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准噶尔盆地东部西山窑组煤地球化学特征及古环境意义

徐小涛, 宁树正, 孙杰, 王化耀, 李保万, 张建强, 丁恋

徐小涛,宁树正,孙 杰,等. 准噶尔盆地东部西山窑组煤地球化学特征及古环境意义[J]. 煤炭科学技术,2024,52(S1):153−163. DOI: 10.12438/cst.2023-0640
引用本文: 徐小涛,宁树正,孙 杰,等. 准噶尔盆地东部西山窑组煤地球化学特征及古环境意义[J]. 煤炭科学技术,2024,52(S1):153−163. DOI: 10.12438/cst.2023-0640
XU Xiaotao,NING Shuzheng,SUN Jie,et al. Geochemical characteristics and paleoenvironmental significance of the Xishanyao Formation coal in the eastern Junggar Basin[J]. Coal Science and Technology,2024,52(S1):153−163. DOI: 10.12438/cst.2023-0640
Citation: XU Xiaotao,NING Shuzheng,SUN Jie,et al. Geochemical characteristics and paleoenvironmental significance of the Xishanyao Formation coal in the eastern Junggar Basin[J]. Coal Science and Technology,2024,52(S1):153−163. DOI: 10.12438/cst.2023-0640

准噶尔盆地东部西山窑组煤地球化学特征及古环境意义

基金项目: 

国家重点研发计划资助项目(2021YFC2902005);新疆国泰新华五彩湾矿业有限责任公司水文地质补勘资助项目(KCY0120220002);中国地质科学院资源调查资助项目(KD-[2023]-XZ-089)

详细信息
    作者简介:

    徐小涛: (1992—),男,山东莱州人,工程师,博士。E-mail:939495507@qq.com

    通讯作者:

    宁树正: (1977—),男,山东诸城人,正高级工程师,博士。E-mail:nsz0321@126.com

  • 中图分类号: P618.11

Geochemical characteristics and paleoenvironmental significance of the Xishanyao Formation coal in the eastern Junggar Basin

Funds: 

National Key Research and Development Project of China (2021YFC2902005); Hydrogeological supplementary exploration Project of Xinjiang Guotai Xinhua Wucaiwan Mining Co., LTD. (KCY0120220002); Resource Survey Project of Chinese Academy of Geological Sciences (KD-[2023]-XZ-089)

  • 摘要:

    准噶尔盆地是我国西北地区重要的含煤盆地,其东部煤田是我国新疆大型煤炭基地的重要组成部分,蕴藏着丰富的煤炭资源。以准噶尔盆地东部中侏罗世西山窑组B0、B1和B2煤层为研究对象,选取220个煤样品进行了详细的微量元素测定,并对16个煤样品进行了显微组分和镜质组最大反射率测试分析,在此基础上,综合分析了准噶尔盆地东部中侏罗世西山窑组成煤时期泥炭沼泽水体的氧化还原状态、古盐度特征以及成煤时期的古气候和大气氧含量变化特征。与世界低阶煤均值相比,准噶尔盆地东部中侏罗世西山窑组B0、B1和B2煤中Sr元素轻微富集,除此之外,B0煤中还存在Co元素轻微富集。Ni/Co-V/Cr和Ni/Co-Mo图解分析表明,泥炭沼泽处于氧化和贫氧状态,由此推断泥炭沼泽中水体的活动性较强,游离态的氧含量较高。煤中B/Ga比值的变化趋势表明,从B0到B1再到B2煤层,泥炭沼泽水体的古盐度逐渐升高,上部B2煤层沉积过程中泥炭沼泽古盐度较高可能是由水体蒸发量增加造成的,同时也表明B2煤层沉积时期的古气候呈现相对干热的特征。通过对B0、B1和B2煤中Sr/Cu比值分析得出,从西山窑组下部的B0煤层到中部的B1煤层再到上部的B2煤层,准噶尔盆地东部西山窑组成煤时期的古气候自下而上经历了由相对温湿逐渐向相对干热转变的过程。B0、B1和B2煤层中惰质组平均体积分数在40.4%~57.2%,平均值为48.5%,基于惰质组与大气氧含量的关系模型,估算出中侏罗世的大气氧体积分数约为27.7%,远高于持续燃烧所需的最低大气氧水平。

    Abstract:

    The Junggar Basin is an important coal-bearing basin in northwestern China. The coalfield in the east of the Junggar basin is an important part of the large Xinjiang coal base, and contains rich coal resources. In this study, we undertook a multi-proxy study evaluating trace elements, macerals and vitrinite maximum reflectance from coal seams B0, B1 and B2 of the middle Jurassic Xishanyao Formation to characterize paleoredox and paleosalinity conditions of coal-forming swamp and atmospheric oxygen level and paleoclimate during coal-forming period in the eastern Junggar Basin. 220 coal samples were selected from coal seams B0, B1 and B2 in order to determine trace elements. Moreover, macerals and vitrinite maximum reflectance of 16 coal samples was investigated. Compared with average values for world low-rank coals, the coal seams B0, B1 and B2 of the middle Jurassic Xishanyao Formation in the eastern Junggar Basin are slightly enriched in Sr, in addition, the coal seam B0 is also slightly enriched in Co. The analysis of Ni/Co-V/Cr and Ni/Co-Mo diagrams indicates that swamp was in oxic and dysoxic condition, which infers that the water in swamp has strong activity and high free oxygen content. According to B/Ga ratio analysis, the paleosalinity of swamp gradually increases from coal seam B0 to coal seam B1 and then to coal seam B2. The high paleosalinity of swamp during coal-forming period of coal seam B2 may be caused by an increase in the evaporation of water, which also indicates that the paleoclimate was relatively dry and hot during this period. The Sr/Cu ratios in coal seams B0, B1 and B2 reveal that the paleoclimate experienced a gradual transition from relatively warm and humid condition to relatively dry and hot condition from bottom to top during the Xishanyao Formation coal-forming period. The inertinite content varies from 40.4% to 57.2% with an average of 48.5% in coal seams B0, B1, and B2. Inferred atmospheric oxygen concentration in the middle Jurassic, as estimated from inertinite contents, were ~27.7%, which is much higher than the minimum required for sustained combustion.

  • 西部矿区是我国主要的煤炭生产基地,大范围、高强度地下采煤引起的地表沉陷对煤矿安全生产和矿区生态环境造成严重影响[1-2]。为了高效精准地获取矿区地表沉陷信息,除了采用常规的测量手段外,近年来InSAR、激光扫描、摄影测量等技术也有一定的工程应用,但均存在各种局限性。常规观测站采用大地测量方式虽然监测精度较高,但野外工作量大,仅能获取地表离散点的变形信息。InSAR技术适用于大范围区域小形变监测,而矿区开采引起的地表沉陷量大,沉降速度快,地形起伏较大,导致SAR影像容易出现失相干,因而无法获取矿区大量级、大梯度的形变信息[3],有学者利用像素追踪技术提取矿区大梯度形变量,但受制于SAR影像的分辨率及影像配准效果和地表辐射程度变化,仍然难以获得高精度的矿区形变信息[4]

    随着无人机遥感技术的快速发展,无人机激光扫描和摄影测量技术以其机动灵活、操作便捷、快捷获取高分辨率数据等优点,已应用于实景三维建模、地貌变化监测、灾害预警等领域[5-6]。其中激光扫描系统设备较为昂贵,所获取的激光点云受精度、密度和地形地貌条件影响较大,在矿区沉陷监测中尚未推广应用[7]。近年来,国内外学者已将无人机摄影测量技术应用于矿区沉陷监测,如利用无人机影像建模生成的数字正射影像(Digital Orthophoto Map,DOM)及数字表面模型(Digital Surface Model,DSM)获取露天矿采场的数字高程模型(Digital Elevation Model,DEM)[8]及提取沉陷区地表裂缝[9],并对多期 DEM直接进行叠加,可以快捷地获取采煤塌陷区地表下沉信息[10-12],通过提取主断面下沉信息,反算地表移动预计参数[13]。但上述基于无人机影像直接建模并进行叠加的数据处理方式,仍无法获取地物遮挡情况下真实的地表沉陷信息[14],尤其在植被覆盖区域,由于植被形态具有时序变化的特点,直接进行地表多期DSM的叠加所获得的地表变化,往往被显著的植被噪声所覆盖,并非是真实的开采沉陷信息。同时,由于多期航测的时间跨度较大,外业采集时光照、风力风向及植被覆盖等条件不同,利用多期影像叠加得到的沉陷信息往往存在显著的系统性误差,制约了该技术在矿区沉陷监测中的实际应用。

    为了消除无人机影像建模过程中植被噪声和模型叠加的系统性误差影响,高精度地提取矿区地表沉陷信息,笔者选择植被覆盖度较低的西部榆神矿区某综采工作面地表沉陷盆地为试验区域,利用低空无人机航拍影像生成的地面DSM及DOM数据开展试验研究,通过DSM叠加构建初始沉陷模型,在分析其误差来源的基础上,针对初始沉陷模型进行植被去除。利用DOM所携带的光谱信息对地物进行多尺度分割,生成各类地物的影像对象,根据同类地物影像对象在沉陷区域及非沉陷区域的高程系统误差情形,筛选出对沉陷模型精度有显著影响的地物类别,并构建矢量掩膜剔除初始沉陷模型中的地物噪声,再结合高斯卷积核及插值算法拟合出沉陷模型中植被区域的沉陷信息。进一步利用沉陷模型的非沉陷区域下沉量应为零的先验特征,消除整个沉陷模型的系统性误差,从而构建消除植被覆盖和无人机航测系统性误差影响的矿区地表沉陷模型,并通过与实测数据对比验证上述方法的有效性。

    试验区域位于陕北榆神矿区,地处毛乌素沙漠边缘地带,干旱少雨,耕地和建筑稀少,生态环境较为脆弱。区域地形起伏较小,地表以风积沙为主,植被覆盖度较低,主要为低矮沙生植物 [15]

    试验区132201开采工作面宽度为300 m,走向长度为4 000 m,煤层倾角平均为1°,埋深为300~350 m,煤厚2~2.5 m,由北东向南西进行回采。走向观测线设计长度900 m,倾向观测线设计长度为1 000 m,监测点间隔距离为20 m。在工作面上地表总共布设了96个监测点,其中沿走向方向布设46个监测点(Z01~Z46),沿倾向观测线布设50个监测点(Q01~Q50)。倾向监测线垂直相交于走向监测线的Z44号点。外业航飞范围为1.2 km×2 km的矩形区域。航飞区域的观测线布设与工作面位置关系如图1所示。

    图  1  航飞区域测点布设与工作面位置
    Figure  1.  Aviation area measurement point layout and working surface position

    采用大疆精灵4pro进行低空无人机航测数据采集。机型号为FC6310S,镜头焦距f为8.8 mm,像素大小为2.41 μm。由于航测区域地形较为平缓,设置相对航高为90 m,航向重叠度为80%,旁向重叠度为70%。该航拍条件下对应的地面分辨率为0.027 m。在工作面开挖之前2020年07月18日及开挖至750 m于2020年10月29日分别对试验区域进行航飞。在航飞区域大致均匀地布设6个像控点,另布设23个检查点。

    利用Pix4Dmapper软件进行无人机影像处理,包括相机检校、影像校正、配准空中三角测量解算生成密集点云数据,并利用点云数据生成DSM及DOM,并对两期 DSM进行叠加,生成初始沉陷模型。开采前、后两期DSM及其叠加生成的初始沉陷模型如图2所示。从图2c可看出,初始沉陷模型在沉陷区域和沉陷盆地以外的稳定区域都存在明显的噪声和粗差。

    图  2  试验区开采前、后DSM及初始沉陷模型
    Figure  2.  DSM of the experimental area before and after mining and initial subsidence model

    为了分析无人机影像数据的误差,通过采集开采前、后DSM中的检查点和像控点平面坐标及高程值,将其与实测数据进行对比,计算出两期影像数据平面坐标和高程的中误差,其结果见表1

    表  1  两期影像中检查点与像控点的中误差统计
    Table  1.  Medium error statistics of checkpoint and image control point in two-phase images
    点类型数据采集时间σx/mσy/mσz/m
    检查点开采前0.0440.0620.041
    开采后0.0470.0650.058
    像控点开采前0.0170.0150.024
    开采后0.0370.0550.029
    下载: 导出CSV 
    | 显示表格

    两期影像数据中像控点水平方向中误差σx、σy均值及高程中误差σz均值分别为0.027、0.035和0.026 m,检查点的平面和高程中误差均值分别为0.046、0.064和0.050 m。分析认为,受制于地面分辨率大小,空三解算后的像控点平面精度受刺点精度影响较大,使得而测区内地势平坦,像控点高程精度未受影响,像控点整体精度与GPS监测精度相当。而检查点平均中误差达到像控点误差的2倍左右,表明两期影像数据存在明显的系统性误差。

    在两期DSM直接叠加构建沉陷模型的过程中,各期影像中地物边界的像元坐标存在一定的偏差,尤其是植被、草地、沙地等地物的形态及范围会随时间发生变化,因此在时序DSM中相同地理坐标的像元往往不能反映同一地物的高程信息,导致两期DSM叠加时高程方向上存在明显的粗差。

    为了探究初始沉陷模型的误差来源及特征,选取初始沉陷模型中外围的非沉陷区域进行模型误差分析。利用面向对象的多尺度分割算法从DOM中提取非沉陷区内主要的地物类型—植被、道路和沙地的边界矢量,将其套合至DSM中,统计出非沉陷区域高程叠加结果的均值和标准差,视为初始沉陷模型的叠加误差,其结果见表2

    表  2  非沉陷区各类地物沉陷误差均值及标准差
    Table  2.  Mean and standard deviation of subsidence errors for all types of features in subsidence area
    DSM叠加值/m非沉陷区域
    植被区道路区沙地区
    均值0.2780.0420.039
    标准差0.1530.0150.035
    下载: 导出CSV 
    | 显示表格

    非沉陷区域的高程叠加所得的沉陷量应为零。而表2中植被覆盖区域叠加所得的沉陷量均值和标准差均很大,表明植被覆盖区域DSM叠加所得的沉陷量不仅存在明显的粗差,也存在显著的随机误差,而道路和沙地区域的沉陷量均值不超过0.042 m,标准差不超过0.035 m,说明这2个地类DSM叠加生成沉陷量的系统性误差和随机误差都较小,基本上与无人机影像数据本身的高程误差相当,这说明植被覆盖区域叠加生成的沉陷量其误差显著增大。因此,在利用无人机影像建模生成沉陷盆地时,必须先去除植被的影响,利用道路和沙地区域的DSM叠加才能获取相对精准的地表沉陷量。

    植被指数可以区分植被与非植被像元。现有的大多数植被指数模型是利用可见光与近红外范围波段进行组合运算来区分植被与非植被像元[16]。由于无人机光学影像数据只包含可见光波段的光谱信息,无法构建以近红外波段为基础的植被指数进行植被识别与分类。有学者[16-21]参照传统的归一化差分植被指数 (Normal Difference Vegetation Index, NDVI)构建了基于可见光波段的植被指数。目前,常见的可见光植被指数有归一化绿蓝差异指数(normalized green-blue difference index, NGBDI)[17]、归一化绿红差异指数(normalized green-red difference index, NGRDI)[18]、叶绿指数(green leaf index, GLI)[19]、可见光波段差异植被指数 (visible-band difference vegetation index,VDVI)[20]、过绿减过红指数(excess green-excess red index, EXG-EXR)[21]等。其中,3种典型的基于可见光波段的植被指数公式见表3

    表  3  典型的可见光植被指数
    Table  3.  Typical visible spectrum vegetation index
    植被指数计算公式取值范围
    NGBDI$ (G-B)/(G+B) $[−1,1]
    VDVI$ \left(2G-R-B\right)/\left(2G+R+B\right) $[−1,1]
    EXG-EXR\$$ 3g-2.4r-b $[−2.4,3]
    注:公式中$ {R}_{}、{G}_{}、B $ 分别代表红、绿、蓝波段的反射率或像元值;$ {r}_{}、{g}_{}、b $ 分别代表归一化处理后的红、绿、蓝波段像元值。
    下载: 导出CSV 
    | 显示表格

    利用表3中三种可见光植被指数计算方法进行植被提取。其关键环节为植被指数构建与阈值选取,而阈值选取的好坏直接影响到植被信息的提取精度。采用双峰直方图法确定各植被指数的阈值。

    图3a为试验区域,构建NGBDI、VDVI、EXG-EXR指数,得到植被指数空间分布,如图3b图3d所示。可以看出3种植被指数在植被与非植被区域整体灰度差异都比较明显,植被区域较亮,非植被区域较暗。但EXG-EXR指数在植被与非植被区对比度较低,部分区域存在类间边界模糊现象;NGBDI指数对光照较为敏感,阴影区域植被与沙地像元具有较大重叠。

    图  3  研究区DOM及3种植被指数计算结果
    Figure  3.  DOM in the study area and results of three planting index calculation

    各类地物在3种植被指数中的统计特征值见表4,NGBDI指数在草地与非植被区域之间存在重叠,且灌木的NGBDI指数与非植被区域很相近,表明该指数对于植被与非植被的区分效果较差。EXG-EXR 、VDVI指数在植被与非植被地物之间无交叉现象,但EXG-EXR指数在植被与非植被区域的标准差均较大,而VDVI指数的标准差均较小,表现更为紧凑,其植被与非植被间的阈值在0附近,表明VDVI能够更好地区分植被与非植被区域。

    表  4  VDVI、NGBDI、EXG-EXR的统计特征值
    Table  4.  Statistics of VDVI、 NGBDI and EXG-EXR
    主要地物VDVINGBDIEXG-EXR
    均值标准差均值标准差均值标准差
    灌木0.1260.0410.4090.0290.2570.067
    草地0.0520.0120.3650.010−0.0530.035
    沙地−0.0040.0060.3530.007−0.2120.031
    道路−0.0050.0080.3630.006−0.2800.026
    下载: 导出CSV 
    | 显示表格

    3种可见光植被指数统计直方图如图4所示,各植被指数直方图都具有明显“双峰”特征,对于出现双峰特征的植被指数采用双峰阈值法确定阈值进行植被信息识别。将目视解译获取的植被区域作为标准影像,通过计算提取正确率及Kappa系数来评价各植被指数提取植被的精度,其结果见表5

    图  4  各植被指数统计直方图
    Figure  4.  Statistical histogram of each vegetation index
    表  5  植被提取精度评价
    Table  5.  Accuracy assessment of vegetation extraction
    植被指数阈值正确率/%Kappa系数
    植被非植被总正确率
    VDVI0.02192.1895.9396.380.96
    NGBDI0.36585.2676.5983.250.76
    EXG-EXR−0.04581.1786.5683.340.71
    下载: 导出CSV 
    | 显示表格

    表5可见,VDVI提取植被的正确率最高,提取结果与目视解译结果基本接近。因此,利用VDVI指数及上述最优分类阈值对试验区DOM进行植被信息提取,获取植被区域矢量边界并进行剔除,样本区植被提取其结果如图5所示。

    图  5  样区VDVI植被提取及剔除结果
    Figure  5.  Vegetation extraction and removal results of VDVI in sample area

    利用上述植被提取方法直接对叠加生成的初始沉陷模型进行植被区域剔除,然后对剔 除植被的空白区域进行沉陷信息拟合,从而构建完整的沉陷模型。具体步骤为:通过上述方法获得植被覆盖区矢量边界,制作掩膜并套合至初始沉陷模型中,剔除植被区域的沉陷信息;然后采用高斯卷积核构建高斯金字塔,将剔除植被后的沉陷模型降采样至较低空间分辨率,在低分辨率顶层图像中采用专业化数字高程模型插值算法[22],对掩膜区域进行插值,并逐层上采样,重构沉陷空白区域的下沉信息,得到剔除植被影响完整的沉陷盆地模型,如图6所示。

    图  6  剔除植被及插值生成的沉陷盆地模型
    Figure  6.  Subsidence basin model after removing vegetation and interpolation

    为验证植被区域剔除后沉陷模型的实际精度,在沉陷模型主断面上提取下沉剖面,并与132201工作面地表移动监测站实测数据进行对比,发现剔除植被影响的沉陷模型下沉值在整体上小于实测的下沉量。经统计,沿走向主断面上模型下沉值与实测值的差值最大为0.170 m,误差均值为0.076 m;沿倾向主断面上模型下沉值与沿倾向主断面上模型下沉值与实测值的差值最大为0.175 m,误差均值为0.071 m。这表明沉陷模型仍然存在一定的系统性误差。为此,本文从非沉陷区的地面控制点出发,沿相邻控制点连线方向,按80 m间隔采集6个非沉陷区样本的沉陷信息,并统计误差分布特征,从中分析沉陷模型中残存的系统性误差。各样区沉陷误差分布曲线如图7所示。

    图  7  非沉陷区样本的沉陷误差统计直方图
    Figure  7.  Statistical histogram of subsidence error in non-subsidence area

    图7可见,在靠近控制点的非沉陷区,如样区1,沉陷误差主要分布于−0.05~0.05 m,该区间误差频率占比达88.5%,误差均值为−0.005 m,几乎不存在系统性误差;距离控制点越远的区域如样区6,沉陷误差主要分布于0 ~0.10 m,该区间误差频率占比达79.62%,误差均值为+0.068 m,沉陷值中存在明显的系统性误差。因此,在剔除植被影响后的沉陷模型中,沉陷误差仍呈现出一定的规律性,可以将非沉陷区域的误差均值视为系统性误差,通过线性回归分析建立沉陷模型的系统性误差与距控制点距离之间的量化关系,构建插值算法对整个沉陷模型进行系统性误差改正。实施误差改正前、后沉陷模型沿主断面的下沉剖面与实测下沉曲线对比如图8所示。

    图  8  系统误差改正前、后沉陷模型及实测数据的下沉曲线对比
    Figure  8.  Comparison of subsidence curves before and after systematic error correction model and measured data

    分析图8中各下沉曲线可知,经过系统性误差改正后的沉陷模型下沉曲线与实测数据更为接近,其明显改善了局部区域误差较大的问题。经统计,沉陷模型沿走向主断面下沉值与实测值的误差均值为−0.025 m;沿倾向主断面下沉值与实测值的误差均值为−0.048 m。在整个沉陷盆地内下沉值的均方根误差为0.041 m。这表明基于无人机影像数据构建的矿区地表沉陷模型,在剔除植被影响和进行系统性误差改正后,已具备较高的精度和可靠性。

    试验区所在的榆神矿区地形起伏不大,地表建筑物及植被较少,大规模采煤引起的地表沉陷量级及沉陷盆地的范围均较大,利用低空无人机航测技术获取地表沉陷信息在技术上具备可行性。然而,目前实际应用中主要是通过直接叠加多期DSM来得到地表沉陷信息。由于开采过程中多期航测作业存在时间差异性,导致时序数字表面模型及叠加后的沉陷模型不可避免地存在因植被变化和航测误差引起的显著噪声,主要表现在以下2方面:

    1)地表植被在光照、风力等外在条件变化及随时间生长变化下,导致密集匹配点云构建的DSM存在形态上的不确定性,即使在非沉陷区同一时间采集到的两次影像数据,在相同地理坐标下各像元所反映的地物仍存在明显的差异性。

    2)无人机影像在拼接和提取过程中产生的误匹配点参与空三解算时,会导致建模结果偏离真值[23-24],且这种误差具有一定的传播性,可能随着距离像控点越远而增大。因此,若要提高沉陷信息获取精度,需消除植被覆盖区所造成的沉陷误差及沉陷模型的系统性误差影响。鉴于无人机影像数据不具备穿透植被的能力,无法获取植被区域的真实地面信息[25],笔者利用VDVI植被指数提取DSM中的植被区域并构建掩膜,在生成的初始沉陷模型中进行植被剔除,从而消除植被对沉陷模型的影响。

    通过提取非沉陷区各类地物(如植被、道路、沙地)影像对象的沉陷误差信息,分析不同地物类型对于沉陷误差的影响。在经过提取、剔除对初始沉陷模型精度影响较大的植被覆盖区并进行沉陷插值后,模型误差均值及标准差均得到有效改善。

    地表沉陷模型包含随机误差和系统性误差。其中随机误差服从正态分布规律,系统性误差源于航测坐标系误差、空三解算时的误匹配、迭代平差过程误差等多因素影响。由于开采沉陷盆地的范围较小,沉陷盆地以外区域可视为沉陷量为零的稳定区(非沉陷区),通过分析剔除植被影响后的非沉陷区域误差分布特征,获取沉陷模型周围的系统性误差分布,利用插值原理对沉陷模型施加系统性误差改正。经过改正后沉陷模型的下沉剖面数据与实测数据符合性更好,改进效果显著。

    1) 在西部矿区植被覆盖度较低的环境下利用低空无人航测影像数据可构建地表全盆地沉陷模型,能够直观、完整地展现矿区开采沉陷的时空特征。但直接通过多期DSM直接叠加生成的初始沉陷模型仍存在显著的噪声。试验区条件下植被覆盖区时序DSM高程不确定性和航测系统性误差是造成沉陷模型噪声的主因。

    2) 针对初始沉陷模型中植被覆盖区域误差较大的问题,选用VDVI植被覆盖指数进行植被信息提取,能够较为准确地获取植被覆盖区矢量边界。初始沉陷模型在经过植被区域提取、剔除并进行沉陷插值后,模型误差均值及标准差均得到显著改善。

    3) 针对沉陷模型的系统性误差,利用沉陷盆地以外稳定区域的沉陷误差空间分布特性,对沉陷模型施加系统性误差改正,能够有效提升沉陷模型的实际精度。与实测数据对比表明,沉陷模型沿走向主剖面下沉量的误差均值为−0.025 m,沿倾向下沉量的误差均值为−0.048 m,在整个沉陷盆地内下沉值的均方根误差为0.041 m,能够满足西部矿区高强度开采引起的地表大量级沉陷监测的基本要求。

    4)矿区开采引起的地表沉陷在产生下沉的同时也会伴随水平位移。在地势平缓,地表起伏较小的西部榆神矿区,地表水平移动对地表下沉信息提取的影响较小,可忽略不计。而在地形起伏较大的条件下,这种水平位移本身会导致显著的高程叠加误差。如在黄土高原矿区复杂地貌条件下,分米级甚至厘米级的水平位移量都会导致DSM及其叠加模型产生米级的高程误差。因此,针对地貌复杂和植被覆盖度很高的矿区沉陷监测,文中提出的基于无人机影像建模的矿区地表沉陷信息提取改进方法仍有待进一步发展和完善。

  • 图  1   准噶尔盆地东部区域地质图

    Figure  1.   Regional geological map of eastern Junggar Basin

    图  2   准噶尔盆地东部中侏罗世西山窑组沉积相综合柱状图

    Figure  2.   Comprehensive column of sedimentary facies variation of the middle Jurassic Xishanyao Formation in the eastern Junggar Basin

    图  3   准噶尔盆地东部五彩湾矿区西山窑组煤中微量元素富集系数

    Figure  3.   Concentration coefficients of trace elements of coal samples from the Xishanyao Formation in Wucaiwan mine, eastern Junggar Basin

    图  4   准噶尔盆地东部五彩湾矿区西山窑组成煤沼泽氧化还原判别图(底图来自RIMMER, 2004 [3]

    Figure  4.   Paleoredox discrimination for the Xishanyao Formation coal-forming swamp in Wucaiwan mine, eastern Junggar Basin (modified after RIMMER, 2004[3])

    图  5   准噶尔盆地东部五彩湾矿区西山窑组成煤沼泽古盐度判别图

    Figure  5.   Paleosalinity discrimination for the Xishanyao Formation coal-forming swamp in Wucaiwan mine, eastern Junggar Basin

    图  6   准噶尔盆地东部五彩湾矿区西山窑组B0、B1和B2煤中Sr/Cu和B/Ga变化趋势

    Figure  6.   Variation trend of Sr/Cu and B/Ga in coal seams B0, B1 and B2 of the Xishanyao Formation in Wucaiwan mine, eastern Junggar Basin

    图  7   准噶尔盆地东部五彩湾矿区中侏罗世西山窑组大气pO2与惰质组含量关系图(底图来自Glasspool等[10]

    Figure  7.   Correlation of atmospheric oxygen concentration with inertinite during the middle Jurassic Xishanyao Formation based on the model proposed by Glasspool et al[10]

    表  1   准噶尔盆地东部五彩湾矿区西山窑组煤中显微组分和镜质组最大反射率

    Table  1   Macerals and vitrinite maximum reflectance of coal samples from the Xishanyao Formation in Wucaiwan mine, eastern Junggar Basin

    煤层 样品数 显微组分体积分数/(%, mmf) 镜质组最大反射率/%
    腐植组 惰质组 稳定组
    B2 6 $\dfrac{39.4\sim 52.2}{44.9} $ $\dfrac{43.3\sim 55.2}{49.4} $ $\dfrac{4.0\sim 9.4}{5.7} $ $\dfrac{0.37\sim 0.41}{0.39} $
    B1 6 $\dfrac{34.5\sim 55.1}{43.3} $ $\dfrac{40.4\sim 57.2}{50.9} $ $\dfrac{4.5\sim 8.3}{5.7} $ $\dfrac{0.40\sim 0.42}{0.41} $
    B0 4 $\dfrac{47.4\sim 52.6}{49.9} $ $\dfrac{41.3\sim 46.5}{43.6} $ $\dfrac{5.8\sim 7.9}{6.6} $ $\dfrac{0.37\sim 0.42}{0.41} $
      注:mmf代表去矿物基;煤中显微组分含量和镜质组最大反射率数据来自《新疆准东煤田吉木萨尔县五彩湾矿区帐南西井田勘探报告》;数据格式为$\dfrac{最小值 \sim 最大值}{平均值} $。
    下载: 导出CSV

    表  2   准噶尔盆地东部五彩湾矿区西山窑组煤中微量元素含量

    Table  2   Trace element compositions of coal from the Xishanyao Formation in Wucaiwan mine, eastern Junggar Basin

    煤层 样品数 元素含量/(μg·g−1)
    Ge V Cr Co Ni Cu Zn Ga Pb Mo Sr B
    B2 81 $ \dfrac{0\sim 15}{0.54}$ $ \dfrac{3\sim 85}{21.12} $ $ \dfrac{4\sim 48}{16.37} $ $ \dfrac{1\sim 16}{5.73} $ $ \dfrac{3\sim 35}{11.68} $ $ \dfrac{1\sim 36}{9.09} $ $ \dfrac{4\sim 197}{26.16} $ $ \dfrac{1\sim 11}{4.06} $ $ \dfrac{0\sim 126}{5.99} $ $\dfrac{0\sim 8}{1.23} $ $ \dfrac{15\sim 1\;708}{453.96} $ $ \dfrac{2\sim 22}{8.73} $
    B1 85 $ \dfrac{0\sim 7}{0.31} $ $ \dfrac{2\sim 58}{12.85} $ $ \dfrac{3\sim 33}{11.20} $ $ \dfrac{1\sim 15}{4.67} $ $ \dfrac{2\sim 29}{9.54} $ $ \dfrac{2\sim 32}{7.00} $ $ \dfrac{5\sim 154}{21.62} $ $ \dfrac{1\sim 11}{2.76} $ $ \dfrac{0\sim 53}{3.52} $ $ \dfrac{0\sim 14}{0.84} $ $ \dfrac{185\sim 1\;136}{359.65} $ $ \dfrac{2\sim 25}{7.69} $
    B0 54 $\dfrac{0\sim 6}{0.37} $ $\dfrac{2\sim 183}{21.44} $ $\dfrac{4\sim 93}{15.67} $ $\dfrac{2\sim 49}{10.28} $ $\dfrac{3\sim 65}{14.39} $ $\dfrac{2\sim 65}{11.91} $ $\dfrac{3\sim 87}{24.87} $ $\dfrac{1\sim 39}{4.76} $ $\dfrac{0\sim 52}{7.35} $ $\dfrac{0\sim 14}{1.09} $ $\dfrac{195\sim 1\;603}{367.30} $ $\dfrac{1\sim 64}{10.65} $
    世界低阶煤 2 22 15 4.2 9 15 18 5.5 6.6 2.2 120 56
      注:煤中微量元素数据来自《新疆准东煤田吉木萨尔县五彩湾矿区帐南西井田勘探报告》。
    下载: 导出CSV
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