国土资源遥感, 2018, 30(2): 147-153 doi: 10.6046/gtzyyg.2018.02.20

技术应用

顾及矿物粒径的月表虹湾地区矿物填图

董晓莹,1, 林伟华,1, 刘福江1, 张琪1, 常远2

1.中国地质大学(武汉)信息工程学院,武汉 430074

2.吉林省交通规划设计院,长春 130021

Lunar mineral mapping in Sinus Iridum in consideration of mineral grain sizes

DONG Xiaoying,1, LIN Weihua,1, LIU Fujiang1, ZHANG Qi1, CHANG Yuan2

1. Department of Information Engineering, China University of Geosciences(Wuhan), Wuhan 430074, China

2. Jilin Provincial Communication Planning and Design Institute, Changchun 130021, China

通讯作者: 林伟华(1978-),男,博士,副教授,主要从事地质遥感、空间数据库和GIS模型方法等研究。Email:22384138@qq.com

第一联系人:

第一作者: 董晓莹(1992-),女,硕士研究生,主要从事深空探测方面的研究。Email: 963339577@qq.com

收稿日期: 2016-10-13   修回日期: 2016-12-18   网络出版日期: 2018-06-15

基金资助: 湖北省2014年面上自然科学基金项目“基于主动学习的遥感大训练样本选择优化”.  编号: 2014CFB911
国家“十二五”“863”项目“大规模空间数据融合分析关键技术与应用服务系统”.  编号: 2014AA121401
“大规模空间数据挖掘关键技术研究”.  编号: 2014AA123001

Received: 2016-10-13   Revised: 2016-12-18   Online: 2018-06-15

Fund supported: .  编号: 2014CFB911
.  编号: 2014AA121401
.  编号: 2014AA123001

摘要

月表矿物丰度及其分布的研究对月球资源利用有重要的指导意义。Hapke模型是月表研究最常用的一种模型,粒径是该模型计算时必须明确的参数之一。为了研究月表5种主要矿物(单斜辉石、斜方辉石、斜长石、橄榄石和钛铁矿)的丰度及分布情况,在顾及矿物粒径对其光谱影响的情况下,利用Relab光谱库数据和Hapke辐射传输模型,采用全约束线性分解的方法,建立了5种矿物的反演模型,得到上述5种矿物的模型相关性分别为0.98,0.98,0.83,0.91和0.50,并用Apollo采样点数据对模型精度进行了验证,最后将模型应用到月表虹湾地区的印度探月卫星月球矿物制图仪(moon mineralogy mapper,M 3)高光谱影像上,得到了虹湾地区5种矿物的丰度分布图。结果表明,顾及矿物粒径的全约束线性分解方法可用于月表矿物分布特征研究。

关键词: 虹湾 ; 矿物粒径 ; 全约束线性分解 ; M 3

Abstract

The distribution of the mineral abundances on lunar surface has a significant meaning. Hapke model is one of the most usually used methods for studying lunar surface, and particle size is one of the parameters that must be clearly understood in doing model calculation. Nevertheless, the research on grain size remains very insufficient. To study the distribution of the abundances of five main minerals, i.e., clinopyroxene, orthopyroxene, plagioclase, olivine and ilmenite, the authors considered the influence of grain size and built inverse models of these five minerals by using fully constrained linear-unmixing method with Relab data based on Hapke radioative transfer model, with the correlation coefficients of these five minerals being 0.98, 0.98, 0.83, 0.91 and 0.50. Furthermore, the accuracy of this models was verified by using data of Apollo sampling points . At last, the lunar minerals abundance distribution maps of Sinus Iridum were compiled by applying the models to the M 3 hyperspectral data,which shows that the fully constrained linear-unmixing method in consideration of mineral grain sizes can be used to study lunar mineral abundance distribution.

Keywords: Sinus Iridum ; mineral grain size ; full constrained linear-unmixing ; M 3

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本文引用格式

董晓莹, 林伟华, 刘福江, 张琪, 常远. 顾及矿物粒径的月表虹湾地区矿物填图. 国土资源遥感[J], 2018, 30(2): 147-153 doi:10.6046/gtzyyg.2018.02.20

DONG Xiaoying, LIN Weihua, LIU Fujiang, ZHANG Qi, CHANG Yuan. Lunar mineral mapping in Sinus Iridum in consideration of mineral grain sizes. REMOTE SENSING FOR LAND & RESOURCES[J], 2018, 30(2): 147-153 doi:10.6046/gtzyyg.2018.02.20

0 引言

月球表面矿物组成的测定是理解月球成因和地质演化的基础,这些成分信息可以用来研究月球地壳形成时的岩浆模型、月球的地壳结构、玄武岩火山、火山口/盆地结构及喷出物的流溢,以及理解月壤的混合机理[1]。可见光/近红外反射光谱(0.4~2.5 μm)是研究月壤矿物组成的重要数据之一,大多数月球矿物在该波段范围有比较显著的吸收特征(中心位置: 750 nm和950 nm)。随着月表高光谱数据探测技术的发展,对月表矿物的分析逐渐趋向于定量反演[2],高光谱数据凭借其具有连续光谱、高光谱分辨率和高空间分辨率成为当前研究的主要数据。印度探月卫星Chandrayaan-1获取的月球矿物制图仪(moon mineralogy mapper,M3)高光谱数据可用于较精确地定量分析月球矿物的光谱特征和含量分布[2]

由于地物混合的多样性以及高光谱数据空间分辨率的限制,混合光谱现象在遥感影像中普遍存在。因此,混合光谱分解[3,4]成为利用高光谱数据进行矿物定量反演研究的热点和难点。Johnson等[5]利用半经验法进行非线性解混反演得到了月表橄榄石、辉石和钛铁矿等的含量; Mustard 等[6]利用Hapke模型进行非线性解混反演月表矿物丰度; Li 等[1]根据Hapke模型采用多端元光谱分解的方法得到了月表矿物丰度图; 闫柏琨等[7,8]基于光谱分解利用Clementine UV/VIS/NIR数据对全月进行了矿物填图; Combe等[9]基于混合光谱分析利用M3对矿物表面混合特性进行了研究。

Hapke模型是一种常用的研究矿物反射特征的传输模型,可以很好地模拟矿物的混合光谱特征,具有严格的物理意义。在任何Hapke模型计算中矿物粒径都是必须明确的参数之一,大量的实验室结果均证明了矿物粒径对矿物的反射特性有显著的影响。Li 等[10]基于Hapke模型利用月壤特征集(lunar soil characterization consortium,LSCC)数据定量研究了矿物粒径对熔融玻璃、单斜辉石、斜方辉石、斜长石、橄榄石、钛铁矿和火山玻璃等月表矿物光谱的影响以及亚微观金属铁(submicroscopic metallic Fe,SMFe)的含量; Nelson等[11]比较了Hapke模型中矿物粒径不同计算方法结果的异同; 张渊智等[12]利用Hapke模型和Mie理论模拟了不同粒径下月表橄榄石的二向性特征,发现光谱反射率随着粒径的增加而降低; 吴昀昭等[13]利用Hapke模型考虑矿物粒径与密度研究了太空风化对其反射光谱的影响。

本文基于Hapke模型,利用线性分解模型在顾及矿物有效粒径大小和密度对反射光谱的影响,用反射实验室(reflectence experiment laboratory,Relab)光谱库数据建立模型,采用M3数据对月表虹湾地区典型矿物的含量及其分布填图。

1 数据源

Relab光谱库是美国Brown大学在美国航天航空局(NASA)的资助下建立的面向公众的免费数据库。本文采用的5种矿物端元光谱分别为LS-CMP-009(单斜辉石)、LS-CMP-012(斜方辉石)、LR-CMP-014(橄榄石)、LS-CMP-011(斜长石)和PI-CMP-006(钛铁矿),使用的光谱范围是430~2 600 nm。针对月壤,该数据库将矿物颗粒分为<10 μm,10~20 μm,20~45 μm和>45 μm 4个等级,本文使用的是10~20 μm的数据,并依据M3影像光谱范围和光谱分辨率进行重采样。M3数据技术性能指标详见表1。不同的观测条件下,光学周期不同,空间分辨率也有明显的差别。在轨道高度为100 km时,包含3个光学周期: OP1A,OP1B和OP2A; 当轨道高度为200 km时,包含有OP2B和OP2C光学周期。其中,OP1B和OP2C可覆盖虹湾地区及月表采样点。

表1   M3主要技术和性能指标

Tab.1  Main technical and performance indicates of M3

成像
模式
视场
/km
光谱范
围/nm
采样间
隔/nm
空间分
辨率/m
波段
数/个
光谱分
辨率/nm
target40430~3 000107026110
global40430~3 000101408520/40

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2 矿物反演模型建立

2.1 矿物单次散射反照率

月表不是理想的朗伯面,平行太阳光经月表物质反射后的光强分布不是各向同性的,受到太阳光入射角、反射光出射角以及两者的夹角(即相位角)的影响,具有明显的二向性反射特性[14],其波谱特征与目标物体的组成成分、物体内部结构和物体表面结构关系密切[15]。月表二向性反射率是进行月表组分识别和矿物含量反演的重要依据[16],利用月表二向性特征、根据实测反射率可以得到月表矿物含量。然而,研究发现矿物光谱的二向性一般不呈现线性特征,不能直接进行线性混合,但是矿物的单次散射反照率(single-scattering albedo, SSA)是各向同性的,可直接进行线性混合[17]。Hapke模型可以实现二向性反射率与同向性的单次散射反照率之间的转换,具有严格的物理意义。假设矿物为朗伯反射体,由Hapke模型[18]可知,矿物光谱的二向性反射率r(μ0,μ,g)的公式为

r(μ0,μ,g)= ω4πμ0μ0+μ{[1+B(g)]P(g)+H(μ0)H(μ)-1} , (1)

H(μ)= 1+2μ1+2μ(1-ω)12, (2)

P(g)=1+bcos(g)+c[1.5cos2(g)-0.5] , (3)

式中: μ0为入射角余弦值; μ为出射角余弦值; g为相位角; ω为矿物SSA,即仅考虑一次散射时,散射能量与颗粒散射和吸收的能量之和的比值; B(g)为后向散射函数,定义了当相位角减小时颗粒粗糙表面亮度值的增加量,当相位角大于15°时,可认为所有的散射都是同向性的,B(g)可忽略; H(μ)为同向性散射函数; P(g)为平均单粒子相函数,即颗粒散射能量随着g变化的规律; bc为常数。

Relab光谱库中数据测量时,入射角i=30°,出射角e=0°,相位角g=30°,故B(g)=0,P(g)=1。可简化Hapke模型如式(4),从而得到矿物SSA,即

r= ωH(μ0)H(μ)4(μ0+μ)。 (4)

2.2 矿物混合反射率

基于Hapke模型得到的矿物SSA,将端元矿物的SSA进行随机线性混合[19],混合时根据已有的研究成果[1],对5种成分矿物的含量进行约束: 斜长石( 0%~100%)、单斜辉石(0%~70%)、斜方辉石(0%~50%)、橄榄石(0%~50%)和钛铁矿(0%~20%),并考虑颗粒的平均有效粒径和密度,然后再利用Hapke模型计算得到混合反射率数据。

颗粒的平均有效粒径<D>和密度ρ等不同,矿物的反射率也会发生变化,因此这里计算混合反射率时,将<D>与ρ考虑进矿物混合公式[16],即

ω'= i=1nωiMiρidi/i=1nMiρidi, (5)

式中: ω'为混合矿物SSA; ωi,Mi,ρidi分别为混合物中第i种组分的SSA、质量分数、密度和平均有效粒径。其中,颗粒的<D>采用area-weighted mean size来表示,即

<D>≈DLln(DU/DL) , (6)

式中: DU为最大粒径; DL为最小有效粒径。文中用到的5种矿物信息详见表2

表2   Relab光谱库端元矿物光学常数信息

Tab.2  Optical constant information of endmember mineralsin Relab spectral library (nm)

矿物类别样本编号光谱范围DUDL<D>
单斜辉石LS-CMP-009350~2 600250520
斜方辉石LS-CMP-012350~2 600250520
斜长石LS-CMP-011350~2 600500523
橄榄石LR-CMP-014300~2 60045511
钛铁矿PI-CMP-006300~2 60075514

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得到混合反射率后需要对其进行去连续统处理。该方法是一种用于分离光谱吸收特征与背景的光谱分析方法,可有效消除地形和照度等对光谱特征造成的影响,从而去除端元光谱与影像光谱之间由于照度不同所导致的光谱变异。

2.3 全约束线性分解

遥感探测器记录的瞬时视场角对应的地面范围内目标的辐射能量总和被称为遥感像元。通常一个遥感像元内包含多种不同类型的地物,被称为混合像元。目前,混合像元分解 [3-4]是解决混合像元最为有效的方法,即将记录混合像元的影像光谱分解为组成光谱(端元)与其相应丰度。针对混合像元分解的模型,目前主要分为线性光谱混合模型与非线性光谱混合模型2类。其中线性光谱混合模型因其简单、效率高、物理意义明确而得到更广泛的应用。本文即采用线性光谱混合模型。

假设像元内相同的地物具有相同的光谱特征并且忽略多次散射过程,混合像元反射率与端元反射率以及端元丰度之间的关系可以表示为[4]

Rλ= i=1mAiλxi+ε=Ax+ε , (7)

式中: Rλ为第λ波段混合像元的光谱反射率; m为端元个数; Aiλ表示第λ波段第i种端元的光谱反射率; xi表示第i种端元的丰度; ε为模型残差,即拟合光谱与真实光谱之间的差值,这个值越小表示拟合越好,当波段数大于端元个数时采用最小二乘法求解端元的丰度。

实际上,利用当前这种模型进行求解时会出现端元丰度为负值或者所有端元丰度之和大于1的现象,这可能是由于选择的端元光谱特征不明显或者在分析时缺少端元光谱造成的[3]。因此,在实际进行分解时加上2个约束条件: ①非负约束,0≤xi≤1,i=1,2,…,p; ②和为1约束, i=1pxi=1,i=1,2,…,p,以上条件与式(7)组合称为全约束线性分解模型[20]

2.4 反演模型建立

充分考虑2个约束条件,利用线性分解得到的各端元矿物的含量记为“矿物分解含量”; SSA随机混合时设定的各端元矿物的含量记为“真实含量”,根据统计关系建立5种端元矿物(单斜辉石、斜方辉石、斜长石、橄榄石和钛铁矿)的丰度反演模型[21,22],如图1所示。

图1

图1   不同端元矿物分解含量与真实含量的统计关系

Fig.1   Statistical relations between the unmixing abundance and the real abundance of different endmember minerals


图1可以看到,单斜辉石、斜方辉石、斜长石、橄榄石和钛铁矿这5种矿物的“分解含量”与“真实含量”之间的线性相关系数分别是0.98,0.98,0.83,0.91,0.50。单斜辉石、斜方辉石、斜长石和橄榄石的线性相关性均比较高,钛铁矿相对来说较低,这与其在月球中本身含量较低以及不透明矿物的特性有关。与之前的研究[23]相比,将矿物粒径对其反射特性的影响考虑进来建立的模型,相关性均有比较明显的提高。尤其是单斜辉石和斜方辉石的模型相关性基本接近于1,说明本实验模型可以较好地反演矿物含量,可以用来进行月表矿物丰度填图。

2.5 模型验证

为了验证反演模型的精度,将该方法应用到Apollo样品数据上,得到矿物分解含量,并与样品的实测矿物含量进行对比。其中,辉石是指单斜辉石与斜方辉石含量之和,结果如表3图2所示。

表3   Apollo 采样点矿物反演结果与实测结果对比

Tab.3  Comparison between the inversed abundance and the measured abundance of different endmember minerals in Apollo(%)

样品编号实测结果反演结果
辉石斜长石橄榄石钛铁矿熔融玻璃辉石斜长石橄榄石钛铁矿
1203021.414.03.73.249.815.762.811.213.3
1507116.719.42.81.849.213.064.511.013.2
7150113.719.83.49.744.89.866.611.122.1
674614.161.01.50.332.43.398.88.37.0
1414110.928.01.61.148.68.979.29.510.3
1416313.818.32.10.958.59.181.27.213.3

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图2

图2   Apollo采样点矿物反演结果与实测结果统计关系

Fig.2   Statistical relations between the inversed abundance and the measured abundance of different endmember minerals in Apollo


表3图2可以看出,相比实测含量,斜长石、橄榄石和钛铁矿的含量均被高估,而反演辉石的含量略低于实验室测量得到的真实值。造成这一现象的原因可能有以下几点:

1)端元选择不够或端元特征不够明显会造成矿物反演结果有偏差 [3,24],本文选取了月表丰度相对较高并且有明显反射特征的5种端元光谱来进行实验。从Apollo带回来的样品中可以发现,熔融玻璃与火山玻璃也是月表的主要矿物,但是由于其是风化产物,光谱反射特征已基本消失,因此没有对其进行反演。

2)斜长石的含量被高估,且反演得到的斜长石含量与实测中斜长石与熔融玻璃含量之和比较接近,可能与斜长石的反射特性有关。斜长石整体反射率较高,且经过月表风化作用,吸收特征被弱化,而熔融玻璃作为风化产物,其基本没有反射特征,反射光谱曲线变得平滑,两者比较接近,因此造成了斜长石含量被高估。

3)钛铁矿的反演结果被高估可能与钛铁矿自身属性有关,钛铁矿作为不透明矿物,在光谱分解时其含量通常会被高估[25]

4)辉石的含量低于实测含量值,这可能与斜长石等含量被高估有关,辉石的含量与长石含量存在高度的负相关[24],而辉石与橄榄石在1 000 nm附近有一个共同的吸收特征。

总体来讲,辉石、斜长石、橄榄石和钛铁矿这4种矿物的“分解含量”与“实测含量”均有较好的相关性,这说明本实验模型能较好地描述这4种矿物的分布,可以用于矿物填图。

2.6 虹湾地区矿物填图

本文采用 M3L2级数据,已经过光度校正和几何校正等预处理。为了进一步削弱噪声的影响,利用Savitzky-Golay滤波法对图像进行平滑处理。Savitzky-Golay 滤波是广泛用于数据流平滑除噪的一种方法,可在确保光谱曲线的形状和宽度不变并保持光谱特征的情况下去除光谱噪声,达到平滑去噪的效果[26]。处理流程如图3所示。

图3

图3   M3数据处理流程

Fig.3   Process flowchart of M3 data


利用上述方法得到5种端元矿物的分解含量后,采用该反演模型,得到矿物在虹湾地区的填图结果,如图4所示。得到5种矿物在虹湾地区含量均值分别为: 7.3 wt%,0.7 wt%,86.7 wt%,13.2 wt%和9.8 wt%。

图4

图4   虹湾地区矿物分布

Fig.4   Mineral abundance distribution in Sinus Iridum


图4可以看出,①单斜辉石主要分布在虹湾内部,一些撞击坑中有较高分布,高地几乎没有分布; ②斜方辉石在与虹湾相接的西北部高地有少量分布,而虹湾内部含量几乎为0; ③斜长石广泛分布于虹湾及其附近区域且丰度很高,尤其在高地区域,虹湾内部丰度相对较低且沿雨海延伸方向逐渐减少,到与虹湾相连的雨海中仅有少量分布; ④橄榄石主要分布在与虹湾相连的雨海中,在虹湾内部仅有零星分布,此外在高地的一些区域也发现有少量橄榄石分布; ⑤钛铁矿主要分布在与虹湾相接的雨海及虹湾内部,在高地仅有少量分布。

将本实验结果与之前未考虑矿物粒径影响得到的结果[23,26]相比,辉石的分布基本不变,只是本文得到的单斜辉石含量较低,且高地分布基本为0; 与虹湾相连的雨海内部有零星分布这一研究结果与李婵等[26]根据矿物吸收特征利用IIM影像在该地区得到的辉石分布相符; 本文反演得到了少量的斜方辉石,且主要分布在高地,在虹湾内部及雨海基本没有分布,这一结果与Lucey和Staid等的研究相符[24,27]; 斜长石的分布基本不变; 本文得到的橄榄石主要分布在与虹湾相连的雨海中,在虹湾内部仅有零星分布,且在高地的一些区域也发现有少量橄榄石分布,这一结果与Lucey之前的研究一致[24]; 本文得到的钛铁矿在虹湾内部有较广泛分布,而在高地仅散落于一些区域上,与李婵等[26]利用根据矿物吸收特征在该地区TiO2的填图结果基本一致,而钛铁矿的分布主要取决于TiO2的分布 [24]。由此可知,本文将矿物粒径考虑进反演模型提高了模型反演的精度,结果更具可信性。

3 结论与展望

矿物粒径的差异会引起反射光谱的变化,对辉石及钛铁矿的影响尤为明显[12]。本文顾及粒径等对矿物反射光谱造成的影响,利用全约束线性分解的方法,得到了虹湾地区5种主要矿物丰度分布。实验结果表明,将粒径等影响考虑进模型提高了模型的反演精度。反演得到的5种矿物在虹湾地区的分布为: 单斜辉石是虹湾地区辉石的主要存在形式,主要分布虹湾内部,一些小撞击坑中含量较高,在高地基本没有分布; 斜方辉石在月表含量较少,主要分布在高地,在虹湾内部和雨海基本没有分布; 斜长石主要分布在高地,在虹湾内部也有分布,相对较少,且与辉石的分布呈现负相关; 橄榄石主要分布与虹湾相连的雨海中,在虹湾内部及高地有少量分布; 钛铁矿主要分布在雨海及虹湾内部,在高地仅有零星分布,含量较低。

但是,本文仅选取了单斜辉石、斜方辉石、斜长石、橄榄石和钛铁矿5种典型矿物,且每种矿物仅选取了一个端元光谱,这可能导致结果存在误差; 且月球由于没有大气层,太空风化现象十分严重,而并未将太空风化效应[28]考虑进模型,也会使结果产生误差。此外,王景然[29]研究发现,地形起伏虽不会引起矿物分布的变化,但是会影响其丰度。因此,为了进一步提高反演结果的准确性,下一步将进一步优化程序并考虑将矿物的地形、粒径和风化程度对矿物光谱的影响加入模型建立光谱库,同一种矿物将对应多个端元光谱进行矿物反演。

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[D].北京:中国地质大学(北京), 2011.

[本文引用: 1]

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月表主要矿物含量的定量反演是月球科学领域的重要课题之一,对未来月表矿物信息解译有重要指导意义,因此提出一种对月表高光谱遥感数据线性分解获取矿物含量的方法.首先,利用Hapke辐射传输模型将Relab光谱库中的5种矿物(单斜辉石、斜方辉石、斜长石、橄榄石和钛铁矿)非线性混合的反射光谱转换为线性混合的单次反照率;然后,按照比例随机生成混合像元;最后基于全约束线性光谱分解方法建立上述5种矿物分解含量与真实含量的统计关系模型.利用Apollo登陆采样点实测数据对该模型进行验证的结果表明,辉石、斜长石、橄榄石和钛铁矿的反演结果与实测结果的相关系数分别为0.83,0.86,0.72和0.77.采用上述方法,利用印度探月卫星Chandrayaan-1搭载的月球矿物制图仪(moon mineralogy mapper,M3)高光谱数据得到月表虹湾地区矿物的含量分布图,表明利用全约束线性分解对高光谱矿物识别和含量反演是一种行之有效的方法.

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Global maps of the distribution of plagioclase, orthopyroxene, clinopyroxene and olivine on the Moon were derived from radiative transfer analysis of 400,000 Clementine UVVIS spectra. Plagioclase inversely correlates with iron while clinopyroxene positively correlates with iron showing these are the major carriers of aluminum and iron respectively. The distribution of olivine in the maria agrees with previous studies; in the highlands the abundance of olivine is low but ubiquitous at a few percent, except within the South Pole-Aitken basin where it is only present in very small exposures. In the very anorthositic farside highlands, olivine is often the sole mafic mineral. The abundance of orthopyroxene is generally low, excepting elevated abundances in the nearside highlands and in areas near and within South Pole-Aitken basin. Mare units with elevated abundances of orthopyroxene are found in some mare and cryptomare deposits distant from the sample return sites.

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分析了矿物在可见光及近红外区光谱生成的机理,介绍了月球表面最为主要的四种矿物——辉石、斜长石、橄榄石、钛铁矿,并分析了它们各自光谱特征及生成原因,讨论了造成同种矿物光谱差异的原因,给出了它们各自的标志性特征。

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The last major phase of lunar volcanism produced extensive high-titanium mare deposits on the western nearside which remain unsampled by landing missions. The visible and near-infrared reflectance properties of these basalts are examined using Clementine multispectral images to better constrain their mineralogy. A much stronger 1 0204m ferrous absorption was observed for the western high-titanium basalts than within earlier maria, suggesting that these last major mare eruptions also may have been the most iron-rich. These western basalts also have a distinctly long-wavelength, 1 0204m ferrous absorption which was found to be similar for both surface soils and materials excavated from depth, supporting the interpretation of abundant olivine within these deposits. Spectral variation along flows within the Imbrium basin also suggests variations in ilmenite content along previously mapped lava flows as well as increasing olivine content within subsequent eruptions.

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