoost) was proposed, which is based on small baseline subset interferometric synthetic aperture radar (SBAS-InSAR). The surface subsidence sequence of a mining area from 2020 to 2023 was obtained using SBAS-InSAR technology; then, STL decomposition was applied to separate the subsidence sequence and obtain the trend term. The non-trend term was used to predict the subsidence sequence for the XGBoost model, while the trend term was predicted using STL decomposition, and the nonlinear and trend term prediction values were equally weighted to obtain the final composite model prediction value. Compared with the mean absolute error (MAE) of the prediction results of the single XGBoost model, that of the STL-XGBoost model was reduced by 31%, and the root mean squared error (RMSE) was reduced by 38%. The prediction results had a higher accuracy and a strong correlation of above 0.9 with the original time series, indicating the effectiveness and reliability of the proposed method in providing a strong technical support for surface-subsidence prediction in mining areas.

There are many parameters influencing mining induced surface subsidence. These parameters usually interact with one another and some of them have the characteristic of fuzziness. Current approaches to predicting the subsidence cannot take into account of such interactions and fuzziness. In order to overcome this disadvantage, many mining induced surface subsidence cases were accumulated, and an artificial neuro fuzzy inference system(ANFIS) was used to set up 4 ANFIS models to predict the rise angle, dip angle, center angle and the maximum subsidence, respectively. The fitting and generalization prediction capabilities of the models were tested. The test results show that the models have very good fitting and generalization prediction capabilities and the approach can be applied to predict the mining induced surface subsidence.

To explore the law of ground deformation from shallow-buried close-distance multi-seam mining, an observation station was built in the Bulianta Coal Mine to measure and record the periodic variation of related parameters about ground subsidence and surface cracks with the advancement of working face. From the data observed from the field, it can be found that, when lower seam mining, the ground subsidence above the previously mined area was deeper and steeper than that above the left pillar; besides, the influence scope of the former was larger than that of the latter. In terms of ground cracks, the ground cracks were formed ahead of the working face and developed rapidly during the period of the breakage of the immediate roof. Besides, the average interval of the ground cracks above the previous gob was 14.75 m, and still existed and hardly changed after the advancement of the working face; while that above the left pillar was 27.8 m and most of them were closed. In addition, when the advance rate of the working face was 12.8 m/day, the advance influence distance of the mining surface crack reached the minimum of 13.6 m. This finding is helpful for protecting the surficial environment in mining area during and after mining operations and is also of significance to conduct green mining in other mining areas.

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 paper presents the results of terrain subsidence monitoring in Poland’s Upper Silesian Coal Basin (USCB) mining area using Differential Interferometry Synthetic Aperture Radar (DInSAR) and Persistent Scatterer Interferometry (PSI). The study area accounts for almost three million inhabitants where mining which started in the 19th century, has produced severe damage to buildings and urban infrastructures in past years. The analysis aimed to combine eight different datasets, processed in two techniques, coming from various sensors and covering different periods. As a result, a map of areas that have been exposed to subsidence within 3045 square kilometers was obtained. The map covers a period of twenty years of intensive mining activities, i.e. 1992–2012. A total of 81 interferograms were used in the study. The interferograms allowed not only to determine subsidence troughs (basins) formed from 1992 to 2012 but also to observe subsidence development over time. The work also included five sets of PSI processing, covering different temporal and spatial ranges, which were used to determine zones of residual subsidence. Based on InSAR datasets, an area of 521 square kilometers under the influence of mining activities were determined. Within the subsiding zones, an area of 312.5 square kilometers of the rapid increase in subsidence was identified on the interferograms. The study of combined different InSAR datasets provided large-area and long-term information on the impact of mining activities in the Upper Silesia Coal Basin.

Solid backfill technology, which can achieve precise control of surface subsidence, has become the primary method used to extract “under three” coal resources (under railways, buildings, and water bodies), especially under buildings. This paper proposes a probability integration model for surface subsidence prediction based on the equivalent mining height (EMH) theory and describes the basic control principle for surface subsidence, i.e., guaranteeing a maximum security standard for surface buildings, based on the maximum EMH, by controlling the backfill body’s compression ratio (BBCR). Based on this control principle, an engineering design process for solid backfill mining under buildings was established, and an engineering design method that employs the BBCR as the critical control indicator and a method for determining the key parameters in subsidence prediction are proposed. In applications at the Huayuan coal mine in China, the measured subsidence values were less than predicted; the measured BBCR was controlled at a level higher than 90 %, which was greater than in the theoretical design; the surface subsidence of buildings was controlled at mining level I. The results of application of the methods proposed in this paper show that the basic principles of controlling the BBCR and maximum EMH provide clear guidance for surface subsidence control in solid backfill mining engineering practice.