HE Yang, XU Tao, SONG Yun, 2015. APPLICATION OF IN-SITU MEASURED SPECTRA DATA BY ASD FieldSpec3 ON LITHOLOGIC CLASSIFICATION IN SHUIMO-DAHE AREA. Journal of Geomechanics, 21 (1): 21-29, 72.
Citation: LYU Y,2024. Variation patterns of boron and lithium isotopes in salt lakes on the Qinghai–Tibetan Plateau and their application in evaluating resources in the Damxung Co salt lake[J]. Journal of Geomechanics,30(1):107−128 doi: 10.12090/j.issn.1006-6616.2023135

Variation patterns of boron and lithium isotopes in salt lakes on the Qinghai–Tibetan Plateau and their application in evaluating resources in the Damxung Co salt lake

doi: 10.12090/j.issn.1006-6616.2023135
Funds:  This research is financially supported by the National Natural Science Foundation of China (Grants No. 42273018 and 41673023).
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  •   Objective  The Qinghai–Tibet Plateau is rich in salt lake resources, known particularly for the concentration of elements such as boron and lithium, forming many distinctive resource-type salt lakes. Compared with ordinary salt lakes, a notable characteristic of resource-type salt lakes is the abundant supply of elements such as boron and lithium. Consequently, these elements' sources and accumulation patterns are key scientific issues for understanding the genesis and mineralization patterns of resource-type salt lakes. Boron and lithium isotopes, characterized by significant mass differences and variations in natural isotope ratios, serve as effective tracers for studying the material sources of boron and lithium in salt lakes. However, the application of boron and lithium isotopes in salt lake systems faces the following three challenges: (1) There is insufficient understanding of how boron and lithium isotopes respond to the fundamental geochemical processes of salt lakes. The salt dissolution process that occurs when supply water flows into lake basins is the main reason for drastic changes in geochemical parameters. Inadequate recognition of salt dissolution processes can lead to an overinterpretation of boron and lithium isotope fractionation changes, weakening their tracking capabilities. (2) Isotope fractionation degree is conflated with changes in isotope composition. In salt lake research, discussions of the solid phase's influence on boron and brine's lithium isotopes are often based solely on fractionation factors between the solid and liquid phases, without considering the ratios of boron and lithium amounts involved in the fractionation process. (3) Discrepancies still exist in understanding the fractionation patterns of boron and lithium isotopes during salt crystallization.  Methods  In light of these problems, our study systematically reviews and analyzes the mechanisms of boron and lithium isotopic fractionation in salt lake systems and summarizes some essential understandings.  Conclusion  (1) Only salt crystallizations have specific impacts on B and Li isotopes in salt lakes. Since there is a genetic association between salt assemblages and specific salt lake hydrochemical types, the salt lakes with the same hydrochemical type exhibit consistent patterns of B and Li isotope changes during their evolutionary processes. Until halite precipitation, the B and Li isotopic compositions in sulfate- and chloride-type salt lakes are in accord with δ11B and δ7Li values of their sources instead of being controlled by their salt deposits. In contrast, the paths of B and Li isotopic changes of carbonate-type salt lakes are complex and are divided into two branches: calcite subtype and hydromagnesite subtype. After calcite precipitation, the δ11B value of the salt lake increases, and its δ7Li value is marginally above source characteristics (less than 2‰). After hydromagnesite precipitation, the δ11B value of the salt lake is also marginally above source characteristics (less than 2‰). After the stage of halite precipitation, the B and Li isotopic compositions of salt lakes in all types show an increasing trend. (2) Based on the evolutionary processes of B, Li, and K during seawater evaporation, the amounts of B, Li, and K in the current salt lake represent most of the corresponding resources in the lake if the salt lake never experienced complete dryness such as playa. For the salt-dissolving lake, most of the B, Li, and K resources are preserved in salt deposits and interstitial brine at the bottom of the lake. It is optimal for the resource potential of a carbonate-type salt lake in the salt-dissolving lake. (3) The B sources of the current Damxung Co salt lake located in the Tibetan Plateau are from clay carbonates exposed to the lake shore and highly soluble salts and interstitial brine at the bottom of the lake. The geothermal waters produced during early hydrothermal activity are the original B source of the Damxung Co salt lake. Based on mass balance equations, it is estimated that the B resource at the bottom of the Damxung Co salt lake is at least 9.1×106t (B2O3), and the lithium resource is at least 8.6 ×106t (LiCl).

     

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  • 随着地物光谱仪的发展与普及,基于岩矿光谱数据测量和特征分析的遥感勘查技术已成为地质工作的重要技术之一。地物光谱数据作为遥感数据分析的重要支撑,可帮助理解各种地物的波谱特性和提高不同种类遥感数据的分析应用精度。1990年,童庆禧等[1]通过大量地物光谱数据的测量和分析,总结出我国277种典型地物波谱及其特征;2000年,王青华等[2]利用MAIS成像光谱仪对河北张家口地区岩石类型进行了较准确的分类;2008年,曹烨等[3]利用便携式短波红外光谱矿物测量仪(PIMA)研究了河南前河金矿区的蚀变矿物种类,并对相对含量大于5%的6种蚀变矿物进行了矿物学填图。随着高光谱技术的快速发展,矿物识别、矿物填图已是遥感地质定量化研究的最重要的研究发展方向[4]

    本文利用ASD FieldSpec 3便携式地物光谱仪,对四川省旺苍县水磨-大河地区63个测点的10余种代表性岩矿进行了波谱数据测量,采用ENVI4.4软件对实测波谱数据进行处理和转换,建立了研究区岩矿光谱数据库,并以此为参考光谱数据运用光谱角填图、矿物指数法、光谱匹配滤波、人机交互解译调整等方法,对研究区ASTER影像进行岩矿信息的提取,结果表明对该区岩性的划分达到了理想效果。该方法精度高、经济实用、可操作性强,具有广阔的应用前景。

    研究区位于四川盆地北缘,大巴山西端南坡,区域面积约为171.2 km2。该区属中亚热带湿润季风气候,雨量充沛,光热资源丰富,植被覆盖度高,地貌复杂,平均相对海拔1399 m,为典型的高中山峡谷地貌(见图 1)。该区属扬子准地台北缘,跨及川中坳陷区及地台北缘坳陷褶皱带2个二级构造单元,经历了强烈的拉张、俯冲、碰撞、走滑等复杂地质演化过程以及频繁的构造运动和强烈的岩浆活动。主要出露地层有中—上元古界火地垭群麻窝子组、震旦系灯影组、寒武系筇竹寺组、奥陶系宝塔组等。区内岩浆活动频繁,岩石类型多样,分带性较明显,岩性主要为闪长岩、石英闪长岩、辉长岩等[5]

    图  1  研究区地貌及实测路线图
    Figure  1.  Geomorphology and measured route of research area

    光谱测量时间直接关系到数据采集的质量,结合该区地理环境情况,本次测量工作选择每天早上10:00至下午14:00,期间光照条件良好,太阳高度角与方位角适宜,能见度高,大气中的CO2、H2O等气体分子和气溶胶、灰尘微粒等含量小且相对稳定,风力小于3级,无浓密云和卷云,保障了数据采集的精度,使测得的光谱数据尽量反映岩矿本身的光谱特性,确保所测结果准确可靠。

    测量路线的选择是数据采集工作中极为重要的部分,本文充分考虑测区地形地貌、岩矿区域分布特征、植被覆盖等因素,选择构造简单、接触关系清楚、地层连续、露头较好且具有区内岩矿类型典型代表性的5条路线作为本次光谱测量工作的测量路线(见图 1)。对区内典型岩矿类型进行实地光谱测量,包括砂岩、灰岩、白云岩、千枚岩、大理岩、闪长岩、霓霞岩等,完成了风化面、新鲜面等多条件下的光谱测量工作。测量路线总长度达35.9 km(邻区25.9 km),涵盖了63个测点(邻区28个)及10余种岩矿类型。

    由于野外实测岩矿光谱数据所受干扰因素较多,如随机误差、大气影响、仪器本身性能等,本次工作对光谱数据进行了处理及各种转换,以消除噪声并突出地物光谱的某些细微差别。

    光谱测量中为了避免探测目标范围和目标局部特征的随机影响误差,采取多次测量取平均值的方法(一般每个样本重复测量6次),在检查每次测量结果时,如果有信号跳变的现象,要予以剔除重测。

    光谱野外实测过程中,由于大气中水汽的吸收,地面光谱和遥感数据在水汽吸收波段基本都为噪声,光谱会在局部(大气吸收带中心)呈现不同程度的跳变,需要加以分析并剔除。

    由于光谱仪不同波段间能量上的差异,导致光谱特征曲线上呈现一些随机噪声信号,为得到平稳与概略的变化,需平滑波形以去除包含在信号内的少量噪声[6]。本次采用静态平均法,使用低通滤波保留低频部分的同时消除高频部分,达到平滑和去噪的目的。

    从ASD FieldSpec 3地物光谱仪导出63个“.mn”格式的实测光谱数据,对所有光谱点曲线导入,建立光谱库,并依据ASTER各个波段的中心波长,对光谱库中所有曲线进行重采样,采样前后的光谱库见图 2。光谱库的建立为该区岩性分类提供了数据支撑。

    图  2  野外光谱数据库采样前后
    (上为采样前,下为采样后)
    Figure  2.  Sampling contrast of spectral database in the field

    ASTER是NASA(美国国家航空航天局)与METI(日本经济贸易产业省)合作并由两国的科学界、工业界参与的项目。它作为一种高级光学传感器搭载于1999年12月发射升空的Terra卫星之上,有可见光近红外、短波红外、热红外3个谱段,几乎覆盖了光学遥感所有大气窗口的谱段,专门为地质应用和火山监测而设计。ASTER卫星传感器数据的显著特点在于短波红外范围的波段数目更多、单波段波长间隔更窄以及热红外设置多波段,在识别和提取岩石、矿物信息方面有明显的优势[7]

    大气校正的目的是消除大气和光照等因素对地物反射率的影响,是一个反演地物真实反射率的过程,影像上地物的光谱反射率曲线与实地对应的真实地物的光谱曲线的接近程度,直接反映了岩石分类的精度。

    本文在ENVI软件平台下使用FLAASH校正工具对工作区影像进行大气纠正。FLAASH采用MODTRAN4+辐射传输模型,通过图像像素光谱上的特征估计大气的属性,不依赖遥感成像时同步测量的大气参数数据,可以有效去除水蒸气/气溶胶散射效应。

    大气纠正精度采用光谱曲线比较进行控制。将经过大气纠正后的比较纯净的影像像元光谱曲线与波谱库中对应地物的光谱曲线进行对比,若曲线曲率差异较大,则调整FLAASH大气纠正的各参数重新进行纠正,直到影像像元光谱曲线与波谱库中对应地物的光谱曲线变化趋势一致,则为达到要求的大气纠正结果。图 3是经过大气校正后的影像,霾和薄云被较好地去除。

    图  3  大气校正前后影像对比
    (RGB=321,左为校正前,右为校正后)
    Figure  3.  Image contrast of atmospheric correction

    由于工作区北高南低,地形起伏度较大,几何上需要进行三维正射纠正,在ERDAS IMAGINE9.1的LPS模块进行。本次工作的精度控制为影像图上随机抽取地物点的平面位置中误差不大于1个像元,特殊情况下不大于2个像元。对区内北东、北西、西部等海拔较高的山地,该指标作适当放宽,限定为上述指标的2倍以内。

    根据野外实测的岩石光谱曲线和USGS光谱库的典型岩石光谱曲线提取端元波谱,对区内区域图像像元的光谱曲线进行匹配,找到最接近的光谱,达到岩性分类的目的。主要手段有光谱角填图、矿物指数法、光谱匹配滤波、人机交互解译调整。所有的计算机自动解译在ENVI 4.4环境下进行。

    光谱角填图将光谱数据视为多维空间的矢量,利用解析方法计算像元光谱与光谱数据库光谱或像元训练光谱之间的夹角,根据夹角的大小确定光谱间的相似程度,以达到识别地物的目的[8-9]。将影像光谱同实测标准光谱进行比较,将两个光谱作为矢量空间的两个矢量,其维度等于波段数,通过计算两者间的“光谱角”,确定它们的相似程度。区内10种岩性的光谱数据库为本次岩性填图的基本依据。图 4是阀值为5°的SAM伪彩色分类图像,其中蓝色为闪长岩,淡蓝色为大理岩,绿色为白云岩,绿黑色为灰岩,紫红色为板岩,紫黑色为霓霞岩,蓝紫色为砂岩。由于像元光谱角度受地物本身、环境辐射等诸多因素影响,需结合其他分析方法及野外岩性点综合对其岩性进行划分。

    图  4  研究区光谱角岩性分类
    Figure  4.  Spectral angle lithology classification of research area

    矿物指数法是为了突出某一类矿物的信息,分别选取同类矿物的3种矿物比值进行处理,然后采用3种矿物指数进行RGB彩色合成(即矿物组合)增强信息[10]。本次对区内碳酸盐/铁镁矿物信息进行提取,采用(6+9)/(7+8) 提取角闪石、绿泥石、绿帘石含量较高的地质体,(7+9)/8提取碳酸盐、绿泥石含量较高的地质体,(6+8)/7提取白云石含量较高的地质体,当(6+9)/(7+8)、(7+9)/8和(6+8)/7分别被赋予红(R)、绿(G)、蓝(B)色时,图像上相应岩性界线就会较清楚地显现出来(见图 5)。在图中含角闪石类较多的地质体呈红色,含碳酸盐较多的地质体成浅绿色、浅黄色,含白云石较多的地质体呈蓝色、蓝绿色。通过该3种矿物比值信息的提取,区内东北部的闪长岩、中南部的白云岩等岩性信息较为清楚地显示出来。

    图  5  研究区矿物指数法RGB合成图
    Figure  5.  Synthesis chart (RGB) of Mineral Index of research area

    将已知端元波谱的响应最大化,并抑制未知背景合成的响应,最后“匹配”已知波谱。光谱匹配滤波后将形成一个新的数据体,其波段数等于分类中所用的参考光谱数目,每个波段对应一个波谱端元,相应的像元值是每个端元波谱的匹配度。采用3种波谱端元的丰度进行RGB合成,图像的色彩界线反映的是相应波谱端元的丰度[11-13]图 6为利用板岩、碳酸盐岩、砂岩作为匹配滤波参考光谱所识别出的岩性分布,从图 6中可见,区内碳酸盐矿物色调呈绿色,且色调较为均一,分布广泛;板岩成紫红色主要分布在北部、中部等地区;砂岩在区内显示不明显,需通过综合分析厘定其岩性界线。

    图  6  研究区光谱匹配滤波RGB合成图
    Figure  6.  Synthesis chart (RGB) of matched filtering of research area

    将计算机自动分类的结果转换成矢量层,在ArcGIS平台下进行空间分析,对照遥感影像特征和野外实测光谱岩性点,结合地质解译经验,勾勒岩性界线,得到工作区岩性信息综合分类图(见图 7)。

    图  7  研究区岩性单元解译图
    Figure  7.  Interpretation chart of lithologic unit classification of research area
    5.4.1   岩浆岩

    闪长岩主要分布于研究区北部,特征非常明显,在ASTER影像上呈灰白色;在光谱角岩性分类图中呈蓝色;在矿物指数法RGB合成图中呈红色、淡红色;在光谱匹配滤波RGB合成图中呈紫色,周边围绕绿色,反映出岩相的变化。霓霞岩分布于研究区中部,在ASTER影像上特征较为明显,火地垭群与发育于其中的霓霞岩岩体构成一个穹隆构造,其边界以陡崖陡坎为界。

    5.4.2   奥陶系(O)

    主要分布在研究区南部,以灰岩、页岩为主。在ASTER影像上色调呈灰白色、浅紫红色,平行纹形发育。在矿物指数法RGB合成图中呈现蓝绿色,与寒武系界线较为清楚。

    5.4.3   寒武系(Є)

    主要分布于研究区南部,在ASTER影像上呈紫红色,其纹理结构特征明显,可见地层连续出露,主要岩性为白云岩、泥岩、砂岩。在矿物指数法RGB合成图中呈蓝色、淡黄色、淡绿色;在匹配滤波RGB合成图中主要呈绿色。

    5.4.4   震旦系(Z)

    震旦系为一套白云岩、夹泥岩地层,其界线在ASTER影像上通常表现为陡崖,在矿物指数法RGB合成图中呈绿色;在光谱匹配滤波RGB合成图中为灰绿色与浅红色的界线。

    5.4.5   元古界火地垭群麻窝子组(Ptm)

    麻窝子组主要岩性为大理岩、千枚岩等。在光谱匹配滤波RGB合成图中呈现浅红、淡绿色,部分地区岩性界线不明显,需要根据遥感影像特征和地质背景资料进行区分。

    5.4.6   元古界火地垭群上两组(Pts)

    上两组岩性为板岩、片岩夹碳酸盐岩。主要分布在区内北部地区,在光谱角岩性分类图中呈紫红色、绿色;在矿物指数法RGB合成图中呈黄色、黄绿色;在光谱匹配滤波RGB合成图中呈现绿色、浅红色。

    野外岩矿光谱数据测量工作受到多重复杂因素的影响,应因地制宜选择合适的测量时间和路线,尽量排除各种外在干扰因素的影响,所测结果才能真实反映岩矿本身的光谱特性。

    运用ASTER数据与岩矿实测光谱数据相结合,在识别和提取岩矿信息方面效果显著,大幅度提高了基于遥感影像的岩性分类精度和可信度,可以有效地划分区内岩性界线,满足填图需求。

    通过本次工作所建立的岩矿光谱数据库,为该区岩矿光普分析、反演和后期类比研究工作提供了科学依据,由其建立的岩石地层解译标志,对该区及邻区的岩矿、地层信息的提取具重要的参考意义。

    该项工作有快捷、高效、经济等显著特点,并能为大比例尺地质矿产调查提供前期的技术资料,在交通不便的未知区域开展类似工作,对区域地质调查、矿产调查等工作具重要的参考辅助价值,其在地学研究领域的应用将会越来越广泛。

    由于工作区特殊的地理气候,植被覆盖度过高,计算机自动解译方法的效果受到了一定影响,须通过目视解译加以弥补。

    如何准确去除大气辐射影响、传感器等其他因素附加在影像中的噪声,如何最大化降低地物光谱仪野外实测的干扰因素,进而提高利用光谱特征匹配进行地物识别的准确度,有待进一步研究和探索。

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