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Quantification and evaluation of the spatiotemporal changes in soil quality is important to understand soil degradation mechanisms and restore the damaged land productivity. However, the effects of coal mining subsidence on the spatial and temporal characteristics of soil quality are not well understood. We investigated the contents of pH, organic matter (OM), total nitrogen (TN), nitrate nitrogen (NN), ammonia nitrogen (AN), total phosphorus (TP), available phosphorus (AP), available potassium (AK), total potassium (TK), cation exchange capacity (CEC), sucrase activity (SA), urease activity (UA), phosphatase activity (PA), catalase activity (CA) and dehydrogenase activity (DA) in the coal mining subsided area. The results showed that the contents of TN, NN, AN, TP, AK, TK, SA, UA, PA, CA and DA exhibited significant (P < 0.05) differences among the four seasons. Compared with the upper layer (0-20 cm), the lower layer (20-40 cm) contained higher contents of AN, NN, TN, TP and TK but lower contents of SA, UA, PA, CA and DA. The NN, AP, TP, AK and UA were identified as key indicators in the minimum dataset using principal component analysis. The seasonal changes of soil quality index (SQI) were in the following order: winter (0.707), spring (0.681), summer (0.616), and autumn (0.563). The spatial changes of SQI were highest for middle slope position 3 (0.508), followed by lower slope position 4 (0.507), top slope position 1 (0.446), upper slope position 2 (0.442), and bottom slope position 5 (0.437). Based on these spatiotemporal changes in soil quality, it was suggested that the application of multiple land use types may be a useful method for land reclamation and the interest of local farmers in the coal mining subsided area. [ABSTRACT FROM AUTHOR]

It is crucial to clarify the concentrations and sources of heavy metals in sediment under the influence of coal mining activities. This study surveyed heavy metals (V, Mn, Ni, Zn, Cu, Pb, Cr, and As) in sediments from the mine subsidence section of the Tuohe River in the Huaibei coalfield of China. The Enrichment Factor (EF) method and the Nemerow Comprehensive Pollution Index (NCPI) method were applied to evaluate the contamination status of heavy metals in sediments. Principal Component Analysis (PCA) and the Positive Matrix Factorization (PMF) model were used to apportion the sources of heavy metals. The results showed that the mean contents of V, Mn, Ni, Zn, Cu, Pb, Cr, and As were 0.93, 1.21, 1.20, 1.06, 1.15, 0.80, 1.18, 1.78 times higher than the background values, respectively. EF results showed that the pollution levels of Cr and As were higher than other heavy metals in the study area. NCPI results showed that the sites S1-S7 and S10-S11 of the subsidence section were slightly polluted while the sites S8-S9 of the non-subsidence section were moderately polluted. Based on PCA and PMF, three sources were identified: mining source (37.2%), agricultural source (34.8%), and natural source (28.0%). This study provides further insights into the heavy metal pollution treatment of aquatic ecosystems in mining areas. [ABSTRACT FROM AUTHOR]

Understanding the movement and alteration of carbon and nitrogen elements in soil is crucial for addressing ecological restoration in coal mining subsidence areas. However, the migration process and mechanisms of these elements during the natural restoration process remain unclear. This study analysed the fluctuating variations and formation mechanisms of TC and TN content in soil profiles at different restoration times in coal mining subsidence areas. Initially, both TC and TN contents decreased followed by an increase during the natural restoration process. Soil compaction effects, variation of C/N ratio, and soil enzyme activity were considered as predominant factors controlling carbon and nitrogen element cycles in collapsed soil profiles. This study will provide valuable insights into understanding the mechanisms behind carbon and nitrogen cycles during natural recovery while establishing a scientific basis for ecological restoration in coal mining subsidence areas. [ABSTRACT FROM AUTHOR]

In recent years, the prolonged exploitation of coal resources has led to the depletion of coal reserves in some mining areas, resulting in the closure of certain mines worldwide. After mine closures, the fractured rock masses in abandoned mine cavities undergo weathering and degradation due to factors such as stress and groundwater, leading to reduced strength. This change alters the stress distribution and load-bearing capacity of the fractured rock within the abandoned voids, resulting in secondary or multiple deformations on the surface, which pose significant potential threats to surface infrastructure and public safety. Research into the mechanisms, patterns, and predictive methods of secondary surface subsidence in closed mines is thus of great theoretical and practical significance. Based on a literature review and practical monitoring experience in closed mine sites, this study systematically examines and analyzes the current state of secondary surface subsidence monitoring methods, formation mechanisms, spatiotemporal distribution patterns, and prediction methods in closed mines, as well as existing challenges. Initially, we compare the advantages and limitations of conventional surface deformation monitoring techniques with remote sensing techniques, emphasizing the benefits and issues of using InSAR technology for monitoring surface subsidence in closed mines. Next, by reviewing extensive data, we analyze the formation mechanisms and spatiotemporal evolution of secondary surface subsidence in closed mines. Building on this analysis, we discuss numerical and analytical methods for predicting secondary surface subsidence mechanisms in closed mines, evaluating the strengths and weaknesses of each approach. Predictive models for surface subsidence and uplift phases in the longwall collapse method are presented based on the constitutive relationships of fractured rock masses. Finally, the study highlights that the mechanisms and patterns of surface subsidence in closed mines represent a highly complex physical–mechanical process involving geological mining environments, fractured rock structures, constitutive relations, deformation characteristics, hydro-mechanical interactions, and groundwater dynamics, underscoring the need for further in-depth research. [ABSTRACT FROM AUTHOR]

The eco-environment-oriented development (EOD) mode is an innovative project organization and implementation approach that integrates ecological governance, industrial operations, and regional development. However, this mode still requires more in-depth research to strengthen its applicability to spatial perspectives and rural patterns. This study contributes to this context by proposing a rural spatial governance framework based on the EOD mode, focusing on the three main governance elements: space, capital, and subject. By constructing a conceptual model of the multidimensional governance effectiveness of EOD systems, it reveals the coupling mechanisms of three rural elements and proposes governance modes with spatially integrated optimization, capital value circulation, and rural subject connection as the focus of elemental regulation. This mode is further demonstrated through instantiation. In response to the developmental dilemmas of the villages' overall decline owing to coal mining subsidence on the Weibei Plateau, an integrated transformation plan for production, living, and ecological spaces is formulated, based on which the stage-specific focuses are clarified in the holistic governance path of "space-capital-subject". The research findings exemplify the application value and feasibility in regulating element relationships and balancing multiple benefits and can provide a reference at the spatial level to promote rural sustainable development through ecological governance. [ABSTRACT FROM AUTHOR]

Introduction: With the increased application of super-long working faces in coal mining, the surface movement and crack development laws of super-long working faces present an urgent problem to be studied and solved. This study aimed to determine the surface movement and crack development laws of super-long working faces when mining medium-depth buried coal seams. Methods: The research area in Xiaobaodang No. 2 coal mine, China, was the adjacent working faces 01, 02 and 03, with inclination widths of 300 m and 450 m, respectively. The laws were determined by applying methods such as manual surface movement observation, GNSS automatic surface movement observation, surface crack observation, and crack morphology tracing. Results: Compared to the working face with an inclination width of 300 m, the maximum subsidence, maximum horizontal movement value, and maximum subsidence coefficient of the super-long working face with an inclination width of 450 m increased by 15.31%, 4.56%, and 16.13%, respectively. Under the influence of mining the 02 working face, the maximum subsidence of the 01 working face increased by 15% and the surface subsidence patterns of the 01 and 02 working face inclination observation lines showed an asymmetric W shape. Discussion: The widths of the cracks parallel to the open-off cut followed the dynamic development law of opening first, then closing or semi-closing. The widths of the cracks parallel to grooves followed the dynamic development law of opening first, then remaining open. The study results are important to protect mining buildings and the ecological environment. [ABSTRACT FROM AUTHOR]

This study focused on the Jinjie mining area by addressing the severe resource and environmental issues arising from excessive coal mining in Shenmu County, Northwest China. Multi-source remote sensing data from GF-1 and Landsat 8 OLI were utilized in this study. Specifically, the fused remote sensing ecological index (FRSEI) was constructed to conduct a detailed evaluation and analysis of the eco-environmental quality in the mining area from 2013 to 2023. The results indicated that the overall eco-environmental quality of the Jinjie mining area exhibited a trend of initial improvement, followed by degradation, and then improvement again over the decade. The eco-environmental quality of the mine pits and their surrounding areas was significantly lower than the overall level, confirming the destructive impact of coal mining on the eco-environment. Meanwhile, as the study area, Shenmu County is actively utilizing coal mining subsidence areas to develop the photovoltaic power generation industry, aiming to achieve green and low-carbon transformation. Although the construction of photovoltaic power plants initially led to the degradation of the condition of vegetation and the FRSEI, both gradually improved after the plants were operational. Furthermore, by comparing the FRSEI with the remote sensing ecological index (RSEI) calculated solely using Landsat 8 OLI data, we found a high degree of similarity between the two, thereby validating the stability and accuracy of the FRSEI. This study not only provides high-precision data support for eco-environmental monitoring in mining areas but also highlights the potential of multi-source remote sensing data fusion technology in improving monitoring accuracy, further providing a scientific basis for formulating sustainable development strategies specifically for the eco-environment in mining areas. [ABSTRACT FROM AUTHOR]

Multiple active mining faces and extensive excavations under thick-hard strata in deep coal mines result in frequent strong mine earthquakes, often accompanied by significant surface subsidence deformation. Understanding the specific law of surface movement and the spatiotemporal distribution response to intense mine earthquakes is crucial for effectively preventing and mitigating dynamic disasters in deep mines. Utilizing the key layer theory, the intricate strata of the Yingpanhao Coal Mine are systematically delineated, drawing upon the engineering context of working faces 2201 and 2202 within the Ordos Chemical Co., Ltd., a subsidiary of the Shandong Energy Group. Field investigations are conducted to analyze the law of surface subsidence associated with multi-working face extraction within deep thick-hard strata, as well as to elucidate the spatiotemporal distribution characteristics of strong mine earthquakes. Furthermore, the interplay between law of surface subsidence and the spatial distribution of strong mine earthquakes is investigated, revealing a cohesive relationship between these phenomena. The research findings of this study provide certain references for the pre-control of surface subsidence and strong mine earthquakes during multiple working face and large space mining under thick-hard strata in deep coal mine with similar engineering geological conditions. [ABSTRACT FROM AUTHOR]

Bridges in mining areas deform primarily because of surface subsidence caused by underground mining. Analysis of these deformations should consider the synergistic effects between the foundation soil and the bridge superstructure. The geology of mining areas, which is inherently complex, significantly effects the selection of soil mechanics parameters, potentially leading to errors in model calculations. This study focuses on the bridge in the Fengfeng mining area of Handan, Hebei, and uses the probabilistic integral method to predict subsidence at the bottom section below the surface over a specified period. A finite element model of the bridge and foundation soil is constructed, utilising these predicted subsidence contour lines as boundary conditions for simulating the surrounding surface subsidence. Additionally, monitoring data of surface and bridge subsidence from the same period are collected to validate the simulation accuracy, analyse error types and causes, and propose model improvements. After the feasibility of the improvement schemes is validated, the revised model is employed to predict the bridge failure trend. The model parameters can be adjusted based on surface subsidence monitoring data, and by integrating these adjustments with the predicted subsidence results at the bottom section during various mining stages, the bridge failure trend can be accurately predicted. [ABSTRACT FROM AUTHOR]

Underground coal mining induces surface subsidence, which in turn impacts the stability of slopes in mountainous regions. However, research that investigates the coupling relationship between surface subsidence in mountainous regions and the occurrence of multiple surface hazards is scarce. Taking a coal mine in southwestern China as a case study, a detailed catalog of the surface hazards in the study area was created based on multi-temporal satellite imagery interpretation and Unmanned aerial vehicle (UAV) surveys. Using interferometric synthetic aperture radar (InSAR) technology and the logistic subsidence prediction method, this study investigated the evolution of surface subsidence induced by underground mining activities and its impact on the triggering of multiple surface hazards. We found that the study area experienced various types of surface hazards, including subsidence, landslides, debris flows, sinkholes, and ground fissures, due to the effects of underground mining activities. The InSAR monitoring results showed that the maximum subsidence at the back edge of the slope terrace was 98.2 mm, with the most severe deformation occurring at the mid-slope of the mountain, where the maximum subsidence reached 139.8 mm. The surface subsidence process followed an S-shaped curve, comprising the stages of initial subsidence, accelerated subsidence, and residual subsidence. Additionally, the subsidence continued even after coal mining operations concluded. Predictions derived from the logistic model indicate that the duration of residual surface subsidence in the study area is approximately 1 to 2 years. This study aimed to provide a scientific foundation for elucidating the temporal and spatial variation patterns of subsidence induced by underground coal mining in mountainous regions and its impact on the formation of multiple surface hazards. [ABSTRACT FROM AUTHOR]

Prediction of ground movements in the case of continuous subsidence is critically important for the planning of underground mining. Many calculation models are used to predict mining subsidence. A comprehensive method to render current calculation models superfluous can only come from a theoretical model, but the challenge remains in defining the parameters, given the great variety of rock structures found. Hence, innovation through a conceptual and technological study of the subsidence mechanism is needed to ensure that this problem can be solved satisfactorily. In this study, a new method is proposed to predict ground surface subsidence by combining a stochastic medium concept with Geographic Information System (GIS) technology. All subsidence computations are implemented within GIS, where spatial components are used to conduct the subsidence prediction analysis. This paper includes simulations of basic subsidence phenomena and a comparative study of the GIS-based calculation method's suitability against the empirical method from the Subsidence Engineer Handbook (SEH), semi-empirical influence function models, and numerical modeling. First, the influence of basic extraction area categories on the character of mining subsidence at the surface for flat seam layers is verified. Second, subsidence and horizontal displacement profiles are compared for both gently and steeply inclined mining. Finally, the verification of calculated horizontal strain values for an actual case of inclined irregular mining is also conducted. The comparative results of subsidence predictions for flat and gently sloping mining demonstrate the suitability of the GIS-based calculation method for use in underground mining strategy. [ABSTRACT FROM AUTHOR]

Investigating deformation and failure mechanisms in shafts and roadways due to rock subsidence is crucial for preventing structural failures in underground construction. This study employs FLAC3D software (vision 5.00) to develop a mechanical coupling model representing the geological and structural configuration of a stratum–shaft–roadway system. The model sets maximum subsidence displacements (MSDs) of the horsehead roadway's roof at 0.5 m, 1.0 m, and 1.5 m to simulate secondary soil consolidation from hydrophobic water at the shaft's base. By analyzing Mises stress and plastic zone distributions, this study characterizes stress failure patterns and elucidates instability mechanisms through stress and displacement responses. The results indicate the following: (1) Increasing MSD intensifies tensile stress on overlying strata results in vertical displacement about one-fifth of the MSD at 100 m above the roadway. (2) As subsidence increases, the disturbance range of the overlying rock, shaft failure extent, and number of tensile failure units rise. MSD transitions expand the shaft failure range and evolve tensile failure from sporadic to large-scale uniformity. (3) Shaft failure arises from the combined effects of instability and deformation in the horsehead and connecting roadways, compounded by geological conditions. Excitation-induced disturbances cause bending of thin bedrock, affecting the bedrock–loose layer interface and leading to shaft rupture. (4) Measures including establishing protective coal pillars and enhancing support strength are recommended to prevent shaft damage from mining subsidence and water drainage. [ABSTRACT FROM AUTHOR]

Underground coal excavation has caused a series of geological disasters and environmental problems, especially coal mining subsidence. Backfill-strip mining, which combines the advantages of strip mining and backfill mining, can reduce subsidence and improve the recovery rate of coal. Therefore, predicting the impact of backfill-strip mining on the surface environment and strata structure is essential for the better development of backfill-strip mining technology. Here, a scientific and comprehensive mechanical model is creatively proposed. The mechanical model is divided into two systems at the main key strata (MKS): the lower strata of the MKS are regarded as a rectangular plane mechanical model on the Winkler foundation, comprising spaced filling bodies and coal pillars, and the upper strata of the MKS are regarded as a space-layer mechanical model. First, the subsidence function of the MKS is proposed. Then, this function is transmitted to the space-layer mechanical model through the interface. Finally, the mechanical model is used to predict the subsidence of the strata and the surface. The feasibility of the model is verified by numerical simulation and similar material simulation, and the characteristics of strata movement are analyzed. Using the mechanical model, the influences of geological and mining conditions on strata movement are discussed. This provides theoretical guidance for the study of strata movement and mechanisms in backfill-strip mining. [ABSTRACT FROM AUTHOR]

Faults, as unique geological structures, disrupt the mechanical connections between rock masses. During coal mining, faults in the overlying strata can disturb the original stress balance, leading to fault activation and altering the typical subsidence patterns. This can result in abnormal ground deformation and significant damage to surface structures, posing a serious geological hazard in mining areas. This study examines the influence of a known fault (F13 fault) on ground subsidence in the Wannian Mine of the Fengfeng Mining Area. We utilized 12 Sentinel-1A images and applied SBAS-InSAR, StaMPS-InSAR, and DS-InSAR time-series InSAR methods, alongside the D-InSAR method, to investigate surface deformations caused by the F13 fault. The monitoring accuracy of these methods was evaluated using leveling measurements from 28 surface movement observation stations. In addition, the density of effective monitoring points and the relative strengths and limitations of the three time-series methods were compared. The findings indicate that, in low deformation areas, DS-InSAR has a monitoring accuracy of 7.7 mm, StaMPS-InSAR has a monitoring accuracy of 16.4 mm, and SBAS-InSAR has an accuracy of 19.3 mm. [ABSTRACT FROM AUTHOR]

Among the various hazards induced by underground coal mining, surface subsidence tends to cause structural damage to the ground. Therefore, accurate prediction and evaluation of surface subsidence are significant for ensuring mining security and sustainable development. Traditional methods like the probability integral method provide effective predictions. However, these methods do not take into account the consolidation behavior of thick soil layers. In this study, based on the principle of superposition, an improved probability integral method that includes surface subsidence caused by rock layer movement and the consolidation behavior of thick soil layers is developed. The proposed method was applied in the Zhaogu No. 2 coal mine, located in the Jiaozuo mining area. Utilizing unmanned surface vehicle measurement technology, it was found that the maximum subsidence values of the two survey lines were 5.441 m and 4.842 m, with maximum subsidence rate of 62.9 mm/day at observation points. Experimental tests have shown that surface subsidence in deep mining areas with thin bedrock and thick soil layers exhibited a large subsidence coefficient and a wide range of subsidence, closely related to the consolidation behavior of thick soil layers. After verification, compared to the probability integral method, the improved probability integral method incorporating soil consolidation showed a 14.7% reduction in average error and a 22% reduction in maximum error. Therefore, the improved probability integral method proposed can be a very promising tool for forecasting and evaluating potential geohazards in coal mining areas. [ABSTRACT FROM AUTHOR]

Ground cracks resulting from coal mining subsidence can pose significant risks to coal mine safety, production, and the surrounding ecological environment. The use of drone technology coupled with deep learning holds practical value for accurately identifying ground cracks in mining areas. This study introduces an enhanced target detection algorithm based on YOLOv8n for analyzing ground cracks in aerial images captured by unmanned aerial vehicles (UAVs) dealing with small targets and widely distributed in shrubland. The C2f module of the backbone network is improved by incorporating deformable convolution (DCN), enhancing the model's ability to adapt to detected object shapes, focus on small targets, and reduce irrelevant background information. Subsequently, the global attention mechanism (GAM) is integrated into the neck network to minimize feature information loss during training. Finally, the wise IOU (WIOU) loss function replaces the complete IOU (CIOU) loss function to improve the model's generalizability. Experimental results demonstrate that the algorithm enhanced in this study exhibits improvements in precision (P), recall (R), and mean average precision (mAP@0.5) of 3.5%, 4.8%, and 5.1%, respectively, compared to the original YOLOv8n model. Furthermore, compared to other prevalent algorithms, the enhanced algorithm notably reduces model parameters, floating point operations per second (FLOPs), and model size while maintaining high detection accuracy. In conclusion, the enhanced YOLOv8n-based algorithm offers practical benefits for detecting cracks in mining subsidence areas. [ABSTRACT FROM AUTHOR]

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Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents the results of long-term monitoring of land subsidence in the Bielszowice area (Upper Silesian Coal Basin) using archival maps from the late 19th and 20th centuries, as well as contemporary LIDAR models from 2012 and 2022. The research work conducted included an analysis of subsidence caused by mining activities based on four terrain models: a historical terrain model obtained by digitizing Messtischblätter topographic maps, showing the land surface in 1883, a terrain model obtained by vectorizing Polish topographic maps from 1993, and LIDAR digital terrain models from 2012 and 2022. The study shows that over a period of 139 years, the study area subsided by an average of 9.5 m, which translated into an anthropogenic land subsidence rate of 68 mm/year and a subsidence volume of 100.5 million m3. The greatest subsidence occurred in the northern part of the study area, where basins with depths exceeding 30 m (the maximum subsidence amounted to 36 m) emerged. During the 139 years studied, land subsidence affected the entire area that was built up until 2022. Overall, 38.9% of built-up areas subsided by less than 10 m, 54.0% was subject to subsidence ranging between 10 and 20 m, and subsidence of more than 20 m affected 7.1% of the areas. Such large-scale subsidence in an urbanized area resulted in mining damage to houses and other infrastructure (e.g., railroads, roads); in extreme cases, some structures had to be demolished. Bielszowice is a good example of an area where spatial conflicts have emerged that have been related to the activities of industrial plants on the one hand and the development of urban areas on the other. [ABSTRACT FROM AUTHOR]

Coal mining-induced surface subsidence can significantly impact resident safety and hinder regional sustainable development, making precise subsidence monitoring and prediction critical. Existing mining subsidence monitoring technologies often exhibit low spatiotemporal resolution, while subsidence prediction models suffer from heavy dependence on data quality and model assumptions, as well as imprecise parameters. This study addresses these limitations by proposing a novel mining subsidence monitoring and prediction method based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the Convolutional Neural Network—Bidirectional Gated Recurrent Unit—Attention (CNN-BiGRU-Attention) model. Focusing on the Banji mining area in Anhui Province, ground subsidence was monitored from July 15, 2021, to September 3, 2023, utilizing SBAS-InSAR technology with Sentinel-1A satellite data. The monitoring results were validated using leveling measurement data. A CNN-BiGRU-Attention prediction model was subsequently constructed based on the time-series monitoring data. The results indicate that the surface subsidence rate in the study area decreases progressively from northwest to southeast, with an average subsidence rate ranging from -49.844 mm/year to -14.810 mm/year. At feature points, the CNN-BiGRU-Attention model effectively captures the characteristics of subsidence time-series changes. For regional subsidence prediction, this model maintains the smallest error, with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of 1.27 mm and 1.44 mm, respectively, and an absolute prediction error of less than 1 mm in most areas. This study integrates SBAS-InSAR technology with the CNN-BiGRU-Attention model to enable unmanned monitoring and prediction of mining subsidence. In comparison to traditional methods, this approach not only reduces monitoring costs but also enhances the accuracy of subsidence predictions, offering critical technical support for the sustainable development of mining areas. [ABSTRACT FROM AUTHOR]

Undersea mining encounters challenges due to the presence of seawater. An influx of seawater into stop in undersea can result in enormous disaster. Predicting strata settlement is a crucial measure to ensure the safety of undersea mining. This study proposed an intelligent model based on deep forest (DF) to evaluate the strata settlement during undersea mining. Initially, the strata displacement was monitored in the Xishan mining area of Sanshandao gold mine, China. Comprehensive datasets encompassing roof displacement and twelve influencing factors were compiled from 120 observations. Then, these datasets were statistically analyzed and used to train the DF model. The developed DF model achieved a training R2 of 0.971 and a testing R2 of 0.936. Compared with other machine learning models, the DF model has superior performance in the prediction of strata settlement. Moreover, a graphical user interface was designed to facilitate the application of the DF model. Finally, to validate model feasibility, displacement monitoring was conducted in the Xinli mining area of Sanshandao gold mine. Additional datasets were collected to validate the capability of the DF model. The results suggested that the DF model can be used to predict strata subsidence in undersea mining effectively. [ABSTRACT FROM AUTHOR]

As the extraction of mineral resources intensifies, ground subsidence in mining areas has escalated, posing substantial challenges to sustainable development and operational safety. This subsidence, resulting directly from mining activities, significantly compromises the safety of nearby residents by damaging residential structures and infrastructure. Thus, developing precise and dependable methods for predicting ground subsidence is crucial. This study introduces an innovative Cabs-Unet model, which enhances the U-Net architecture by integrating a Convolutional Block Attention Module (CBAM) and Depthwise Separable Convolutions (DSC). This model aims to predict the spatiotemporal dynamics of the Interferometric Synthetic Aperture Radar (InSAR) time series. Employing Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS InSAR) technology, we gathered and validated data on ground subsidence at the Pengzhuang coal mine from May 2017 to November 2021, covering 130 scenes, with its accuracy corroborated by levelling survey results. An empirical evaluation of the Cabs-Unet model in two distinct subsidence zones demonstrated superior performance over conventional methods like Convolutional Long Short-Term Memory (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN), with Root Mean Square Error (RMSE) values of 1.44 and 1.70, respectively. These findings highlight the model's efficacy in accurately predicting spatiotemporal InSAR ground subsidence. Further predictive analysis using InSAR data indicated an expected increase in subsidence, projecting cumulative declines of −457 mm in Area A and −1278 mm in Area B by 17 July 2022. Our model proves effective in assessing subsidence, promptly detecting potential risks and facilitating the rapid implementation of risk mitigation strategies. [ABSTRACT FROM AUTHOR]

Constructing high-precision subsidence basins is of paramount importance for mining subsidence monitoring. Traditional unmanned aerial vehicle (UAV) photogrammetry techniques typically construct subsidence basins by directly differencing digital elevation models (DEMs) from different monitoring periods. However, this method often neglects the influence of horizontal displacement on the accuracy of the subsidence basin. Taking a mining area in Ordos, Inner Mongolia, as an example, this study employed the normalized cross-correlation (NCC) matching algorithm to extract horizontal displacement information between two epochs of a digital orthophoto map (DOM) and subsequently corrected the horizontal position of the second-epoch DEM. This ensured that the planar positions of ground feature points remained consistent in the DEM before and after subsidence. Based on this, the vertical displacement in the subsidence area (the subsidence basin) was obtained via DEM differencing, and the parameters of the post-correction subsidence basin were inverted using the probability integral method (PIM). The experimental results indicate that (1) the horizontal displacement was influenced by the gully topography, causing the displacement within the working face to be segmented on both sides of the gully; (2) the influence of the terrain on the subsidence basin was significantly reduced after correction; (3) the post-correction surface subsidence curve was smoother than the pre-correction curve, with abrupt error effects markedly diminished; (4) the accuracy of the post-correction subsidence basin increased by 43.12% compared with the total station data; and (5) comparing the measured horizontal displacement curve with that derived using the probability integral method revealed that the horizontal displacement on the side of an old goaf adjacent to the newly excavated working face shifted toward the advancing direction of the new working face as mining progressed. This study provides a novel approach and insights for using low-cost UAVs to construct high-precision subsidence basins. [ABSTRACT FROM AUTHOR]

Coal mining-induced ground subsidence is a severe hazard that can damage property, infrastructure, and the environment in the vicinity when the deformation is not negligible. The boundary of a mining-induced subsidence-affected zone refers to the area beyond which the ground subsidence is less concerned. Accurately measuring mining-induced ground deformation is essential for delineating the irregular boundary of the impacted area. This study employs multitemporal interferometric synthetic aperture radar (MT-InSAR) techniques, including differential InSAR (DInSAR), InSAR stacking, and interferometric point target analysis (IPTA), to analyze coal mine subsidence and delineate the boundaries of the mining-impacted zones. DInSAR accurately reconstructs, locates, and detects the trend in mining-induced subsidence and correlates well with documented mining operations. The InSAR stacking method maps the spatial variation of the ground's average line-of-sight (LOS) velocity over the mining area, delineating the boundary of the impacted zone. IPTA analysis combining multilook and single-pixel phases achieves millimeter-level surface measurement above tunnel alignments and measures unevenly distributed deformation fields. This study considers an average of 4 cm per year of surface deformation in the LOS direction as the subsidence threshold value for delineating the boundary of the mining-induced subsidence-affected (MISA) zone during the active coal mining stage. Interestingly, there are twin transportation tunnels near the mining area. The twin tunnels completed before the coal mining activities started were functioning well, but damage was observed after the mining began. Our study reveals the tunnels are located within the InSAR-derived MISA zone, although the tunnels approach the MISA boundary. As direct signs of subsidence, ground fissures have been identified near the tunnels via field investigations and UAV photogrammetry. Furthermore, the derived distribution of ground fissures validates and verifies InSAR measurements. The integrated approach of MT-InSAR, UVA photogrammetry, and field investigation developed in this study can be applied to delineate the irregular boundary of the MISA zone and study the accumulating effects of mining-induced subsidence on the performance of infrastructure in areas proximate to coal mining activities. [ABSTRACT FROM AUTHOR]

The Silesian Upland in southern Poland is known as a place where subsidence processes induced by mining activities occur in an area of nearly 1500 square kilometres, with many water bodies that formed in subsidence basins. This study concerned the dynamics of changes in the occurrence, boundaries and area of water bodies in subsidence basins (using orthoimagery from 1996 to 2023), as well as the assessment of the factors underlying the morphogenetic and hydrogenetic transformations of these basins. Within the subsidence basins covered by the study, water bodies occupied a total area that changed from 9.22 hectares in 1996 to 48.43 hectares in 2003, with a maximum of 52.30 hectares in 2009. The obtained figures testify to the extremely dynamic changes taking place in subsidence basins, which are unprecedented within such short time intervals in the case of other morphogenetic types of lakes and anthropogenic water bodies (for instance, from 1996 to 2003, the basin of the Brantka water body in Bytom underwent a more than two-fold change in its area, with RA values in the range of 54.4% to 131.9). A reflection of the dynamics of short-term changes in the water bodies in question in the period from 1996 to 2023 is the increase in the water area of the three studied water bodies, which was projected by linear regression to range from 0.09 hectares/year to 0.56 hectares/year. The area change trends, as determined by polynomial regression, suggest a slight decrease in the water table within the last few years, as well as within the next few years, for each of the studied basins. [ABSTRACT FROM AUTHOR]

The subsidence caused by coal mining could cause the destruction of roads and houses, and even the failure of infrastructures. Understanding of the mechanism of coal mining subsidence may provide early protecting to infrastructures on coming failure, but dynamic analysis of subsidence due to coal mining is currently needed. In this study we apply particle image velocimetry (PIV) method to reveal strata movement and subsidence according to the prototype and indoor physical model similarity experiment of Henan. Our result shows magnitude of the subsidence of overlying strata during the coal mining at different excavation thickness, that more coal mining thickness may produce more subsidence, and that shallower coal may cause more significant subsidence. Our result suggests that further PIV test combined with field monitoring data may be an effective measure to study subsidence mechanism and pattern helping to predict disaster caused by subsidence. [ABSTRACT FROM AUTHOR]

Accurate monitoring of mining three-dimensional deformation plays an important role in analyzing the subsidence mechanism and assessing potential geological hazards in the mining area. This paper combines TerraSAR-X and RADARSAT-2 heterogeneous SAR images employing MSBAS technology to obtain the three-dimensional time-series of deformation of the 132 610 working face in the Fengfeng mine from December 2015 to February 2016. The monitoring results show that the maximum surface subsidence and the subsidence rate during the monitoring period were –243 mm and 4.42 mm/day, and the maximum east-west horizontal movement was –80~30 mm. The surface subsidence basin is in the active stage, which is prone to pose a threat to the safety of the surrounding structures. Comparison with the leveling data shows that the root-mean-square deviation of the vertical deformation obtained by MSBAS technology is 10.7 mm, and the accuracy can reach the centimeter level. Meanwhile, based on the vertical deformation obtained by MSBAS technology, combined with the proportionality relationship between horizontal movement and tilt, the north-south horizontal movement was estimated as –80~70 mm, which provided a solution for obtaining the north-south horizontal movement of the mining area by InSAR. Analysis of the three-dimensional time-series of the surface deformation at the main sections shows that the surface deformation features during the mining process of the 132610 working face match well with the general model of mining subsidence, but the working face inclination angle has an influence on the surface deformation. [ABSTRACT FROM AUTHOR]

Monitoring three-dimensional (3D) deformation in underground mining areas is crucial for the prevention and control of mining-induced disasters. Differential interferometric synthetic aperture radar (D-InSAR) is limited to detecting one-dimensional (1D) deformation along the line of sight (LOS). This paper proposes a new method for extracting 3D mining-induced deformation based on the differentiability of D-InSAR LOS deformation fields. The method approximates the D-InSAR LOS deformation field in underground mining areas as a differentiable function and constructs a 3D deformation extraction model utilizing directional derivatives of this function. The least squares method is used for estimating and evaluating the 3D deformation. Simulation and real data experiments have been used to verify the feasibility of the method in extracting mining-induced 3D deformation. The simulation results show relative root mean square errors (RRMSES) of 1.24%, 6.05%, 0.97%, and 11.47% for vertical and horizontal displacements along the east–west and south–north directions, respectively. The real data experiments using Sentinel-1 images show that the root mean square errors (RMSES) of the up–down, south–north, and east–west directions are 14.06 mm, 7.37 mm, and 11.56 mm, respectively. Experimental results show that the method can provide a certain basis for 3D surface deformation monitoring of mining subsidence. [ABSTRACT FROM AUTHOR]

The exploitation of coal resources has caused problems such as ground deformation, affecting the ecological environment. Spatiotemporal varying characteristics between land subsidence and ecological environmental quality (EEQ) are an important research hotspot. Using the SBAS-InSAR method, 64 Sentinel-1 images were utilized to monitor land subsidence in the Shenfu mining area, one of China's largest coal source regions. And the remote sensing ecological index (RSEI) was used to monitor and evaluate EEQ of the Shenfu mining area. Global and local spatial autocorrelation methods were used to assess the spatial aggregation degree and change patterns over time. Spatial Econometric Models were employed to explore the impacts of land subsidence on EEQ. The results showed the following: (1) The average RSEI values in the Shenfu mining area were 0.531, 0.488, and 0.523 in 2016, 2018, and 2020, respectively; there was a slight downward trend in EEQ. The permanent scatter (PS) point deformation rate ranged from −353.40 mm/year to +246.24 mm/year, with average deformation rates of 0.1642, 0.2181, and 0.2490 mm/year, respectively. (2) There was a significant correlation and spatial agglomeration effect between land surface subsidence and EEQ. Low–high, high–low, and low–low clusters were the main types of relationships, indicating that land subsidence primarily has a negative spatial impact on the ecological environment. (3) The relationship between land subsidence and EEQ varied spatially in the Shenfu mining area at 500 × 500 grid units. This research can provide scientific guidance for disaster prevention and sustainable development in mining areas by considering long-term differences in ecological environmental quality and its correlation with land subsidence. [ABSTRACT FROM AUTHOR]

The destruction of arable land caused by coal mining in coal grain compound areas is a major bottleneck restricting grain production increase. The spatiotemporal correlation between the decline in cultivated land quality and crop growth deterioration due to mining subsidence still needs to be clarified. This study employed the CDR AVHRR NDVI dataset and applied correlation and trend analysis methods to extract vegetation cover information from 1982 to 2022. It also explored the relationships between vegetation cover and temperature and precipitation. The study found the following: (1) Over the past 41 years, the NDVI in the study area showed a significant upward trend. Specifically, the average annual NDVI growth rate in the mining area was 51.85%, while the corresponding growth rates for the 10 km buffer area, 20 km buffer area, and check area (CK) were 65.91%, 65.86%, and 68.09%, respectively. The start of the growing season (SOS) for winter wheat in the mining area and control area advanced by 49 ± 1.5 days and 65 ± 1.5 days, respectively, while the length of the growing season (LOS) extended by 59 ± 1.5 days and 72 ± 1.5 days, respectively. For summer maize, the SOS advanced by 11 ± 1.5 days and 15 ± 1.5 days, respectively, and the LOS extended by 17 ± 1.5 days and 19 ± 1.5 days, respectively. The study area exhibited a significant positive correlation between the NDVI and temperature. Specifically, the correlation coefficient for the mining area was 0.6865 (p < 0.01); for the 10 km buffer zone, it was 0.5937 (p < 0.01), for the 20 km buffer zone, it was 0.6775 (p < 0.01), and for the control check area (CK), it was 0.6591 (p < 0.01). The results of this study can provide data support for the collaborative rehabilitation of and source reduction in coal grain compound areas, as well as for the restoration of damaged farmland. [ABSTRACT FROM AUTHOR]

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Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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The extraction of underground mineral deposits can cause surface subsidence, which will bring danger to surface buildings or structures. In this paper, the Panlong lead–zinc mine in Guangxi, China, was taken as an example to analyze the correlation between underground mining‐induced surface subsidence trends, and the depth of extraction. Based on the stochastic probability integral method, the surface subsidence trend at various different mining depths was analyzed. The results showed that with the increase of mining depth, the surface subsidence presents an obvious increasing trend, but the rate gradually decreases. The maximum surface subsidence is closely related to the mining depth and is less notably affected by the mining size. The maximum surface subsidence along the dip direction is basically the same as that along the strike direction for the same mining depth of the same ore body. In addition, numerical simulation by FLAC3D was conducted to analyze the evolution trend of surface subsidence at different mining depths. The subsidence cloud map demonstrated a significant expansion of the subsidence area until a depth of 680 m. With the further increase of mining depth, the subsidence area did not substantially increased, while the maximum subsidence at the center of the subsidence zone was increasing. Finally, the maximum subsidence at the center of the subsidence zone was measured by Global Positioning System (GPS) monitoring instruments. The observed data showed that subsidence trend along the direction of the mineral body was basically consistent with the theoretical and numerical simulation results above. This verifies the reliability and accuracy of the above research methods for analyzing surface settlement trend. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) oblique photogrammetry has been extensively employed in mining, albeit predominantly for reconstructing three-dimensional scenes and detecting changes within mining sites, lacking predictive capabilities. Leveraging 3D real scene model data, this study presents a two-stage prediction model, merging the probabilistic integral method with recurrent neural network (PIMF-RNN), to mitigate the impact of internal and external factors on surface subsidence, thereby enhancing predictive accuracy. Building upon this framework, a methodology was developed to forecast the maximum surface subsidence height and affected area under the block caving method, offering crucial data support for mitigating hazards associated with this mining technique. Analysis of surface data from Pulang copper mine during 2018–2020 demonstrates a prediction accuracy of 91.47% for maximum surface subsidence height and 87.52% for subsidence area. This research expands the potential applications of UAV oblique photogrammetry techniques within mining contexts. Furthermore, it establishes a cost-effective and efficient operational procedure for predicting mine surface subsidence. [ABSTRACT FROM AUTHOR]

Ground subsidence caused by underground coalmining result in the formation of ponding water on the ground surface. Monitoring the surface water level is crucial for studying the hydrologic cycle in mining areas. In this paper, we propose a combined technique using Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) and GNSS Interferometric Reflectometry (GNSS-IR) to estimate the surface water level in areas of ground subsidence caused by underground coal mining. GNSS RTK is used to measure the geodetic height of the GNSS antenna, which is then converted into the normal height using the local height anomaly model. GNSS-IR is employed to estimate the height from the water surface to the GNSS antenna (or, the reflector height). To enhance the accuracy of the reflector height estimation, a weighted average model has been developed. This model is based on the coefficient of determination of the signal fitted by the Lomb-Scargle spectrogram and can be utilized to combine the reflector height estimations derived from multiple GNSS system and band reflection signals. By subtracting the GNSS-IR reflector height from the GNSS RTK-based normal height, the proposed method-based surface water level estimation can be obtained. In an experimental campaign, a low-cost GNSS receiver was utilized for the collection of dual-frequency observations over a period of 60 days. The collected GNSS observations were used to test the method presented in this paper. The experimental campaign demonstrates a good agreement between the surface water level estimations derived from the method presented in this paper and the reference observations. [ABSTRACT FROM AUTHOR]

The adverse combination of excessive mining practices and the resulting land subsidence is a significant obstacle to the sustainable growth and stability of regions associated with mining activities. The Lakhra coal mines, which contain some of Pakistan's largest coal deposits, have been overlooked in land subsidence monitoring, indicating a considerable oversight in the region. Subsidence in mining areas can be spotted early when using Interferometric Synthetic Aperture Radar (InSAR), which can precisely monitor ground changes over time. This study is the first to employ the Small Baseline Subset (SBAS)-InSAR and stacking-InSAR techniques to identify land subsidence at the Lakhra coal mines. This research offers critical insights into subsidence mechanisms in the study area, which has never been previously investigated for ground deformation monitoring, by utilizing 150 Sentinel-1A (ascending) images obtained between January 2018 and September 2023. A total of 102 deformation spots were identified using SBAS-InSAR, while stacking-InSAR detected 73 deformation locations. The most extensive cumulative subsidence in the Lakhra coal mine was −114 mm, according to SBAS-InSAR, with a standard deviation of 6.63 mm. In comparison, a subsidence rate of −19 mm/year was reported using stacking-InSAR with a standard deviation of 1.17 mm/year. The rangeland covered 88.8% of the total area and exhibited the most significant deformation values, as determined by stacking and SBAS-InSAR techniques. Linear regression showed that there was not a strong correlation between subsidence and topographic factors. As detected by optical remote sensing data, the subsidence locations were near or above the mines in the research area, indicating that widespread mining in Lakhra coal mines was the cause of subsidence. Our findings suggest that SAR interferometric time series analysis is helpful for proactively identifying and controlling subsidence difficulties in mining regions by closely monitoring activities, hence reducing negative consequences on operations and the environment. [ABSTRACT FROM AUTHOR]

Since the 1970s, extensive halite extraction in Maceió, Brazil, has resulted in significant geological risks, including ground collapses, sinkholes, and infrastructure damage. These risks became particularly evident in 2018, following an earthquake, which prompted the cessation of mining activities in 2019. This study investigates subsidence deformation resulting from these mining operations, focusing on the collapse of Mine 18 on 10 December 2023. We utilized the Quasi-Persistent Scatterer Interferometric Synthetic Aperture Radar (QPS-InSAR) technique to analyze a dataset of 145 Sentinel-1A images acquired between June 2019 and April 2024. Our approach enabled the analysis of cumulative displacement, the loss of amplitude stability, the evolution of amplitude time series, and the amplitude change matrix of targets near Mine 18. The study introduces an innovative QPS-InSAR approach that integrates phase and amplitude information using amplitude time series to assess the lifecycle of radar scattering targets throughout the monitoring period. This method allows for effective change detection following sudden events, enabling the identification of affected areas. Our findings indicate a maximum cumulative displacement of −1750 mm, with significant amplitude changes detected between late November and early December 2023, coinciding with the mine collapse. This research provides a comprehensive assessment of deformation trends and ground stability in the affected mining areas, providing valuable insights for future monitoring and risk mitigation efforts. [ABSTRACT FROM AUTHOR]

The fluid–solid coupling effect is an important factor which cannot be ignored to study the surface subsidence of deep coal seam mining in the area with abundant underground water. To study the influence of boundary permeability characteristics on surface subsidence in deep near‐horizontal coal seam mining under the effect of fluid–solid coupling, the pore water pressure field, vertical stress field, surface subsidence, and vertical displacement of a rock seam during deep mining under different permeability boundary conditions were analyzed based on fluid–solid coupling theory, taking the 3−1501 working face of Erdos Hongqinghe coal mine as an example. The results revealed that the permeability characteristics of different hydraulic boundaries affected the pore water pressure, vertical stress in the rock layer, and surface subsidence during deep mining. Moreover, the trends of pore water pressure, vertical stress, and surface subsidence of a fixed‐head permeability boundary were largely the same as those under impermeable boundary conditions, but the calculated results of the fixed‐head permeability boundary were lower than those for the impermeable boundary at the same depth. The maximum surface subsidence for the fixed‐head permeable boundary condition and the impermeable boundary condition was 879 and 925 mm, respectively, which were 239 and 285 mm higher than those obtained when the fluid–solid coupling effect was neglected (an increase of 37.3% and 44.5%, respectively). The field monitoring trend for the subsidence basin aligns closely with the subsidence basin trend under the influence of fluid–solid coupling. Considering the constant head permeable boundary conditions in the context of fluid–solid coupling yields accurate surface subsidence results. The results have provided a theoretical basis for the analysis and prediction of coal mining surface subsidence considering the fluid–solid coupling effect. [ABSTRACT FROM AUTHOR]

Accurately monitoring the change of mine area in the mining process is beneficial to mine safety management. This paper briefly introduces the collection of remote sensing images by unmanned aerial vehicles (UAVs) and its application in measuring surface mining subsidence and surrounding vegetation in the mining area. A case study was carried out in some mining areas of Nanshan Mountain, Yuncheng City, Shanxi Province. The surface mining subsidence value and vegetation-related parameters were measured by comparing the digital elevation model and multi-spectral images collected on May 12 and June 12, 2023. The validity experiment verified that the UAV image data could be used to measure the mining subsidence and vegetation parameters. Moreover, it was found that mining underground coal could lead to significant ground subsidence and pollute the surrounding environment, reducing vegetation. The innovation of this article lies in using UAV-collected remote sensing images instead of manually collecting ground elevation data and vegetation distribution data, providing effective references for safe mining in mining areas. [ABSTRACT FROM AUTHOR]

Borehole-to-surface electrical (BTSE) technology plays an important role in detecting mining subsidence area in coalfields. However, the ambiguity of linear inversion and the influence of surrounding rock resistivity reduced the effectiveness and accuracy of BTSE technology in identifying mining subsidence area. In this study, the damped least-squares inversion method constrained by inequality was investigated based on the basic principles of BTSE technology. On this basis, a novel method using the ratio of post- to pre-mining inversion resistivity was proposed to detect mining subsidence area. Finally, the application effect of this method was analyzed by using theoretical model and laboratory mining model, respectively. It was found that the resistivity inversion of the BTSE constrained by inequality could obtain good layered information of the stratum provided by prior information. Using the ratio of post- to pre-mining inversion resistivity can eliminate the influence of surrounding rock on the abnormal resistivity and highlight the resistivity anomalies caused by stratum collapse during coal mining. The application results of the theoretical model and laboratory mining model illustrated the feasibility and effectiveness of this novel method in detecting mining subsidence area, which provides important theoretical basis and reference for its future promotion and application. [ABSTRACT FROM AUTHOR]

Surface subsidence resulting from block caving mining causes considerable environmental and economic harm in mining areas, highlighting the critical need for accurate predictions of surface subsidence. Given the unique features of the block caving technique and the resemblance between the released ore pillars and the mining processes, this paper developed a lightweight model to forecast surface settlement utilizing the probability integration approach to address the issue of predicting surface settlement in metallic mines. This study focuses on the Pulang Copper Mine, situated in the northeast of Shangri-La County within the Yunnan Province, as a case example. This mine employs the block caving method, which results in substantial surface subsidence. A visual mining simulation program is designed to combine the ore mining plan with the prediction model, manage the ore output of each mining point in batches, treat the ore pillars released in the planning cycle as strip work, and simulate and calculate the surface area above the ore pillars settlement value. The calculated values of surface subsidence induced by ore drawing are then interpreted as the downward displacement of the surface subsidence beneath the strip workings. Furthermore, to verify the reliability of the model, three-dimensional laser point cloud data of the Pulang Copper Mine in recent years were collected, and the differences between the predicted surface and the measured surface were calculated and analyzed. [ABSTRACT FROM AUTHOR]

The Inner Mongolia mining area in western China are characterized by the development of numerous penetrating fissures, resulting in severe land damage. It is significant to reveal the underlying evolution mechanism and identify treatment timing for restoring the ecological environment. The Guanbanwusu mining subsidence area in Inner Mongolia, China was selected as the research case for this study. The evolution mechanism of different penetrating fissures was revealed by field measurement, physical simulation and theoretical analysis. The treatment timing prediction model for the mining subsidence area was established based on the enhanced Weibull time function. The results show that the ground fissures are mainly step-type and collapse-type fissures. The breaking form of overlying strata determines their vertical opening and horizontal dislocation. The high mining intensity in the western mining area results in a shortened period of dynamic fissure expansion and reduced closure degree. The damage extent of the overlying strata exhibits zoning characteristics both vertically and horizontally. The relative standard deviation of the prediction model is only 3.7%. Concurrently, the prediction model is employed to determine the optimal timing for treatment in the study area, estimated to be 259 days. Subsequently, once this threshold is reached, the study area undergoes treatment and restoration of its e cological environment. This study addresses the knowledge gap in this field by highlighting the interconnectedness between rock strata structure and evolution mechanism of penetrating fissures, thereby providing a method for determining the treatment timing in mining subsidence areas. [ABSTRACT FROM AUTHOR]

Subsidence is one of the main environmental impacts of underground mining worldwide. Besides, the increasing complexity of underground mining due to greater depths and interaction with inhabited and environmentally sensitive areas can lead to challenges that may threaten the viability of mining due to phenomena such as subsidence. This research aims to increase the knowledge about surface subsidence due to underground mining, characterising the main factors that rule mining subsidence utilising an actual mine that extracts potash. The calculation methodology was based on 74 sections of the subsidence basin, using GPS measurements and the InSAR technique, with data collected over twelve nonconsecutive years from 1995 to 2021. Thus, three different active areas and one residual area were determined. Average boundary angles and their average distances of influence for the active regions have also been determined. Furthermore, using the least squares method, the subsidence basin curve was defined using a Gaussian function. The algorithm that governs the subsidence process has been successfully calculated, allowing the approximation of the deformation of any point within an area of interest. The novelty of this paper is twofold: the results obtained provide a detailed subsidence behaviour and a prediction model of the case study. Furthermore, the methodology implemented can be applied to other subsidence basins with mines in their area of influence. Hence improving the surface mining area's safety levels and managing the environmental impacts. [ABSTRACT FROM AUTHOR]

Shuozhou is a typical coal mining city, and the Pingshuo Antaibao open-pit coal mine in its area is one of the largest open-pit coal mines in China. The mining of coal resources is an important part of ensuring national energy security, and at the same time, it inevitably has a certain impact on the ecology, such as coal dust generated by open-pit mining will affect air quality, soil, water and vegetation. It is of great significance to explore the temporal and spatial variation of ecological environment quality in coal mining cities for ecological protection and sustainable social and economic development. Based on the Google Earth Engine (GEE) platform, this paper combines the index-based coal dust index (ICDI) and Remote Sensing Ecological Index (RSEI) models to construct an improved RSEI (IRSEI) that can reflect coal mining cities. This paper explores the spatial–temporal evolution characteristics and spatial correlation of ecological environment quality in Shuozhou from 2000 to 2020. The results showed that the average value of IRSEI in Shuozhou was between 0.262 and 0.418, and the overall change showed an upward trend. The growth areas of ecological environment quality are mainly located in the eastern and southwestern areas with good vegetation growth, and these regions have vigorously implemented the Northern Shelter Forest Project, afforestation and greening projects, implemented the forest resource management and protection responsibility system, promoted the construction of ecological civilization, and significantly improved the ecological environment. While the declining areas are mainly located in the central and southern regions where mining activities and human activities are more intensive. The IRSEI in the study area showed a significant spatial positive correlation, and the agglomeration types of the spatial pattern were mainly high-high and low-low agglomeration types, with the high-high agglomeration types mainly distributed in the eastern and southwestern regions, and the low-low agglomeration types distributed in the northern and south-central regions of the study area. The trend of low and low agglomeration has decreased, which further proves that the ecological restoration measures taken by the government, such as returning farmland to forests, integrating protection and restoration of mountains, waters, forests, fields, lakes, grasslands, and sands, controlling soil erosion, and stage wise reclamation of coal mining subsidence areas, have improved the ecological environment quality of Shuozhou. This study provides a reference for understanding the spatiotemporal changes of the ecological environment of coal mining cities, and is conducive to formulating appropriate ecological protection strategies. [ABSTRACT FROM AUTHOR]