国土资源遥感, 2018, 30(3): 89-95 doi: 10.6046/gtzyyg.2018.03.13

Landsat8 OLI数据斑岩铜矿遥感蚀变矿物组合提取研究

汪子义1, 张廷斌,1,2,3, 易桂花1, 钟康惠1, 别小娟1, 王继斌1, 孙姣姣1

1. 成都理工大学地球科学学院,成都 610059

2. 成都理工大学工程技术学院, 乐山 614000

3. 自然资源部地学空间信息技术重点实验室,成都 610059

Extraction of hydrothermal alteration mineral groups of porphyry copper deposits using Landsat8 OLI data

WANG Ziyi1, ZHANG Tingbin,1,2,3, YI Guihua1, ZHONG Kanghui1, BIE Xiaojuan1, WANG Jibin1, SUN Jiaojiao1

1. College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China

2. College of Engineering and Technical, Chengdu University of Technology, Leshan 614000, China

3. Key Laboratory of Geoscience Spatial Information Technology, Ministry of Nature Resources, Chengdu 610059, China

通讯作者: 张廷斌(1978-),男,博士,教授,主要从事遥感地质、生态遥感研究。Email:zhangtb@cdut.edu.cn。

责任编辑: 陈理

收稿日期: 2016-11-23   修回日期: 2017-02-20   网络出版日期: 2018-09-15

基金资助: 国家自然科学基金项目“西藏典型斑岩型铜矿床遥感蚀变信息重现性机理研究”.  41202233
自然资源部公益性行业科研专项“斑岩-浅成低温热液成矿系统研究及勘查评价示范——以西藏多龙整装勘查区为例”.  201511017
中国地质调查局地质调查项目“西藏物玛—先遣地区斑岩型铜金矿成矿规律研究与找矿靶区优选”.  12120113095300

Received: 2016-11-23   Revised: 2017-02-20   Online: 2018-09-15

作者简介 About authors

汪子义(1988-),男,硕士研究生,主要从事遥感地质研究。Email:763340359@qq.com。 。

摘要

Landsat8 OLI数据NIR波段(0.845~0.885 μm)和SWIR2波段(2.100~2.300 μm)波谱分辨率的提高,已分别成为含Fe 3+类、Al-OH类和Mg-OH类蚀变矿物的诊断性谱段。通过利用混合调制匹配滤波(mixture tuned matched filtering,MTMF)方法,开展多龙矿集区铁格隆远景区含Fe 3+类、Al-OH类和Mg-OH类蚀变矿物信息提取研究,并与Hyperion数据蚀变矿物信息提取结果进行对比分析。研究结果表明,基于Landsat8 OLI提取的3类蚀变矿物较为可靠; 铁格隆远景区蚀变矿物组合的空间分布与斑岩铜矿经典蚀变分带特征基本吻合; 遥感蚀变分带由内而外分别为绢英岩化+泥化带(Al-OH类蚀变矿物)和青磐岩化带(Mg-OH类蚀变矿物),Fe 3+矿物组合则分布在绢英岩化+泥化带和青磐岩化带之间或过渡带上。

关键词： OLI ; 斑岩铜矿 ; 蚀变分带 ; MTMF ; 遥感 ; 铁格隆

Abstract

The enhancing of NIR band and SWIR2 band of OLI data makes the spectra become the diagnostic spectra of ferric iron minerals, Al-OH and Mg-OH alteration minerals. The authors used the mixture tuned matched filtering (MTMF) mapping method to extract ferric iron, Al-OH and Mg-OH alteration minerals in Tiegelong prospective block of the Duolong porphyry Cu-Au ore concentration area. Compared with the results of mineral mapping from Hyperion data, the three mineral mapping types extracted from Landsat8 OLI are reliable. At the same time, the spatial distribution pattern of alteration minerals agrees with the hydrothermal alteration zone of typical porphyry copper deposits. The remote sensing alteration zones from interior to exterior of the Tiegelong porphyry Cu ore block are phyllic+argillic zone (Al-OH minerals) and propylitic zone (Mg-OH minerals), and the ferric iron minerals are between the arephyllic+argillic and propylitic zones.

Keywords： OLI ; porphyry copper deposit ; hydrothermal alteration mineral zones ; MTMF ; remote sensing ; Tiegelong

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本文引用格式

汪子义, 张廷斌, 易桂花, 钟康惠, 别小娟, 王继斌, 孙姣姣. Landsat8 OLI数据斑岩铜矿遥感蚀变矿物组合提取研究. 国土资源遥感[J], 2018, 30(3): 89-95 doi:10.6046/gtzyyg.2018.03.13

WANG Ziyi, ZHANG Tingbin, YI Guihua, ZHONG Kanghui, BIE Xiaojuan, WANG Jibin, SUN Jiaojiao. Extraction of hydrothermal alteration mineral groups of porphyry copper deposits using Landsat8 OLI data. REMOTE SENSING FOR LAND & RESOURCES[J], 2018, 30(3): 89-95 doi:10.6046/gtzyyg.2018.03.13

0 引言

世界经济发展的百年历程中,斑岩铜矿提供了超过75%的Cu资源和超过50%的Mo资源需求量,同时,也是Au和Ag等金属资源的重要来源[1]。我国斑岩铜矿占铜储量的42.19%,矽卡岩铜矿占铜储量的22.42% [2]。但是,我国每年的铜产量并不能满足经济发展的需求,Cu资源需求量对外依赖度高达70%[3]。因此,斑岩型铜矿床在工业界和科学界备受关注。

利用遥感技术开展围岩蚀变信息(蚀变矿物)提取研究已有近半个世纪的历程。受岩浆热液作用影响,斑岩型矿床具有良好的水平和垂向蚀变矿物分带特征[4]。近些年的研究表明,遥感蚀变矿物(组合)提取技术能有效识别斑岩铜矿围岩蚀变的分带特征[5,6]。目前,TM,ETM+,ASTER,Hyperion和Landsat8 OLI等遥感数据,在遥感蚀变信息(蚀变矿物组合)提取和识别方面应用广泛[7,8]。TM和ETM+数据常用来提取铁染和泥化蚀变信息[9,10,11,12],也有研究者用来进行铁镁质岩石信息的识别[12,13,14]。其中,主成分分析法(principal component analysis,PCA)是最常用的技术方法。ASTER数据在SWIR和TIR波谱区间的波谱分辨率提高,使得ASTER数据在蚀变矿物(信息)提取和岩性信息识别等方面应用也较广,通常用来识别Mg-OH类、Al-OH类、C O32-类和硅化等矿物信息[15,16]。随着遥感数据信息的深入研究与挖掘,多光谱卫星数据的应用正向着矿物类型细分(铝白云母和铁白云母)[17]、矿物组合(蚀变的分带)研究[18]、矿物含量定量化(半定量化)[19]以及遥感蚀变异常下限定量化确定[20,21]等方向发展。Hyperion高光谱遥感数据光谱分辨率较高,可达10 nm,能更好地识别矿物类型,但因存在幅宽窄、存档数据少和信噪比低等问题,目前多用于矿区蚀变矿物的提取而难以开展区域性应用。随着Landsat8 OLI数据辐射分辨率和波谱分辨率的大幅提升,为铁染类、羟基类、C O32-类和硅化等4类蚀变矿物信息的提取研究提供了新的数据来源[22,23,24,25]。

以班公湖—怒江成矿带多龙矿集区铁格隆斑岩铜矿远景区为例,在对铁格隆远景区各蚀变带蚀变矿物(组合)波谱分析的基础上,利用混合调制匹配滤波(mixture tuned matched filtering,MTMF)方法开展基于OLI数据的蚀变矿物组合提取研究,并与Hyperion高光谱数据提取结果进行对比验证,初步分析铁格隆远景区基于OLI数据的蚀变矿物组合分带特征。

1 研究区概况

青藏高原是聚合板块最发育的地区,对斑岩型铜矿床及伴生矿床形成十分有利,具有良好的斑岩型铜矿床成矿前景,已经成为我国最重要的矿产资源基地之一[2,26-27]。青藏高原气候干旱、空气稀薄、植被稀疏、基岩裸露等特点,也为遥感蚀变矿物识别和提取提供了良好的地表环境。其中,西藏多龙矿集区大地构造位置属三江构造系(一级构造单元)、羌塘弧盆地系(二级构造单元)、扎普—多不杂岩浆弧(三级构造单元),位于班公湖—怒江结合带西段,是冈底斯陆块与羌塘陆块的分界线[28]。多龙矿集区中的铁格隆斑岩铜矿远景区作为本文研究区域,主要出露下侏罗统曲色组(J1q)、中侏罗统色哇组(J2s)以及第四系(Q4); 岩浆岩较发育,主要有花岗闪长斑岩和辉长岩等[29]。研究区工作程度较低,在地表和钻孔内见硅化、绢云母化、褐铁矿化和角岩化[29],地表常见蚀变矿物主要有褐铁矿、绢云母、高岭石、石英、绿泥石和绿帘石等。

2 遥感数据源与矿物波谱特征分析

2.1 遥感数据源

Landsat8 OLI数据VNIR波谱区间有6个波段,SWIR波谱区间有3个波段,波段设置如表1所示。

表1   OLI数据波段设置

Tab.1  Bands setting of OLI data

波段号波长范围/μm空间分辨率/m
8 (pan)0.520~0.68015
1 (Coastal)0.433~0.45330
2 (Blue)0.450~0.515
3 (Green)0.525~0.600
4 (Red)0.630~0.680
5 (NIR)0.845~0.885
9 (Cirrus)1.360~1.390
6 (SWIR1)1.560~1.660
7 (SWIR2)2.100~2.300

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OLI数据获取于2015年11月22日,为经过几何校正的L1T产品,地理参考为UTM投影WGS坐标系。Hyperion数据获取于2004年10月30日,为L1R产品,没有地理参考,需要以OLI数据为基准进行几何校正。为了削弱大气散射和吸收等影响,OLI数据进行了辐射定标和基于MODTRAN(moderate resolution transmittance)辐射传输模型的大气校正等预处理; Hyperion数据进行了坏波段去除、辐射定标、坏线修复、条纹去除、Smile效应去除、基于MODTRAN辐射传输模型的大气校正和几何校正等预处理,几何校正误差控制在0.5个像元以内。

2.2 矿物波谱特征分析

利用SVC HR-1024波谱仪采集了地表常见蚀变矿物褐铁矿、高岭石、白云母和绿泥石等的野外光谱,并对野外采集矿物波谱、USGS波谱库矿物波谱和基于OLI波谱重采样的USGS矿物波谱进行了对比分析,对比结果示意图如图1所示。

图1

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图1   典型蚀变矿物波谱曲线对比

Fig.1   Comparison of spectra of typical alteration minerals


从图1(a)和(c)中可以看出: ①绢英岩化带中富含白云母(绢云母),白云母在2.20 μm附近(2.13~2.27 μm之间)具Al-OH类矿物吸收特征[30,31,32],VIR波谱区间反射率较低; ②褐铁矿在0.5 μm和0.9 μm附近具吸收特征,是Fe3+类矿物的诊断性谱段[32,33,34],VNIR波谱区间反射率较低; ③高岭石为泥化带典型蚀变矿物,在2.05~2.24 μm之间具双吸收特点[32,34],其他波谱区间反射率较高; ④绿泥石为青磐岩化带的典型蚀变矿物,通常主要为铁绿泥石和镁绿泥石,绿泥石在2.32 μm附近(2.28μm~2.40μm之间)具强吸收特征,主要是由Mg-OH类矿物引起,而在0.90 μm附近的吸收特征主要是由Fe3+类矿物引起[33,34],VNIR波谱区间反射率低于SWIR。对应OLI数据重采样波谱(图1(b)),高岭石和白云母在SWIR2波段具有吸收特征; 相反,绿泥石在SWIR2波段具有反射特征; NIR波段褐铁矿和绿泥石波谱都具有一定的吸收特征,但区别于褐铁矿,绿泥石在红光波段也具有一定的吸收特征,褐铁矿则为反射特征; 而褐铁矿在蓝光波段具吸收特征。

通过对比OLI与TM和ETM+的波段设置,调整较大的为NIR波段(0.845~0.885 μm)和SWIR2波段(2.100~2.300 μm); NIR波段波谱宽度变窄,排除了0.825 μm 处水汽吸收的影响,并且位于褐铁矿吸收谷位置,成为含Fe3+类矿物的诊断性谱段; SWIR2波段光谱分辨率的提高,消除了C O32-类矿物对羟基类矿物提取的影响[35],同时成为Al-OH类矿物的诊断性谱段,也突出了Mg-OH类矿物在SWIR2波段的高反射特征。

3 蚀变矿物提取与分析

3.1 提取方法

MTMF方法是一种高光谱数据制图方法,近年来被广泛应用到多光谱蚀变矿物提取中[24,35-36]。该方法通过对目标波谱的最优线性检测与评估,在混合像元分解基础上,反演每个像元中不同探测目标的丰度[37]。该方法主要基于目标波谱的整体形态,矿物波谱特征分析时,不仅分析了矿物的吸收特征,还分析了矿物的整体波谱形态及吸收特征对整体形态的影响。匹配结果由2个部分构成,分别记录了目标端元波谱的匹配程度得分和不可信度得分,匹配得分与不可信度得分比值则能较好地表达蚀变矿物丰度信息[38]。MTMF方法提取蚀变矿物时,还需要以最小噪声分离(minimum noise fraction rotation,MNF)、纯净像元指数计算、N维散点图纯净像元(端元)选取和端元波谱分析(端元识别)等系列数据处理为基础。

3.2 OLI数据蚀变矿物提取

OLI数据识别出的端元中共有3种端元与研究区常见矿物波谱特征比较吻合,利用SAM方法对3种端元进行矿物类型识别,端元1与野外白云母和高岭石SAM匹配得分最高分别为0.885和0.902; 端元2与野外绿泥石SAM匹配得分最高为0.913,端元3与野外褐铁矿SAM匹配得分最高为0.847。3种端元与重采样USGS波谱匹配得分情况见表2。因此,确定端元1为Al-OH类蚀变矿物,端元2为Mg-OH类蚀变矿物,端元3为铁染类蚀变矿物。OLI数据3类蚀变矿物丰度如图2所示。

表2   OLI数据3种端元USGS波谱库波谱SAM得分

Tab.2  SAM score of three endmembers of OLI data

端元编号矿物名称SAM 得分
1高岭石+蒙脱石+白云母0.912+0.893+0.886
2绿泥石+绿帘石0.915+0.901
3黄钾铁矾+赤铁矿0.818+0.792

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图2

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图2   3种端元矿物丰度

Fig.2   Abundance of three endmembers


从图2中可以看出,Al-OH类蚀变矿物信息主要呈EW向串珠状集中展布,分布于研究区中部; Mg-OH类蚀变矿物信息大面积团块分布于研究区西南部,东部、北部有零星分布; 铁染类蚀变矿物信息团分布在Al-OH类和Mg-OH类蚀变矿物信息之间,斑块状分布。OLI数据遥感蚀变矿物空间分布基本符合斑岩铜矿围岩蚀变分带特征,即由内而外分别是Al-OH类蚀变矿物(绢云母化+泥化)、铁染类蚀变矿物和Mg-OH类蚀变矿物(青磐岩化)。

3.3 Hyperion数据蚀变矿物提取

Hyperion数据共识别出4种端元分别与研究区常见矿物褐铁矿、绿泥石、高岭石和白云母波谱特征比较吻合,采用SAM方法对这4种端元进行定性和定量分析,确定矿物类型。端元1,2,3和4分别与野外矿物白云母、高岭石、绿泥石和褐铁矿波谱匹配得分最高,分别为0.823,0.849,0.917和0.862。这4种端元与USGS波谱库矿物波谱匹配得分情况见表3。因此,确定端元1为白云母,端元2为高岭石,端元3为绿泥石,端元4为褐铁矿。依据这4种端元矿物波谱分别进行蚀变矿物提取,提取结果如图3所示。

表3   Hyperion数据4种端元USGS波谱库SAM匹配得分

Tab.3  SAM score of four endmembers of Hyperion data

端元编号矿物名称SAM 得分
1白云母0.864
2高岭石+蒙脱石0.859+0.825
3绿泥石0.888
4黄钾铁矾+针铁矿+褐铁矿0.837+0.819+0.813

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图3

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图3   4种端元矿物丰度

Fig.3   Abundance of four endmembers


对比图2和图3可以发现,OLI数据与Hyperion数据提取的蚀变矿物空间分布基本一致。OLI数据Al-OH类矿物主要包含白云母、高岭石和蒙脱石信息,图2(a)中的1和2与图3(a)中的1和2白云母信息对应,图2(a)中的3与图3(b)中的3高岭石+蒙脱石信息对应; OLI数据Mg-OH类矿物与Hyperion数据绿泥石空间分布一致,图2(b)中的4和5与图3(c)中的4和5绿泥石信息对应; OLI数据铁染类矿物与Hyperion数据铁染类矿物空间分布一致,图2(c)中的6和7与图3(d)中的6和7铁染类矿物对应。

3.4 OLI和Hyperion数据遥感蚀变分带特征

2种数据遥感蚀变分带特征如图4所示。

图4

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图4   OLI和Hyperion数据遥感蚀变分带特征

Fig.4   Characteristics of remote sensing alteration zones of OLI and Hyperion data


从图4中可以发现,OLI数据Al-OH类矿物主要包含白云母、高岭石和蒙脱石信息,对应斑岩铜矿的绢英岩化+泥化蚀变带,分布在内带; Mg-OH类矿物主要包含绿泥石信息,绿泥石是青磐岩化带主要蚀变矿物,对应青磐岩化蚀变带,分布在外带; OLI数据和Hyperion数据铁染类蚀变矿物空间分布基本一致,主要分布在内外带之间。在图4(a)中,铁格隆斑岩铜矿远景区围岩蚀变由中心向外围依次是Al-OH类(绢云母+高岭石+蒙脱石)和Mg-OH类(绿泥石)蚀变矿物组合分带,对应为绢英岩化+泥化带和青磐岩化带围岩蚀变分带特征,铁染类矿物组合则分布在绢英岩化+泥化带和青磐岩化带之间; 在图4(b)中,铁格隆斑岩铜矿远景区围岩蚀变由中心向外围依次是绢云母、绢云母+高岭石、高岭石和绿泥石4种蚀变矿物组合分带,对应于绢英岩化带→绢英岩化带叠加泥化带→泥化带→青磐岩化带的围岩蚀变分带特征,青磐岩化带在矿区的西南部最为发育。总体上,OLI和Hyperion数据遥感蚀变分带特征一致,符合斑岩铜矿围岩蚀变分带特征。

4 结论与展望

通过分析斑岩型铜矿典型蚀变矿物(组合)在Landsat8 OLI数据各波段的吸收和反射特征,基于MTMF方法进行了西藏多龙矿集区铁格隆研究区地表蚀变矿物组合的提取研究,得出以下结论:

1)对比Hyperion数据蚀变矿物信息提取结果,OLI数据能够有效区分和识别Al-OH类、Mg-OH类和含Fe3+类蚀变矿物信息。

2)对应斑岩型矿床地表蚀变分带模型,OLI数据Al-OH类(绢英岩化+泥化)、Mg-OH类(青磐岩化)和含Fe3+类遥感蚀变矿物组合的分带特征良好。

此外,OLI数据较TM和ETM+数据将热红外波段细分为TIR1和TIR2 2个波段,其中,硅化蚀变矿物在TIR2波段(11.5~12.51 μm)具有强吸收特征,对识别硅化蚀变矿物信息十分有利; 矽卡岩型铜矿围岩蚀变中富C O32-类蚀变矿物信息,C O32-类蚀变矿物在2.34 μm附近具有显著吸收特征,与Mg-OH类矿物波谱吸收特征十分相近,但在可见光—近红外波段有别于Mg-OH类蚀变矿物信息。因此,基于OLI数据的硅化信息提取和C O32-类与Mg-OH类蚀变信息识别与区分,将是今后OLI数据应用研究的重要方面。

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