Accretionary orogenic processes of the Beishan orogenic belt
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摘要:
北山造山带位于中亚造山带南缘中段，处于东西向构造衔接的关键大地构造位置。近年来，北山造山带构造演化成为研究热点，在基底时代与构造属性、岩浆岩成因、蛇绿岩时代与构造背景、沉积物源与大地构造背景、构造变形样式与时限等方面取得了重要进展。本文以这些新进展为主线，总结北山造山带各构造单元的基本特征，梳理岩浆作用时空格架和蛇绿混杂岩时代，在此基础上探讨北山增生造山过程。北山造山带前寒武纪岩浆记录为零星分布的中元古代（约1.4 Ga）及新元古代（0.9~0.8 Ga）花岗片麻岩，不存在大规模的前寒武纪结晶基底；早古生代—早中生代发育连续的岩浆作用。蛇绿岩时代具有从中部寒武纪—奥陶纪向南北两侧石炭纪—二叠纪变年轻的特点，最终的缝合带位于柳园—后红泉一带，基性岩时代和最年轻的沉积岩基质时代共同限定了最终的增生拼贴时代为中—晚三叠世。北山造山带从中元古代开始位于超大陆外围增生边缘，此后经历了新元古代和古生代的长期增生，在三叠纪完成了最终的增生造山过程并进入陆内演化阶段。增生造山过程中幔源岩浆形成岛弧新生地壳、增生楔和岛弧侧向拼贴增生，对大陆生长具有重要意义。
Abstract:The Beishan orogenic belt is located in the middle of the southern margin of the Central Asian orogenic belt, and is at the key tectonic position of the east−west tectonic connection. In recent years, the tectonic evolution of Beishan has become a research focus, and important progress has been made in the aspects of orogenic basement, magmatism, ophiolitic mélanges, sedimentation, and deformation. This paper focuses on recent advances and summarizes the basic characteristics of each tectonic unit of Beishan, particularly the spatial−temporal distribution of magmatism and ophiolitic mélanges, and discusses the accretionary processes of Beishan. The Precambrian magmatic records are scattered Mesoproterozoic (Ca. 1.4 Ga) and Neoproterozoic (Ca. 0.9~0.8 Ga) gneissic granitoids, and there is no large−scale Precambrian basement in Beishan. Continuous magmatism developed from Early Paleozoic to Early Mesozoic across Beishan. The ophiolitic mélanges changed from Cambrian–Ordovician in the middle to Carboniferous–Permian in the north and south, and the final suture zone is located in the Liuyuan–Houhongquan area in southern Beishan. The ages of mafic rocks and the youngest sedimentary matrix jointly define the age for terminal accretion as the Middle–Late Triassic. Beishan was located at the margin of supercontinent from the Mesoproterozoic, and then experienced long−term accretion in Neoproterozoic and Paleozoic, and terminated accretionary orogeny and shifted to intracontinental evolution in Triassic. The accretion of mantle−derived arc magmatism and growth of accretionary wedges are of great significance to continental growth during the long−lived accretionary orogenesis.
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Keywords:
- Beishan orogenic belt /
- magmatism /
- ophiolitic mélange /
- accretionary orogeny /
- continental growth
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进入新世纪以来，中国面临城镇化进程的跨越。党的十九大报告提出实施区域协调发展战略，要求“以城市群为主体构建大中小城市和小城镇协调发展的城镇格局”，新型城镇化建设进入新时期。城市不断建设和扩张，建设用地、工业用地增长的同时，侵蚀着耕地、水域、草地等。安庆市是快速发展的长三角区域的城市之一，多年来城市地区人类活动剧烈，原始地貌改变众多，地表覆被严重，给城市勘察带来众多困难。其中一个典型问题为，原有的地表河浜可能由于各种原因被填埋形成暗浜。暗浜底部多以淤泥为主，部分地段还含有大量生活垃圾，土性极差。随着时间的推移，这些埋藏于地下的不良地质条件逐渐成为后期市政道路、桥梁工程建设中的质量隐患，造成工程开挖难度加大，尤其给河网地区浅表的工程建设带来较多的困难。因此，提取城市地区的暗浜分布范围，为城市工程建设与城市规划提供决策支持，具有十分重要的意义。
暗浜反映出地表覆被类型的变化。目前暗浜的探测多以物探方法为主[1-2]，如二维微动剖面探测法、瞬态瑞雷波法、高密度电法, 这些探测方法一般是在对工作区地质条件较熟悉的条件下开展的，且适合小范围的勘察，成本较高。随着卫星对地观测技术的发展，遥感影像的时空分辨率在不断提高，这为监测和刻画土地利用变化时空过程提供了丰富、可靠的数据支撑。变化检测是通过观察一个对象或一个现象在不同时间的状态，来确定其发生的差异过程[3-6]。遥感影像变化检测是利用不同时期覆盖同一地表区域的多源遥感影像和相关地理空间数据，结合相应地物特性和遥感成像机理，采用图像、图形处理理论及数理模型方法，确定和分析该地域地物的变化，包括地物位置、范围的变化和地物性质、状态的变化[7]。其研究的目的是找出感兴趣的变化信息，滤除作为干扰因素的不相干的变化信息。
多时相高分辨率遥感影像具有空间信息丰富、地物细节清晰的特点, 可以利用空间信息提高变化检测精度，已越来越多地应用于城市监测等领域[8-12]。面向对象的处理是高分辨率遥感影像解译领域的重要思想。面向对象的变化检测不仅可以利用基于像素的方法所使用的光谱、纹理和特征变换等作为变化的测度，还可以利用有关对象的形状特征和空间关系的附加信息。面向对象的遥感图像变化检测首先要解决的问题是如何根据多时相影像获取适合变化检测的图像对象，以图像分割技术为基础，基于获取的图像对象进行变化检测。Desclé等[13]对SPOT多期影像进行分割，计算每期影像的光谱值，利用统计方法提取出变化的对象。Conchedda等[14]为检测红树林的变化，利用分割方法中的区域生长法多时相影像进行分割。Stow等[15]应用变化检测技术提取植被的变化信息，其分割方法选择试错法确定分割参数。Duveiller等[16]将多时相遥感影像叠加，同时分割，用以提取森林覆盖的变化。Wang等[17]在此分割方法基础上，对多时相图像对象提取了多种特征，选择最优的阈值界定“变”与“不变”，提取土地覆盖类型的变化信息。很多学者利用基于对象的分类后比较方法对地图或GIS图层进行更新[18-20]，能够提取出“from-to”信息，在土地覆盖/利用变化检测中应用广泛。就遥感影像分类方法而言，SVM的独特优势在于可以处理小样本、高维数、非线性数据问题，将SVM算法应用于遥感影像分类时，可以通过少量的训练样本获得比传统分类方法更高的分类精度。由于SVM分类算法应用广泛且分类精度较高，为进行暗浜自动化监测提供了有效途径，故本文选择SVM算法为遥感影像分类方法。
暗浜反映出地面以下地质体与周围物体结构的差异，如水塘填埋物与其周围土体结构必然是不同的。而传统的光学遥感卫星难以检测地下土体或岩石结构特征。微动测深是一种便捷有效的物探手段，基于微动信号估算地层速度结构，抗干扰能力强，探测深度大，适用于人口密度大、有振动干扰的城区房屋密集区环境[21-24]，广泛应用于地铁工程、道路工程、城市地质等工程领域。为了验证利用遥感变化检测提取的暗浜范围是否准确可靠，将遥感技术与微动探测方法相结合，基于暗浜提取结果，选取典型区域，采用微动探测进行实地勘察。
本文以提取安庆研究区(主要分布在主城区)的暗浜分布范围为目的，利用面向对象的分类后比较的变化监测方法，分别对2期遥感影像T1与T2进行面向对象的监督分类，进而对分类结果进行叠加分析，提取出前期土地利用类型为水体但目前变为其他土地覆盖类型的区域，即为暗浜的分布区域。在此基础上，将检测出来的变化区域作为靶区，选择典型区域，利用物探工作方法中的微动测量方法，探测地下是否存在暗浜，验证变化检测结果的可靠性。本次工作旨在利用遥感影像自动识别暗浜方法和技术流程，为城市工程建设与城市规划提供决策支持。
1. 研究区与数据介绍
安庆市位于安徽省西南部，长江下游北岸，皖河入江处，河网密集，是一个快速发展的城市。多年来安庆市采取了“东进、北扩、西拓”的扩张方式，对于城市的改造力度很大，由此导致了城市地表覆被类型的变化。在城市规划和工程建设中，暗浜是一个较典型的不良工程地质现象，对工程施工、尤其对基础工程建设构成隐患。为避免暗浜对工程建设尤其是基坑工程建设带来不利影响，有必要查明暗浜的特征和空间分布范围，为实施地球物理勘探方法圈定靶区。
暗浜调查区域布置为矩阵区域，以安庆市辖区为主，包括宜秀区、十里铺、老峰镇、大龙山镇等，数据选取2004年的Quickbird影像与2018年的worldview-2影像。调查区域与数据选择见图 1。
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图 1 研究区概况及遥感影像数据a—研究区位置图；b—T2(2018年)影像；c—T1(2004年)影像Figure 1. Illustration of the study area and remote sensing images2. 研究方法
暗浜提取采用以遥感图像识别为主，微动探测为辅的思路，首先利用遥感图像变化检测方法提取暗浜范围，进而结合微动探测技术，选择典型区域，验证结果的可靠性。遥感图像变化检测技术通过处理相同区域在不同时相的遥感影像，得到该区域的地物类型变化，已经在城市建设、森林保护、土地监测、灾害评估等领域发挥了重要的作用。在变化检测中，分类后比较法是一种根据分类比较方法得到变化细节的判别方法，目前使用非常广泛。分类后比较具体的做法是分别对不同时相的遥感影像进行分类，得到不同时期的专题图，通过叠加这些专题图像，判别随着时间跃迁，类别的具体变化。微动探测方法可以勘察地表以下地层结构，根据横波波速结构或视横波波速剖面图，结合钻孔钻探资料及其他地质资料加以解释，给出地质现象解译，判断暗浜是否存在。
技术路线图见图 2，首先针对2期高分辨率遥感影像T1和T2，对数据预处理，进而采用分类后比较的变化检测方法，其中分类时以图像分割得到的影像对象为基本处理单元，不仅利用遥感影像的光谱信息，而且充分利用影像的形状、纹理、上下文等信息，提高分类精度。根据分类结果，提取暗浜范围，进而选择典型的区域，辅以微动探测，布设试验，验证暗浜提取结果的可靠性。
2.1 图像预处理
影像预处理是面向对象的变化检测的前提，主要包括辐射校正和几何校正。首先对2期影像进行相对辐射校正，以降低因图像辐射差异对变化检测结果的影响。然后对影像进行几何校正，使2期影像精确配准，控制误差小于0.5像素。
2.2 图像分割
由于高分辨率遥感影像单个地物特征内反射率的变异性及图像的复杂性，传统的逐像素分析方法难以适应于高分影像的应用需求。研究表明，基于对象的影像分析(OBIA)技术可减少地理参考及光谱变异性等带来的影响。图像分割是OBIA的核心，分割过程将图像划分为光谱上相似且在空间上相邻的同质对象。理想的分割结果是，对象内部的方差最小，对象与对象之间的异质性较大。通过分割生成图像对象，即分割体，获得更丰富的信息，包括纹理、形状和与相邻对象的空间关系，从而利用空间上下文信息进一步分类。
目前的分割方法可以分为2类：①基于边缘的影像分割；②基于区域的影像分割。基于边界的分割算法是按照影像中各类地物像素灰度值不连续的特点、边界特征差异性的原则实现影像分割[19]。目前常用的边缘检测算子有Krisch算子、Robert算子等。基于边缘的方法更符合人们的认知，当影像各地物类型之间有显著差异时，该方法能够取得良好的效果。基于区域分割的方法主要分为2种：①区域增长；②区域分裂与合并。区域分割的依据是影像内像素点或像素块的特征相似性，包括灰度、颜色、纹理、空间结构等特征信息[25]。
影像分割的核心问题在于如何选择最优的影像分割尺度，在最优分割尺度下获得的影像对象能够更好地表达实际地物的特征信息，有利于变化检测结果精度的提高[26]。本文影像分割步骤采用ENVI 5.3的Feature Extraction模块。该模块采用基于边缘的分割方法，可以根据遥感影像内邻近像素的色度、亮度、纹理等信息对影像进行分割，同时通过分割效果预览框可以实时地进行分割尺度的调整，操作简单方便，应用性较强。本文反复试验对比，通过目视判断确定研究区的最优分割尺度。
2.3 特征提取
利用影像的原始特征光谱，提取指数特征及纹理特征作为特征组合。指数特征有归一化水体指数NDWI及归一化植被指数NDVI。
NDWI=ρgreen−ρnirρgreen+ρnir (1) (1) 式中，ρnir与ρgreen分别表示影像近红外波段、绿色波段的反射率，突出水体信息。
NDVI=ρnir−ρredρnir+ρred (2) (2) 式中，ρred表示影像红色波段的反射率，增强植被信息。
纹理特征使用灰度共生矩阵计算，通过8种基于二阶矩阵的纹理滤波的结果对比，选择均值滤波的结果作为纹理特征。利用PCA提取特征组合。通过协方差矩阵计算主成分，选择包含95%的信息量的前3个特征分量。
2.4 图像分类
基于分割结果，利用支持向量机(SVM) 算法，对图像进行监督分类。经过对研究区影像进行判读分析，确定建设用地(包括城镇用地、农村居民点、其他建设用地)、林地、草地、耕地、水域、未利用土地等类别，选择各类地物的训练样本，训练样本的位置随机选择。在此基础上，利用SVM算法对影像进行分类。
SVM算法的基本思想是，基于训练样本集D在样本空间找到具有“最大间隔”的划分超平面，将不同类别的样本分开。SVM在线性分类的基础上引入了结构风险最小化原理，提高了算法的泛化能力，在小样本的情况下也能达到较好的分类精度。SVM与核函数方法结合，解决了非线性分类问题。由于此算法的上述优势，在遥感影像分类中广泛应用。
具体模型如下：
min (3) 其中, w=(w1; w2; …; wd)，为超平面的法向量，b为位移项，xi∈D为训练样本集中的一个样本。
对式(3)使用拉格朗日乘子可得到其“对偶问题”：
\begin{gathered} \max \limits_{\alpha} \sum\limits_{i=1}^{m} \alpha_{i}-\frac{1}{2} \sum\limits_{i=1}^{m} \sum\limits_{j=1}^{m} \alpha_{i} \alpha_{j} y_{i} y_{j} x_{i}^{T} x_{j} \\ { s.t. }\ \sum\limits_{i=1}^{m} \alpha_{i} y_{j}=0 , \\ \alpha_{i} \geqslant 0, i=1, 2, \cdots, m \end{gathered} (4) 对于线性不可分问题，将核技巧应用到支持向量机，基本思想是通过非线性变换将输入空间对应一个特征空间，使得在输入空间Rn中的超曲面模型对应于特征空间H中的超平面模型。这样，分类问题的学习任务通过在特征空间中求解线性支持向量机就可以完成。此时，对偶问题则为：
\begin{aligned} &\max \limits_{\alpha} \sum\limits_{i=1}^{m} \alpha_{i}-\frac{1}{2} \sum\limits_{i=1}^{m} \sum\limits_{j=1}^{m} \alpha_{i} \alpha_{j} y_{i} y_{j} K\left(x_{i}, x_{j}\right) \\ & { s.t. }\ \sum\limits_{i=1}^{m} \alpha_{i} y_{j}=0, 0 \leq \alpha_{i} \leq C, i=1, 2, \cdots, m \end{aligned} (5) 其中, K(xi, xj)为核函数，C为惩罚参数。常用的SVM核函数有多项式核函数，高斯核函数，sigmoid核函数等。已有研究表明，高斯核函数更适用于土地覆被的分类[27]，因而采用高斯核函数作为SVM分类器中的核函数，其数学形式为：
K(x, z)=exp \left(-\frac{|| x-z||^{2}}{2 \sigma^{2}}\right) (6) 2.5 变化检测
变化检测的流程如图 3所示。变化检测是基于SVM算法得到地物分类结果，将2期图像的结果进行对比，提取时相T1(2004年)影像上的水体在时相T2(2018年)影像的变化情况。首先将2期影像的分类结果图转为分类矢量图，对2个图层进行叠加分析，得到包含变化信息的多边形，同时赋予这些多边形类别变化的属性信息，完成变化检测，并统计各类别的增加、减少情况。
2.6 地球物理特征分析
基于变化检测提取的暗浜范围，选择前期是坑塘目前已变为耕地或道路的区域作为采样点，布设微动测线，根据现场的探测结果解译，判断地下是否有存在暗浜的可能。图 4为在安庆市迎江区布设的3条微动测线(底图 4，为2004年遥感影像)，可知在2004年此区域地表覆盖类型为池塘，而现场测量时发现，此处已在开发建设，坑塘早已被填埋。若暗浜填充物的波阻抗大于周围地层介质波阻抗，则微动速度-深度剖面上暗浜区域应表现为相对低速异常，反之则为相对高速异常。由此，推断地质断面图并判断暗浜的位置。
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图 4 微动测量工作布置示意图(2004影像)a—测线1和测线2；b—测线3Figure 4. Layout of microtremor survey (images of 2004)3. 结果
3.1 变化检测实验结果
对2期影像分别进行裁剪、拼接、相对辐射校正、地理配准，得到T1与T2两期影像。本文目的在于提取暗浜的空间分布，即以前地表为水体包括沟塘河渠的地区，现在变化为非水体。因此，实验中，对T1影像即2004年QuickBird影像分为水体和非水体2类，将T2影像即2018年WorldView-2影像分为建设用地(包括城镇用地、农村居民点、其他建设用地)、林地、草地、耕地、水域、未利用土地等类别，分类结果如图 5所示。
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图 5 影像分类结果a—研究区位置图；b—T2影像分类结果；c—T1影像水体提取结果Figure 5. The classification results of remote sensing images最后对分类结果进行精度评价，发现Kappa系数很高，结果准确度较高(表 1)。
表 1 分类精度Table 1. Accuracy of classification影像类型 总体精度 Kappa系数 T2影像 90.49% 0.8506 T1影像 96.12% 0.94 对2期影像分类结果进行叠加分析，得到T1影像表现为水体而T2影像表现为非水体的图斑，作为暗浜空间范围(图 6)。
对暗浜图斑的属性进行赋值，分析得到研究区暗浜幼前期的水体目前转变为何种地物类别(图 7)。
3.2 微动探测试验结果
基于变化检测结果，选取2个区域，布设微动探测剖面3条，共计140个点，采用直线型布阵方式进行测量，台阵半径1 m-2 m-4 m，采样频率250 Hz，测量时间20 min，点距8 m。采用标准化流程进行数据处理，并根据收集的钻探成果对其过程参数进行修正，在总体与钻探成果较为吻合后确定最终的微动速度-深度剖面图，并推断地质断面图，如图 8所示。根据图 8-a并结合遥感图像可知，1~5号点为深色暗浜或水田区域，11~60号点为暗浜区域，61~71号点为深色暗浜或水田区域。图 8-b为测线1推断地质断面图，10 m以浅的低速异常推测为暗浜分布区域，速度范围在100~300 m/s之间，变化检测结果基本一致。同时，按剖面图 8-c中100~300 m/s速度范围区分暗浜，发现15 m以浅的低速异常为暗浜分布区域，由于该微动测线布设靠近暗浜中心，故而低速异常深度相比图 8-a稍大。
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图 8 不同测线微动速度-深度剖面和推断地质断面图(a、c、e分别为测线1、测线2、测线3微动速度-深度剖面图，b、d、f分别为测线1、测线2、测线3推断地质断面图)Velocity-depth profile of microtremor survey and inferred geographical profile of different line由图 8-e分析，发现在14号点、22号点、26号点存在局部的低速异常，根据变化检测结果可知，在2004年时15~22号点都属于暗浜范围(图 8-f)，至2018年地物地貌变化极大，暗浜基本被回填。
4. 讨论
利用本文的算法，提取出研究区暗浜面积9.86 km2，从空间分布看，大部分暗浜位于安庆市辖区南部沿江地区，包括宜秀区、十里铺乡、老峰镇、白泽胡乡、山口乡，暗浜表面主要地物类型为建筑物(表 2)。整体看，有89.15%的暗浜区域，其地表覆盖类型目前为建设用地，如道路、桥梁、房屋等，其中居民点占42.35%，其他建设用地占46.80%。这跟安庆市近十五年的发展建设一致，城市的扩张使得不透水层面积急剧增加，以前的很多小池塘沟渠被填埋，用以城市开发建设。此外，暗浜区域地表覆盖类型为耕地的面积为0.69 km2，占暗浜总面积的6.97%，一些湖泊被人为造田，使水域面积减少。
表 2 暗浜空间范围分布面积Table 2. Area statistics of underground siltkm2 名称 建设用地 耕地 林地 草地 未利用土地 暗浜总计 宜秀区 2.3368 0.0486 0.0053 0.1844 — 2.5751 十里铺 2.0097 0.0064 0.0510 0.0008 — 2.0679 老峰镇 1.6146 0.3560 0.0010 0.0050 0.0093 1.9859 白泽湖 0.5841 0.0622 — — — 0.6463 山口乡 0.5852 — 0.0186 0.0010 — 0.6048 长风乡 0.5028 0.0607 0.0016 0.0067 — 0.5718 龙狮乡 0.5120 0.0496 — — — 0.5616 大龙山 0.3972 0.0021 0.0055 0.0159 — 0.4207 大观区 0.0932 — 0.0007 0.0000 — 0.0939 月山镇 0.0716 — 0.0072 0.0035 0.0007 0.0830 海口镇 0.0003 0.0659 0.0000 0.0000 — 0.0662 迎江区 0.0184 0.0268 0.0045 0.0138 — 0.0635 菱北街 0.0306 — — 0.0010 — 0.0316 杨桥镇 0.0049 — 0.0180 0.0024 — 0.0253 鲟鱼镇 0.0225 — 0.0009 0.0000 — 0.0234 五横乡 — — 0.0202 0.0015 — 0.0217 新洲乡 0.0090 0.0091 — 0.0018 — 0.0199 总计 8.7929 0.6874 0.1345 0.2378 0.0100 9.8626 基于变化检测结果及微动探测实地验证，推断地质断面，能够勘察得到暗浜的深度和边界。经实地验证，发现布设的3处测线均存在暗浜，填充物较复杂，填充不均匀，且2处由于地表城市规划已开始施工作业，导致微动剖面速度特征杂乱；在填充物简单、地表未进行人类工程活动时，微动探测应用效果更好。
5. 结论
遥感技术的发展为地质调查提供了极大的便利。本文将遥感与物探手段结合，首先利用遥感变化检测技术监测暗浜分布范围，为实施物探划定出靶区或重点区域，这样既能极大程度节约成本，又能提高暗浜检测的准确性。首先从遥感数据入手，利用变化检测方法自动提取安庆市城区暗浜的空间位置与范围，共提取暗浜面积9.86 km2，且发现约89%的区域目前已为建设用地，且建设用地中超过一半的区域为道路、桥梁等工矿用地，留有工程隐患的可能性很大。然而，利用遥感影像提取到的暗浜是二维的空间分布形态，无法得到暗浜位置准确的深度信息。本文基于变化检测技术监测暗浜分布范围，布设3条测线进行了实地勘察，验证了变化检测结果的可靠性。本文的暗浜提取方法与结果能够为城市工程建设与规划提供决策支持，具有十分重要的意义。
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图 1 北山造山带大地构造位置及构造单元划分简图（据Song et al., 2024修改）
Figure 1. Tectonic location and sketch map of Beishan orogenic belt showing division of tectonic units
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图 2 北山造山带中部勒巴泉地区变质硅质岩中紧闭褶皱（a）和变质碎屑岩中的无根钩状褶皱（b）
Figure 2. Tight folds in metachert (a) and rootless folds in metaclastic rock (b) at the Lebaquan area, central Beishan orogenic belt
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