南秦岭长角坝群低庄沟组的锆石U-Pb年龄及其构造意义

刘志慧; 刘晓春; 陈龙耀; 郑光高; 胡娟

doi:10.12090/j.issn.1006-6616.2024027

南秦岭长角坝群低庄沟组的锆石U-Pb年龄及其构造意义

doi: 10.12090/j.issn.1006-6616.2024027

刘志慧^{1, 2,},
刘晓春^{1, 2},
陈龙耀^{1, 2},
郑光高^{1, 2},
胡娟^{1, 2}

1.
中国地质科学院地质力学研究所，北京 100081
2.
自然资源部古地磁与古构造重建重点实验室，北京 100081

基金项目: 国家自然科学基金项目（42202057，41872059）；中国地质调查局地质调查项目（DD20240031）

详细信息

作者简介:
刘志慧（1987—），男，博士，助理研究员，主要从事变质岩石学方面研究。Email：zhliu809@163.com

中图分类号: P534；P597
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出版历程
- 收稿日期: 2024-03-15
- 修回日期: 2024-07-10
- 录用日期: 2024-07-18
- 预出版日期: 2024-07-25
- 刊出日期: 2024-12-27

Zircon U-Pb dating of the Dizhuanggou Formation, Changjiaoba Group in the South Qinling Belt and its tectonic significance

LIU Zhihui^{1, 2
,},
LIU Xiaochun^{1, 2},
CHEN Longyao^{1, 2},
ZHENG Guanggao^{1, 2},
HU Juan^{1, 2}

1.
Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China
2.
Key Laboratory of Paleomagnetism and Tectonic Reconstruction, Ministry of Natural Resources, Beijing 100081, China

Funds: This research is financially supported by the National Natural Science Foundation of China (Grants No. 42202057 and 41872059) and Geological Survey Project of China Geological Survey (Grant No. DD20240031).

摘要

摘要: 长角坝群出露于南秦岭佛坪地区，是该构造带内残留的少数研究程度较低的地层之一，其物质组成、形成时代一直缺乏准确的限定，进而制约了对南秦岭的构造归属和构造演化的深入研究。文章对长角坝群内低庄沟组变质沉积岩开展了岩石学和锆石U-Pb年代学研究，结果显示所取2个样品的碎屑锆石年龄主要峰值为810~835 Ma, 最年轻的年龄区间为600~700 Ma，最大沉积时代为新元古代。这与同为长角坝群、出露最广泛的泥盆纪黑龙潭组具有明显的时代差异，显示了长角坝群物质组成的复杂性。此外，低庄沟组的碎屑锆石年龄谱系特征与用于对比测试的另一个碎屑锆石最小年龄峰值为718 Ma、主要峰值为810 Ma的佛坪群样品高度相似，结合二者的岩石学及野外地质特征，认为二者共同构成了南秦岭西部的过渡性基底，与南秦岭带内宁陕断裂以东的过渡性基底−武当群、耀岭河群具有明显的可对比性。长角坝群新元古代物质的识别还为佛坪地区变质单元的划分提供了新的依据，进而梳理出成层性、变质程度不同的3个单元，为南秦岭中生代造山演化过程的恢复提供了佐证。
- 南秦岭 /
- 长角坝群 /
- 碎屑锆石 /
- 新元古代 /
- 单元划分
Abstract: Objective The Changjiaoba Group, located in the Foping area of the South Qinling Belt, is one of the few poorly-studied strata in the belt. The lack of an accurate composition and formation age of these strata has restricted research on the tectonic affinity and evolution of the South Qinling Belt. Method This paper investigates the petrological characteristics and zircon U-Pb chronology of two metasedimentary rocks from the Dizhuanggou Formation of the Changjiaoba Group. Results The results show that the dominant peak detrital zircon dates of the two samples were approximately 810–835 Ma, with the youngest date range being approximately 600–700 Ma, indicating that the maximum depositional age was Neoproterozoic. This age spectrum differs significantly from that of the most exposed Devonian Heilongtan Formation of the Changjiaoba Group, but is highly similar to that of another sample from the Foping Group, which has a minimum age peak of 718 Ma and a major peak at 810 Ma. In addition, metamorphic zircons from the Dizhuanggou Formation and the Foping Group yielded ages of 207 Ma and 193 Ma, respectively. Conclusion The distinct depositional age of the Changjiaoba Group indicate the complexity of its composition. Combined with the petrological and field geological features, the Dizhuanggou Formation and a Neoproterozoic strata of the Foping Group are considered to form the transitional basement of the western South Qinling Belt. This is comparable to the transitional basement of the Wudang and Yaolinghe Groups east of the Ningshan Fault in the South Qinling Belt. Significance The identification of Neoproterozoic and Mesozoic materials in the Changjiaoba Group provides a new basis for the division of metamorphic units in the Foping area, identifying three units with different degrees of layering and metamorphism, which, in turn, facilitates understanding of the Mesozoic orogenic process in the South Qinling Belt.
- South Qinling Belt /
- Changjiaoba Group /
- detrital zircon /
- Neoproterozoic /
- unit division

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0. 引言

遥感技术在基础地质工作中应用越来越广泛，但是由于植被等因素的干扰，难以将植被覆盖下的信息从图像中分离出来，植被是遥感对地表探查的一道天然屏障。在基岩被植被完全覆盖的地区，遥感所获得的信息主要是植被信息，给遥感地质勘查工作带来极大不便。因此，找到一种可行的植被剔除方法，还原植被覆盖下的基岩信息，可更好地发挥遥感技术在基础地质工作中的应用能力。

过去研究人员主要基于像元的植被指数对植被等干扰信息进行提取，但这种基于像元的模式不能有效地提高提取的精度。因为遥感图像中每个像元一般是多个地物的混合体，一个像元里记录了多类不同性质的地面目标，图像的光谱特征也是多个地物光谱特征的混合反映。混合像元的存在，产生了同物异谱、同谱异物现象，从而给地物的分类造成许多困难。因此，为了提高干扰信息提取的精度，就必须找到一种有效的进行混合像元分解的方法。线性光谱混合模型（Linear Spectrum Mixture Model，LSMM）是目前应用较多的混合像元分解方法^[1~2]，但LSMM因为要对整幅图像的每个像元都进行处理，所以处理速度较慢。

蚁群算法是由意大利学者Dorigo等人，通过模拟自然界蚂蚁寻径的行为提出的一种全新仿生进化算法^[3~4]，是具有离散性、并行性、鲁棒性、正反馈性等特点的一种随机搜索方法。由于其概念简明、实现方便，迅速得到认可，并在优化问题求解、电力系统、计算机、冶金自动化等领域都有成功的应用^[5]。但目前很少有人将蚁群算法引入遥感地质领域，由于遥感地质领域的信息提取也可以看作是一个组合优化问题，蚁群算法的全局性、离散性和基于概率选择路径等特点对于遥感图像非常适用。

本文为了剔除植被干扰信息，综合考虑LSMM处理速度慢而蚁群算法识别目标速度快的特点，结合蚁群算法和线性光谱混合模型，建立基于蚁群搜索的光谱分解模型，剔除植被干扰信息，重构不含有植被信息的新的多波段图像，以期为后续基础地质工作提供基础影像。

1. 研究方法

1.1 蚁群算法基本原理

对于采用整数编码求解组合优化问题的蚁群系统，设求解问题因素有N个，蚁群中共有M只蚂蚁，τ_ij(t)表示在t时刻i和j组合之间信息素的数量。蚂蚁m在运动过程中根据各个路径上信息素的数量决定下一步的路径。用p_ij^m(t)表示在t时刻蚂蚁m由城市i转移到城市j的概率，则：

$p_{ij}^m\left( t \right) = \left\{ \begin{array}{l} \frac{{\tau _{ij}^\alpha \left( t \right) \cdot \eta _{ij}^\beta \left( t \right)}}{{\sum\nolimits_{r \in {T_m}} {\tau _{ir}^\alpha \left( t \right) \cdot \eta _{ir}^\beta \left( t \right)} }}\;\;\;\;\;j \in {T_m}\\ \;\;\;\;\;\;\;\;\;0\;\;\;\;\;\;\;\;\;\;otherwise \end{array} \right.$

(1)

其中：T_m表示蚂蚁m下尝试组合情况的集合，该集合随蚂蚁m的行进过程而动态改变。

信息量τ_ij(t)随时间的推移会逐步衰减，用1-ρ表示它的衰减程度。经过n个时刻，要根据下式对各路径上的信息量作更新：

${\tau _{ij}}\left( {t + 1} \right) = \rho \cdot {\tau _{ij}}\left( t \right) + \Delta {\tau _{ij}}$

(2)

$\Delta {\tau _{ij}} = \sum\limits_{s = 1}^M {\Delta \tau _{_{ij}}^k}$

(3)

Δτ_ij^k表示蚂蚁m在本次循环中在组合i和j之间留下的信息量，其计算方法根据蚁群系统的计算模型而定。

1.2 蚂蚁移动规则

正如Chialvo^[6]和Millonas^[7]所描述的，单只蚂蚁的状态可以用位置参数r和方向参数θ来表示。蚂蚁的状态转移规则可用一个加权函数表示：

$w\left( \sigma \right) = {\left[{1 + \frac{\sigma }{{1 + \delta \sigma }}} \right]^\beta }$

(4)

这个方程描述了移动到信息素浓度为σ(r)的像素r处的相对概率。参数β表示一种随机度。β大，则w(σ)值较大，蚂蚁以较大的权系数跟随外激素浓度大的路径；反之，对蚂蚁路径选择影响不大。1/δ表示了蚂蚁感知外激素的能力。

蚂蚁从像元k运动到像元i的归一化转移概率定义为：

${p_{ik}} = \frac{{w\left( {{\sigma _i}} \right)w\left( {{\Delta _i}} \right)}}{{\sum\nolimits_{j/k} {w\left( {{\sigma _j}} \right)w\left( {{\Delta _j}} \right)} }}$

(5)

w(Δ)是一加权因子，Δ_j表示蚂蚁在t-1时刻运动时的方向改变量，它的取值为8个离散的w值，一般蚂蚁的来向w定为1/20，原路径方向定为1，邻近的8个像元顺时针依次为1/20，1/12，1/4，1/2，1，1/2，1/4，1/12。

蚂蚁爬行时，先计算8邻域每个像元的转移概率p_ik，然后根据轮赌算法选择移动到下一个像元。为了有利于全局搜索最优解，避免陷入局部最优解，设定了蚂蚁的兴奋阈值，当某支蚂蚁达到该值时，采用随机算法从8邻域中选择一个像元进行移动。

1.3 线性光谱混合模型

线性光谱混合模型（Linear Spectrum Mixture Model，LSMM）主要的目的是绘制特定像元内地面物质的相对丰度图。因此，它必须要满足几个假设:

① 在一个像元里端元的混合必须是线性的。这种线性关系下光子在从太阳到传感器的路径上仅仅与一种端元成分交互，这要求端元组分分布在足够大的面积上。

② 在图像上所有的地物类型必须要有足够多的对照光谱，以便对它们进行分离。

③ 在图像里存在的端元组分必须在最少一个像元里保持纯状态，这个像元必须能正确识别且可空间定位。

LSMM假定传感器测量的光谱是像元内所有成分光谱的线性组合^[1]。LSMM模型描述如下：

${R_i} = \sum\limits_{k = 1}^n {{f_k}{R_{ik}} + {\varepsilon _i}}$

(6)

在这里i为光谱波段的数目；k是端元的数目；R_i是像元波段i的光谱反射率；f_k是像元内端元k的面积比；R_ik是波段i端元k的光谱反射率；ε_i是波段i的模拟误差。

f_k受下式约束:

$\sum\limits_{k = 1}^n {{f_k}} = 1\;\;\;且\;\;\;0 \le {f_k} \le 1$

(7)

残差RMS用下式计算:

$RMS = \sqrt {\left( {\sum\limits_{i = 1}^m {\varepsilon _i^2} } \right)/m}$

(8)

RMS残差要对所有图像像元都计算。RMS越大，模型的匹配就越差。所以残差图可以用来估计选择的端元是否适当，选择的端元数目是否足够。

因为在LSMM模型中，端元反射率和像元反射率都是已知的，只有端元在像元里的面积比未知，只要波段的数目大于端元数目，就可以求出端元的面积比，从而得到各分量图像和残差图像，进而得到地物的分类图像。

LSMM的优点在于它相对简单，并且是一种有物理意义的丰度测量方法；理论上有较好的科学性，对于解决像元内的混合现象有一定的效果。

2. 基于蚁群算法的光谱分解算法

本文中基于蚁群算法的光谱分解算法采用的是混合像元分解蚁群算法，输入经过反射率转换预处理后的影像，输出不含有植被信息的新的多波段图像。

【算法开始】

① 初始化

1) 蚁群及相关参数；

2) 信息素矩阵；

3) 访问标志矩阵P；

4) 残差矩阵RMS；

5) 丰度矩阵F；

② 迭代，直到达到停止条件

1) 评价每只蚂蚁，对每只蚂蚁做

如果没有访问过当前像元则完成下述工作

(1) 建立端元系数矩阵x；（矩阵x主要存储像元内各子端元的面积）

(2) 建立当前像元波段矩阵y；（矩阵y主要存储当前像元各波段的光谱反射率值）

(3) 调用最小二乘法计算各端元的比例系数矩阵a；（重新计算各子端元的面积）

(4) 对矩阵a进行负数漂移处理；

(5) 对矩阵a进行归一化处理；

(6) 计算该点残差存放到RMS矩阵；

(7) 记录丰度信息到F；

(8) 根据矩阵a调整该像元的各波段值；

(9) 设置当前像元访问标志；

(10) 如果剔除端元所占比例超过某阈值，调整（增加）信息素；

2) 每只蚂蚁爬行，做

如果蚁群迭代到某兴奋阈值则

蚂蚁采用随机算法从8邻域中选择一个像元进行移动

否则

(1) 根据公式（4）计算8邻域每个像元的w(σ)

(2) 根据进入方向分配8邻域每个像元的权值

(3) 根据公式（5）计算8邻域每个像元的转移概率p_ik

(4) 按照轮赌算法选择移动到下一个像元

③ 按照BSQ格式保存图像数据；

④ 按照BSQ格式保存残差矩阵；

⑤ 按照BSQ格式保存剔除专题的丰度矩阵；

【算法结束】

3. 试验及结果分析

选取青海黄南州吉地地区为试验区，该区为植被高度覆盖区。试验区遥感数据为Landsa7号卫星的ETM，景号为132-36，2000年7月18日获取的影像，太阳高度角为64.2°。试验区图像大小为408×402像素，经过反射率转换预处理后的影像作为输入图像。基于ENVI3.6及VC++6.0开发的蚁群光谱分解软件进行试验。

通过求取植被指数，可以发现该区植被覆盖度为85.7157%（见图 1，绿色部分为植被），高植被覆盖率严重影响了矿化弱信息的提取。选用ETM1，2，3，4，5，7等6个波段进行主成分变换，其中PC1和PC2占95.3%。通过主成分分析，选用PC1和PC2制作二维散点图（见图 2）。通过散点图分析，确定图像上主要有植被及3种未知类别共4个端元。运行蚁群光谱分解软件，最后得到剔除植被后影像（见图 3）及残差图（见图 4）。

图 1 预处理后影像（ETM7+ETM 4+ETM 1）

Figure 1. The image after pre-processing

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图 2 散点图

Figure 2. Scatter diagram

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图 3 剔除植被后影像（band 7+band 4+band 1）

Figure 3. The image after eliminating vegetation

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图 4 残差图（最大残差为0.103）

蓝色：0~0.025；绿色：0.025~0.050；黄色：0.050~0.075；红色：0.075~0.103

Figure 4. The residual map

下载: 全尺寸图片幻灯片

从残差图上可以看出，最大残差为0.103，绝大部分残差在0.05以下。说明光谱分解的效果较好。对比图 2和图 3可以发现，进行植被剔除后，原来绿色部分的植被覆盖区被还原为该区本来的面目。

4. 结论

基于蚁群算法的光谱分解方法剔除植被信息，首次将蚁群这种全新的算法引入到遥感地质领域，综合考虑传统的光谱分解植被剔除方法处理速度慢和蚁群算法识别目标速度快的特点，通过残差图分析以及原图与剔除植被后影像对比分析，初步验证了基于蚁群算法的光谱分解方法来剔除植被信息的可行性。

由于蚁群算法的理论体系还有待进一步完善，所以用这种全新的算法来处理遥感地质领域的一些复杂问题，还有许多工作要做。如蚁群的初始参数怎样优化，才能取得理想的效果，有待于进一步深入研究。

图 1 研究区位置图

T—三叠纪；D—泥盆纪；Pt—元古宙a— 秦岭造山带大地构造略图（据张国伟等，2001；李三忠等，2003；Dong et al.，2011；Zhao and Cawood，2012；Hu et al.，2016修改）; b— 佛坪地区地质简图（据陕西省地质矿产局，1999修改）

Figure 1. Location of the study area

(a) Simplified tectonic map of the Qinling orogenic belt (modified after Zhang et al., 2001; Li et al., 2003; Dong et al., 2011; Zhao and Cawood, 2012; Hu et al., 2016); (b) Geological sketch map of the Foping area (modified after Brueau of Geology and Mineral Resources of Shaanxi Province, 1999)T—Triassic; D—Devonian; Pt—Proterozoic

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图 2 佛坪地区新元古代岩石样品野外露头照片及显微镜单偏光照片

Grt—石榴石；Bt—黑云母；Sill—矽线石；Pl—斜长石；Q—石英a— 长角坝群低庄沟组样品FP105-3野外照片；b— FP105-3单偏光照片；c—长角坝群低庄沟组样品FP141-5野外照片；d— FP141-5单偏光照片；e— 佛坪群样品FP110-5野外照片；f— FP110-5单偏光照片

Figure 2. Photographs and photomicrographs of the Neoproterozoic samples in the Foping area

(a) Outcrop of sample FP105-3 from the Dizhuanggou Formation; (b) Photomicrograph of sample FP105-3; (c) Outcrop of sample FP141-5 from the Dizhuanggou Formation; (d) Photomicrograph of sample FP141-5; (e) Outcrop of sample FP110-5 from the Foping Group; (f) Photomicrograph of sample FP110-5 Grt—garnet; Bt—biotite; Sill—sillimanite; Pl—plagioclase; Q—quartz

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图 3 佛坪地区新元古代样品碎屑锆石的阴极发光（CL）图像

红色圆圈为锆石测点位置，黄色年龄数据来自岩浆锆石，蓝色年龄数据来自变质锆石a— 长角坝群低庄沟组样品FP105-3的碎屑锆石特征；b— 长角坝群低庄沟组样品FP141-5的碎屑锆石特征；c—佛坪群样品FP110-5的碎屑锆石特征

Figure 3. Cathodoluminescence (CL) images of detrital zircons from the Neoproterozoic samples

(a) FP105-3 from the Dizhuanggou Formation, Changjiaoba Group; (b) FP141-5 from the Dizhuanggou Formation, Changjiaoba Group; (c) FP110-5 from the Foping Group Red circles are analytical spots, yellow dates numbers are from magmatic zircons, while blue dates numbers are from metamorphic zircons.

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图 4 佛坪地区新元古代样品碎屑锆石U-Pb年龄谐和图与频率分布直方图

a—样品FP105-3的碎屑锆石U-Pb年龄谐和图；b—样品FP105-3的碎屑锆石U-Pb年龄频率分布直方图；c—样品FP141-5的碎屑锆石U-Pb年龄谐和图；d—样品FP141-5的碎屑锆石U-Pb年龄频率分布直方图；e—样品FP110-5的碎屑锆石U-Pb年龄谐和图；f—样品FP110-5的碎屑锆石U-Pb年龄频率分布直方图

Figure 4. U-Pb concordia and age probability density diagrams of detrital zircons from the Neoproterozoic rock samples in the Foping area

(a) Zircon U-Pb concordia diagrams of sample FP105-3; (b) Zircon U-Pb age histogram probability density diagrams of sample FP105-3; (c) Zircon U-Pb concordia diagrams of sample FP141-5; (d) Zircon U-Pb age histogram probability density diagrams of sample FP141-5; (e) Zircon U-Pb concordia diagrams of sample FP110-5; (f) Zircon U-Pb age histogram probability density diagrams of sample FP110-5

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图 5 研究区及其相邻区域年代学数据频谱对比图

a—佛坪地区新元古代地层变质沉积岩碎屑锆石U-Pb年龄频率分布直方图（数据来源于刘志慧等，2018及文中）；b— 南秦岭东段新元古代地体变质沉积岩碎屑锆石U-Pb年龄频率分布直方图（数据来源于李怀坤等，2003、凌文黎等，2007，2010；祝禧艳等，2008；张永清等，2013；王嘉玮等，2021）；c— 扬子地区火成岩结晶年龄（频率直方图及数据引自Zhang et al.，2023a）

Figure 5. Age probability density diagrams of the samples from the study area and its adjacent regions

(a) Ages of detrital zircons from the Neoproterozoic metasedimentary rocks in the Foping area (data are from this paper and Liu et al., 2018); (b) Ages of detrital zircons from the Neoproterozoic metasedimentary rocks in the eastern South Qinling area (data are from Li et al., 2003; Ling et al., 2007, 2010; Zhu et al., 2008 and Zhang et al., 2013); (c) Igneous ages from Yangtze block (diagram and data quoted from Zhang et al., 2023a)

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0. 引言
1. 研究方法
1.1 蚁群算法基本原理
1.2 蚂蚁移动规则
1.3 线性光谱混合模型
2. 基于蚁群算法的光谱分解算法
3. 试验及结果分析
4. 结论

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南秦岭长角坝群低庄沟组的锆石U-Pb年龄及其构造意义

doi: 10.12090/j.issn.1006-6616.2024027

作者简介:
刘志慧（1987—），男，博士，助理研究员，主要从事变质岩石学方面研究。Email：zhliu809@163.com

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Zircon U-Pb dating of the Dizhuanggou Formation, Changjiaoba Group in the South Qinling Belt and its tectonic significance

0. 引言

1. 研究方法

1.1 蚁群算法基本原理

1.2 蚂蚁移动规则

1.3 线性光谱混合模型

2. 基于蚁群算法的光谱分解算法

3. 试验及结果分析

4. 结论

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目录

0. 引言

1. 研究方法

1.1 蚁群算法基本原理

1.2 蚂蚁移动规则

1.3 线性光谱混合模型

2. 基于蚁群算法的光谱分解算法

3. 试验及结果分析

4. 结论

留言板

南秦岭长角坝群低庄沟组的锆石U-Pb年龄及其构造意义

doi: 10.12090/j.issn.1006-6616.2024027

作者简介: 刘志慧（1987—），男，博士，助理研究员，主要从事变质岩石学方面研究。Email：zhliu809@163.com

计量

出版历程

Zircon U-Pb dating of the Dizhuanggou Formation, Changjiaoba Group in the South Qinling Belt and its tectonic significance

0. 引言

1. 研究方法

1.1 蚁群算法基本原理

1.2 蚂蚁移动规则

1.3 线性光谱混合模型

2. 基于蚁群算法的光谱分解算法

3. 试验及结果分析

4. 结论

计量

出版历程

目录

0. 引言

1. 研究方法

1.1 蚁群算法基本原理

1.2 蚂蚁移动规则

1.3 线性光谱混合模型

2. 基于蚁群算法的光谱分解算法

3. 试验及结果分析

4. 结论

作者简介:
刘志慧（1987—），男，博士，助理研究员，主要从事变质岩石学方面研究。Email：zhliu809@163.com