利用光学卫星遥感数据监测抚顺地区煤矿开采引起的地貌变化

抚顺市是中国重要的采煤城市之一,100多年的煤矿开采已经使抚顺地区的地貌特征发生了重大变化。由于长期的露天开采,抚顺西露天矿形成了一个海拔约为-300m的深坑;煤矿的地下开采造成了大面积的地表沉陷;与此同时,煤矸石的露天堆积形成了3个排土场。本研究利用多时相的美国Landsat MSS（Multispectral Scanner）,TM（Thematic Mapper）,ETM⁺（Enhanced Thematic Mapper Plus）,以及日本ASTER（Advanced Spaceborne Thermal Emission and Reflection Radiometer）卫星遥感数据分析过去近30年来抚顺地区煤矿开采所引起的地貌变化。多时相卫星遥感图像的对比分析显示,由于地面沉陷引起的地表积水面积在近5年（2001～2006年）内出现明显增大的趋势,增加了1.73km。同期的ASTER DEM数据对比分析还发现,西露天矿的开采深度在不断增加,最大增加量为55m,与此同时出现的新增排土场造成地面高程增加量最大为25m。这一研究表明利用遥感技术可以定量监测人类矿产开发活动所引起的地貌特征变化及其过程。     

基于四旋翼无人机的宁夏羊场湾煤矿采煤沉陷量监测

小型无人机遥感技术具有成本低、操作灵活便利等优点,在地质调查中的作用愈来愈重要。采煤地表沉陷量变形监测是掌控采煤地表岩移变形规律和治理塌陷的关键性工作。重点探索四旋翼无人机遥感技术监测在羊场湾煤矿Y120212工作面采煤沉陷量的监测研究,通过野外踏勘与控制点布设、无人机航线规划与执行、4D产品制作的工作程序和监测方法,探索无人机遥感技术监测在矿山地质塌陷监测的应用。研究结果表明,通过对无人机遥感技术生成的DSM处理,经过多期地面高程的对比,得到Y120212工作面最大沉陷量达6.5m。结合分析、对比,无人机遥感技术可以实现采煤塌陷区地表沉陷变形监测,进而形成和发展了煤矿地面塌陷新的监测技术。     

近十年淮南矿区地表热环境时空演变分析

地表温度是描述陆表过程和反映地表特征的重要参数。研究矿区地表温度的时空演变特征,对于理解煤矿开采活动对矿区生态环境的影响具有重要意义。淮南矿区是我国东部典型的高潜水位煤矿区,为了探究该区域地表温度的时空异质性与开采扰动的关系,利用2008—2018年5期Landsat影像进行地表温度的反演和下垫面信息提取,结合热力景观格局指数和城市热岛比例指数的计算,分析近十年来淮南矿区地表热环境的时空演变特征。结果表明:淮南矿区地表温度分布格局及其变化与下垫面结构及其变化密切相关。2008—2018年间,淮南矿区地表温度总体以中温区为主,其次是次低温和次高温区,高温区和低温区面积及其占比相对较低。由于城镇和矿井建设,以及煤矿开采产生的煤矸石堆积和开采沉陷积水,建设用地和水域面积不断增加,植被面积不断减少,相应的低温区、次高温和高温区增长,中温和次低温区减少,总体上城市热岛比例指数不断增加,热岛效应不断加剧;低温区斑块破碎度和复杂度不断减小,优势度不断增大。其他温度级斑块破碎度和复杂度,景观团聚程度和连通性,多样性和均匀度变化分为2008—2013、2013—2015和2015—2018三个阶段,其中2013—2015年人类干扰强度较大,斑块破碎度和复杂度增大,景观团聚程度和连通性,多样性和均匀度减小。研究成果为淮南矿区生态环境的恢复治理和城市产业规划提供了科学依据。      

马坪岭煤矿地面岩溶塌陷分析

与马坪岭煤矿毗邻的某军工企业厂区附近发生地面塌陷,严重影响了企业的正常生产。为此需对马坪岭煤矿地面岩溶塌陷进行分析。通过收集资料与实地调查,认为地面塌陷是由于附近煤矿、老窑开采引发突水,长期疏干排水以及大量的抽取地下水造成的。为了以后能安全顺利开采,以-50m标高为界,根据老窑分布、地层岩性、未来采空区的涌水量,将开采区划分为水文地质条件复杂区和中等区。其中预测复杂区最大涌水量为1 792m3/h,易引发地面塌陷,目前不宜开采;中等区最大涌水量253m3/h,不易引发地面塌陷。并根据矿区构造特点,提出了在断层附近采取帷幕注浆方式,以减少断层导水性的预防措施。     

陕北煤矿区黄土丘陵地貌景观规划模式设计与布局

陕北煤矿区是我国重要的产煤基地,煤炭大量开采后会引起地表沉陷和景观扰动。以陕北柠条塔矿沉陷区为例,针对该区域内黄土丘陵地貌特征,通过室内统计分析、现场调研、定性定量相结合等方法综合分析地表景观存在的问题,并进行景观模式规划设计与布局,为此地貌类型中地下开采矿区景观规划提供借鉴。结果发现,地上景观存在4个问题:自然景观碎片化,生态环境缺乏整体布局;煤炭地下开采动态化,地上景观规划缺乏联动机制;土地资源利用低效化,景观生态?产业经济缺乏有机链接;环境土质干旱瘠薄化,景观再造与技术支持缺乏联系。针对这些问题构建坡度坡向分级为基础的区划规划模式,“三期两带”的时空规划模式,以复合种植模式为主的植物组合规划模式,以微生物菌肥、无人机飞播、矿井水再利用为主的技术规划模式,并进行了生态–经济–社会的综合效益分析,为煤矿区的后续生存发展、生态经济的升级转型奠定基础。      

Research and application of roadway backfill coal mining technology in western coal mining area

In China’s western coal mining area, the traditional room mining technology is facing coal pillar instability, mine earthquake, large-area roof subsidence in the goaf, surface subsidence, water and soil loss, vegetation deterioration, and other environmental problems. To solve the aforementioned problems and to improve coal recovery, the roadway backfill coal mining (RBCM) method was proposed as a solution and its technical principle and key equipment were presented in this paper. In addition, the microstructure and mechanical behavior (strain-stress relation in confined compressive test) of aeolian sand and loess backfill materials were studied for a rational backfill design for underground mines. Further, coal pillar stress, plastic zone change, and surface deformation of the RBCM schemes were studied using the FLAC^3D numerical simulation software, and a reasonable mining scheme of “mining 7 m and leaving 3 m” was determined. The engineering application in Changxing Coal Mine shows that the RBCM method with loess and aeolian sand as backfill materials allows a stable recovery of coal pillars with a recovery ratio of more than 70 %. The maximum accumulated surface subsidence and the maximum horizontal deformation were measured to be 15 mm and 0.8 mm/m respectively, indicating that the targeted backfilling effect can help protect the environment and also control surface subsidence.     

Unique documentation,analysis of origin and development of an undrained depression in a subsidence basin caused by underground coal mining (Kozinec,Czech Republic)

This article aims to explain and demonstrate the origin and development of a subsidence basin caused by coal mining as well as to point out important aspects of this phenomenon in engineering geology. Engineering geology needs to deal with a number of issues related to the origin and development of subsidence basins in areas affected by deep coal mining. An interesting case study from the Upper-Silesian Basin in the northeast of the Czech Republic near the Polish border is presented in this paper. There is a clear time chronological succession in the ground surface changes manifested by a ground subsidence gradation, both in their absolute values as well as in their spatial distribution. The phenomenon is documented by aerial photo time series, which optimally depict the origin and development of the subsidence. In the study area, there are changes in the landscape elements and it is essential to be considered in future land use plans. The marginal conditions of the Quaternary geological structure and hydrogeological conditions are responsible for an unconfined aquifer which manifests there as a water body in an undrained depression in the course of the ground subsidence.     

GPS/terrestrial 3D laser scanner combined monitoring technology for coal mining subsidence: a case study of a coal mining area in Hebei, China

In order to overcome the shortage that point-based data acquisition techniques cannot retrieve the whole basin subsidence caused by underground mining, and to avoid complex splicing of terrestrial 3D laser scanner (TLS) point cloud data and the errors caused by such splicing, GPS/TLS combined technology is employed for mining subsidence monitoring. The basic idea of the monitoring technology is put forward. In this article, an application of the method to a coal mining area in China is presented. Support vector machine (SVM) model for GPS level conversion in the mining area is established, and a comparative analysis of SVM, BP neural network and polynomial established local quasi-geoid in the mining area is conducted. Ground surface digital elevation model (DEM) of the mining area is established by using TLS point cloud data, and the ground surface dynamic subsidence basin is obtained through a subtraction of two DEMs. The results indicate that the quasi-geoid established by using SVM model features a relatively high level of stability and accuracy and that the established mining surface DEM and subsidence basin can provide the fundamental data for the reconstruction of ecological environment in the mining area. GPS/TLS combined monitoring technology is a new monitoring technology, which entangles the advantages of both GPS and TLS and could offset their disadvantages, thus obtaining complementary advantages. According to analysis on its application in the mining area, we conclude that the technology is feasible and has a great application prospect for the mining area purposes.     

Integrated method of RS and GPR for monitoring the changes in the soil moisture and groundwater environment due to underground coal mining

Mining affects the environment in different ways depending on the physical context in which the mining occurs. In mining areas with an arid environment, mining affects plants’ growth by changing the amount of available water. This paper discusses the effects of mining on two important determinants of plant growth—soil moisture and groundwater table (GWT)—which were investigated using an integrated approach involving a field sampling investigation with remote sensing (RS) and ground-penetrating radar (GPR). To calculate and map the distribution of soil moisture for a target area, we initially analyzed four models for regression analysis between soil moisture and apparent thermal inertia and finally selected a linear model for modeling the soil moisture at a depth 10 cm; the relative error of the modeled soil moisture was about 6.3% and correlation coefficient 0.7794. A comparison of mined and unmined areas based on the results of limited field sampling tests or RS monitoring of Landsat 5-thermatic mapping (TM) data indicated that soil moisture did not undergo remarkable changes following mining. This result indicates that mining does not have an effect on soil moisture in the Shendong coal mining area. The coverage of vegetation in 2005 was compared with that in 1995 by means of the normalized difference vegetation index (NDVI) deduced from TM data, and the results showed that the coverage of vegetation in Shendong coal mining area has improved greatly since 1995 because of policy input RMB￥0.4 per ton coal production by Shendong Coal Mining Company. The factor most affected by coal mining was GWT, which dropped from a depth of 35.41 m before mining to a depth of 43.38 m after mining at the Bulianta Coal Mine based on water well measurements. Ground-penetrating radar at frequencies of 25 and 50 MHz revealed that the deepest GWT was at about 43.4 m. There was a weak water linkage between the unsaturated zone and groundwater, and the decline of water table primarily resulted from the well pumping for mining safety rather than the movement of cracking strata. This result is in agreement with the measurements of the water wells. The roots of nine typical plants in the study area were investigated. Populus was found to have the deepest root system with a depth of about 26 m. Based on an assessment of plant growth demands and the effect of mining on environmental factors, we concluded that mining will have less of an effect on plant growth at those sites where the primary GWT depth before mining was deep enough to be unavailable to plants. If the primary GWT was available for plant growth before mining, especially to those plants with deeper roots, mining will have a significant effect on the growth of plants and the mechanism of this effect will include the loss of water to roots and damage to the root system.     

Prediction of ground subsidence in Samcheok City,Korea using artificial neural networks and GIS

This study shows the construction of a hazard map for presumptive ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using an artificial neural network, with a geographic information system (GIS). To evaluate the factors governing ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, global positioning system (GPS) data, land use map, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed by employing field investigations and reinforcement working reports for the existing ground subsidence areas at the study site. Seven major factors controlling ground subsidence were determined from the probability analysis of the existing ground subsidence area. Depth of drift from the mining tunnel map, DEM and slope gradient obtained from the topographical map, groundwater level and permeability from borehole data, geology and land use. These factors were employed by with artificial neural networks to analyze ground subsidence hazard. Each factor’s weight was determined by the back-propagation training method. Then the ground subsidence hazard indices were calculated using the trained back-propagation weights, and the ground subsidence hazard map was created by GIS. Ground subsidence locations were used to verify results of the ground subsidence hazard map and the verification results showed 96.06% accuracy. The verification results exhibited sufficient agreement between the presumptive hazard map and the existing data on ground subsidence area. An erratum to this article can be found at     

Model Establishment for Ponding in Coal Mining Subsidence Areas and its Prediction: Case Study of Northern Jining,China

Mining in areas with a high phreatic water surface leads to groundwater exposure and the accumulation of the water in the subsidence basin. The calculation of the volume of water is of high importance. This paper analyzes the seasonal or perennial ponding caused by mining in areas with high phreatic water surfaces. With the help of digital elevation model, the estimated groundwater elevation, and the basis of a mining subsidence prediction model, a model of ponding in subsidence areas has been established. Using this model, the amount of mining subsidence ponding in northern Jining has been calculated. Furthermore, the evolution of ponding has been forecasted. Our experimental results indicate that the average depth of the ponding area is projected to be about 3 m after mine closure. The volume of the new perennial ponding is 0.2–2 million m³ per year and it is negligible to the river confluence in Jining. This model helps in the calculation of the amount of water in the subsidence area and the evaluation of the impact of mining subsidence area on the catchment, which would provide a certain basis for the reclamation and protection of water resources in the locality.     

铁法矿区采煤沉陷区的发展演化趋势分析

通过对矿区采煤沉陷区的调查，对影响沉陷形成的主要因素(煤层的厚度、煤层的埋藏深度、上覆底层的物理力学性质、松散层的厚度、矿井开采条件等)进行了针对性的分析，并对将来煤炭采空区的地面沉陷情况(至2010年末，区内将再形成约55km^2的沉陷与变形区)进行了预测，为该矿区地面沉陷的综合治理提供依据。     

“物探+遥感”技术在矿山生态修复中的应用——以邢台园博园为例

矿山经过多年开采,地下形成采空区,造成地面沉降,开展绿色发展、生态修复工作刻不容缓. 冀中邢东煤矿由于长期开采,造成不同程度地面塌陷,不适于人类长期居住和农业耕作,邢台市经研究决定在采煤塌陷区建设中央生态公园——邢台园博园. 基于此,通过收集分析研究区内的地震、电法等物探资料,确定煤矿煤层赋存形态、采空区范围及富水情况,指导地面进行生态公园选址;利用遥感技术监测矿山生态修复进度. 研究表明:在矿区开展地震、电法工作查明煤矿开采生产情况,对科学规划地面矿区生态修复工作具有指导意义;利用遥感技术实时动态监测矿山生态修复进程,可为生态环境修复提供数据基础,对治理前后生态环境变化对比分析及效果评价提供影像资料.     

地下水位下降对采矿覆岩下沉影响探析

地下采煤会导致地表下沉盆地的形成。然而地下水尤其是承压水的流失即水位降低对地表下沉盆地的形成存在不可忽视的影响，而对两者之间的关系及其过程的力学机理是岩移领域亟待研究的课题之一。矿区地下水位降低的主要原因为承压水通过采动裂隙向下渗流、煤层开采前顶板岩溶含水岩层的疏干以及地面人为钻井取水。根据对一具体矿区地下水位下降对地表沉陷影响实测数据的分析得出，在以采煤为主导条件导致地表下沉的过程中，几乎全部的开采沉陷量中均包含有由于含水层释水而造成的沉陷量。通过对上覆岩层力学机理分析，指出水位降低对覆岩移动、地表下沉的影响是由于一定地质条件下可渗水性岩石的物理力学特性、渗水对其它岩石的软化、释水后原冲积层的压密固结以及水渗透过程中的水岩耦合作用等因素综合作用所致。同时，在以上理论的基础上还探讨性地建立了地下水位下降对地表下沉贡献模型，在此模型基础上可以建立2个或2个以上影响因素对地表下沉的贡献模型。最后以框图形式概括了地下水位下降及其对地表下沉影响的综合过程。     

基于Weibull时间序列函数的动态沉陷曲线模型

研究开采沉陷灾害发生及发展的动态过程具有重要的理论意义和工程应用价值。因采场上覆岩层及采矿过程的复杂性,全面研究采矿过程中上覆岩层的移动及地表的沉陷具有很大的难度,以研究地表沉陷区观测点下沉变化过程的时间序列为切入点,对开采沉陷的动态过程进行了研究。通过研究地表沉陷区观测点下沉量的时间序列发现,Weibull（韦布尔）函数可较准确地拟合和描述地表观测点的下沉过程曲线。煤层开采地表移动过程的FLAC3D模拟研究发现,同一沉陷区内各观测点的下沉过程在时间和空间上具有独立性。在该基础上,将概率积分沉陷曲线模型与Weibull时间序列函数相结合,给出沉陷区主剖面的动态下沉、倾斜和曲率曲线的唯象学模型,经实例证明建模思路是可行的。     

山东省济宁市煤矿矿集区地面沉陷现状遥感调查

山东省济宁地区煤矿资源丰富,但煤矿资源的开采利用造成了严重的地面沉陷。本文旨在调查济宁市煤矿矿集区地面沉陷的现状。在调查过程中主要采用最新获取的GeoEye-1高空间分辨率遥感影像,并结合历史资料分析研究区内地面沉陷造成的危害。通过遥感调查发现研究区内有地面沉陷积水坑47个,积水面积达2828hm2。地面沉陷已造成了20个村庄的搬迁,破坏土地约2667hm2,严重影响了当地人民的生活。本文使用遥感手段开展地面沉陷调查工作,充分反映了研究区地面沉陷灾害的实际情况,也充分体现了遥感技术快速、宏观的特点。     

神府—东胜矿区采煤塌陷对包气带结构的影响

通过对比采煤前和采煤塌陷过程中及稳定后包气带结构的变化,详细研究了神府—东胜矿区采煤塌陷对包气带结构的影响。研究结果表明:在采煤塌陷前,包气带组成岩性主要为风积砂岩和萨拉乌苏组粗砂岩,相对较薄,包气带组成介质层序较清晰,各岩性介质颗粒排列有序、均一,包气带内部结构以孔隙为主;在采煤发生后的塌陷非稳定阶段,塌陷裂隙贯通含水层,使地下水大量渗漏,同时产生大量贯穿地表的裂隙,引起岩土孔隙性发生变化,使得塌陷区包气带变厚,在一定深度上结构不均,浅部包气带结构转化为以裂隙为主;到塌陷稳定阶段,包气带结构变化趋于稳定,厚度增加,但在地表以下仍存在一些断续的裂缝(隙),使塌陷区包气带形成以孔隙为主,间夹断续裂隙的特殊包气带结构。     

无人机遥感技术在采煤地面塌陷监测中的应用

为研究无人机遥感技术在采煤地面塌陷监测中的应用,以宁东煤炭基地金凤煤矿011805综采工作面为例,探讨了利用无人机遥感技术进行地表裂缝解译、地面沉降量计算和地面塌陷规律研究的方法。结果表明:无人机飞行航高可根据需要识别的地表裂缝宽度确定,地形平坦地区识别2 cm地表裂缝的飞行航高一般应不超过143 m;地表裂缝宜于采用基于光谱、延长度和紧密度规则的面向对象的信息提取方法进行自动识别,在采用这种方法发现地表塌陷裂缝时宜采用基于边缘检测的图像分割模型和基于Full Lambda Schedule的图像融合模型;对无人机遥感地表高程值进行拟合校正可近似获得采煤工作面地表下沉量和下沉系数,说明无人机遥感技术可应用于采煤地面沉降量监测;综采工作面内地表裂缝数量多,总体垂直回采方向排列,切眼和顺槽附近地表裂缝数量少,总体平行顺槽和切眼展布。      

SBAS-InSAR和PS-InSAR技术在鲁西南某线性工程沿线地面沉降成因分析中的应用

地面沉降问题严重影响着鲁西南经济发展区交通工程建设。文中选择某线性工程两侧5 km范围作为研究区,文章收集RadarSAT-2（2017—2020年）、Sentinel-1A（2019—2020年）存档数据和沿线区域地质、水文地质、矿产开发资料,采用时序InSAR分析的方法,对研究区沿线地面沉降分布特征及规律进行综合分析。研究结果表明:研究区主要地面沉降诱因是煤矿采空区塌陷和地下水超量开采,前者以矿区工作面为中心形成沉降漏斗,沉降速率变化和沉降中心移动与煤矿作业工作面挖掘进度和转移密切相关;后者沉降分布规律与地下水开采使用点相关,形成与地下水开采使用范围相近的沉降带。研究区在2017—2020年内持续发生沉降,最大年均沉降速率为136.5 mm/a,单年累计最大沉降量为220 mm。经同期CPI水准点观测结果校核,InSAR数据处理成果平均误差小于1 cm/a,相关系数到达70%以上。本文采用的分析方法能及时准确反映出线路方案穿行研究区内各处地面沉降变化,为线路方案规划和地质灾害整治提供有效合理参考。     

Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS

This study constructs a hazard map for ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using a probability (frequency ratio) model, a statistical (logistic regression) model, and a Geographic Information System (GIS). To evaluate the factors related to ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, Global Positioning System (GPS) data, land use map, lineaments, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed from field investigations and reports on the existing ground subsidence areas at the study site. Nine major factors causing ground subsidence were extracted from the probability analysis of the existing ground subsidence area: (1) depth of drift; (2) DEM and slope gradient; (3) groundwater level, permeability, and rock mass rating (RMR); (4) lineaments and geology; and (5) land use. The frequency ratio and logistic regression models were applied to determine each factor’s rating, and the ratings were overlain for ground subsidence hazard mapping. The ground subsidence hazard map was then verified and compared with existing subsidence areas. The verification results showed that the logistic regression model (accuracy of 95.01%) is better in prediction than the frequency ratio model (accuracy of 93.29%). The verification results showed sufficient agreement between the hazard map and the existing data on ground subsidence area. Analysis of ground subsidence with the frequency ratio and logistic regression models suggests that quantitative analysis of ground subsidence near AUCMs is possible.