Extraction of mineral alteration information and mineralization prospecting analysis based on GF−5 hyperspectral in Zhule−Mangla, Tibet
-
摘要:
研究目的 近年来，遥感在地质调查和矿产勘查领域取得了广泛的应用，基于多光谱遥感数据的蚀变矿物填图为地质找矿工作提供了重要技术支撑，然而基于国产高光谱遥感数据在此领域的研究却为数不多。高分五号（GF−5）较小的波谱间隔提供了相比于多光谱更为丰富的目标地物波谱信息，为矿物的精细识别提供了良好的数据源。本文主要基于GF−5开展西藏革吉南地区的矿物蚀变信息提取，同时结合Landsat−8、ASTER多光谱数据提取结果叠加对比，综合野外调查验证，进一步深化遥感在地质矿产资源调查领域的应用。
研究方法 基于多光谱数据建立了不同类别蚀变矿物的光谱指数模型，在GF−5数据蚀变信息提取方面，摒弃了传统的光谱角匹配等方法，提出了基于决策树分类辅助混合调谐匹配滤波技术进行矿化蚀变信息的提取方法，最后综合区域构造、蚀变信息提取结果等要素，圈定成矿有利区，并开展野外调查验证。
研究结果 基于Landsat−8、ASTER两种多光谱数据对铁染、羟基类（Mg−OH、Al−OH）、碳酸盐类矿物信息进行了增强与提取；基于GF−5数据识别出了方解石、钠云母、普通白云母、多硅白云母、明矾石、高岭石、地开石、绿帘石8种蚀变矿物。
结论 结合不同数据源的提取与叠加结果，证实了本文提出的矿化蚀变信息提取方法的可行性。根据野外验证情况综合揭示了该地区发育高硫型浅成低温热液蚀变矿物组合，具有斑岩−浅成低温热液矿床的成矿潜力。本文认为高光谱与多光谱数据相结合有助于后续蚀变分带的分析与更精确的成矿预测，从而更好地服务于矿产勘查工程等领域。
Abstract:This paper is the result of mineral exploration engineering.
Objective Remote sensing has been widely used in geological survey and mineral exploration in recent years. Alteration mineral mapping using multispectral remote sensing data provides important technical support for geological prospecting. However, only a limited number of surveys were carried out based on the Chinese hyperspectral remote sensing data. The smaller spectral interval of the GaoFen−5 (GF−5) hyperspectral remote sensing data provides a richer spectral information of geological targets than multispectral data, which offers an ideal data source for mineral identification. This paper was mainly focusing on the extraction of alteration minerals based on GF−5 data in the south of Geji, Tibet. Moreover, the alteration minerals extracted from GF−5 were overlaid and compared with the results extracted from Landsat−8 and ASTER data. The results were verified by field survey, which could deepen the application of remote sensing in mineral resources investigation.
Methods A spectral index model for different alteration minerals was proposed based on the multispectral data. During the extraction of alteration information from GF−5 data, traditional methods such as spectral angle mapper were abandoned, while the decision tree algorithm was adopted to support mixture tuned matched filter for the extraction of mineralized alteration information. Finally, regional structure, alteration information and other factors were integrated to delineate mineralization anomalous targets. The field investigation was carried out to verify the technical reliability.
Results The information of iron−stained, hydroxyl (Mg−OH, Al−OH) and carbonate minerals was enhanced and extracted using Landsat−8 and ASTER. Eight alteration minerals including calcite, paragonite, muscovite, phengite, alunite, kaolinite, dickite and chlorite were identified by GF−5.
Conclusions Combined with the extraction and superimposition results from different data sources, the reliability of the mineralization alteration information extraction method proposed in this paper was confirmed. The field investigation results showed that the area is characterized by high−sulfur epithermal alteration mineral assemblage, which has the potential for porphyry−epithermal hydrothermal deposit. It is suggested that the combination of hyperspectral and multispectral could help the subsequent analysis of alteration zonation and provide accurate mineralization prediction for prospecting, so as to serve the sector of mineral exploration engineering.
-
-
表 1 保留的波段及波长范围（共计290个波段）
Table 1. Bands remained and wavelength ranges (290 bands in total)
保留波段号（波段序号） 波长范围/nm 1~150(1~150) 390~1029 155~191(151~187) 1038~1342 202~245(188~231) 1435~1797 266~324(232~290) 1974~2463 
下载: 导出CSV
表 2 ASTER数据Band1、3、4、8主成分得分
Table 2. Changes in principal components of band 1, 3, 4 and 8 based on ASTER data
主成分 Band1 Band3 Band4 Band8 PC1 −0.463516 −0.860375 −0.173869 −0.121149 PC2 −0.025923 0.252097 −0.774868 −0.579098 PC3 0.885530 −0.442635 −0.125650 −0.064204 PC4 −0.017808 0.016595 −0.594610 0.803646
下载: 导出CSV
表 3 ASTER数据Band7、8、9主成分得分
Table 3. Changes in principal components of band 7, 8 and 9 based on ASTER data
主成分 Band7 Band8 Band9 PC1 −0.628682 −0.557667 −0.542002 PC2 −0.759775 0.291838 0.581010 PC3 0.165833 −0.777070 0.607175
下载: 导出CSV
-
[1] Chen Chao, He Chunxiao, Shi Shanqiu, Zhou Shaoguang. 2016. Research on remote sensing images classification based on decision tree[J]. Geospatial Information, 14(8): 50−51, 60, 5 (in Chinese with English abstract).
[2] Chen Q, Zhao Z F, Zhou J Xi, Zhu R F, Xia J S, Sun T, Zhao X, Chao J Q. 2022. ASTER and GF−5 satellite data for mapping hydrothermal alteration minerals in the Longtoushan Pb−Zn deposit, SW China[J]. Remote Sensing, 14(5): 1253. doi: 10.3390/rs14051253
[3] Dai Jingjing, Wang Runsheng. 2013. Spectral characteristics of typical transparent mineral groups[J]. Geological Science and Technology Information, 32(2): 8−14 (in Chinese with English abstract).
[4] Fang Caili, Zhao Yaliang. 2012. Research on destriping algorithm for hyperspectral images[J]. Computer Engineering and Applications, 48(12): 158−162 (in Chinese with English abstract).
[5] Fang Sen, Jiao Shuhong. 2019. Multi−endmember hysperspectral image unmixing based on group optimization[J]. Applied Science and Technology, 46(6): 20−24, 52 (in Chinese with English abstract).
[6] Gao Ke, Song Yang, Liu Zhibo, Yang Huanhuan, Lin Bin, Li Faqiao. 2024. Constraints on metallogenic age from cryptoexplosive breccia in Naruo Cu(Au) deposit, Xizang[J]. Geology in China, 51(2): 385−398 (in Chinese with English abstract).
[7] Gao Yang, Xi Ge, Chen Chun Xia, Xiu Liancun, Zheng Zhizhong, Yan Peisheng, Dong Jinxin, Yan Baikun, Liu Jiahang, Zhao Yuhao, Yu Zhengkui. 2023. The application of a small airborne hyperspectral imaging spectrometer on remote sensing geology and lithology interpretation in alpine canyon regions[J]. Geology in China, 50(4): 1032−1043 (in Chinese with English abstract).
[8] Guha A, Saw A K, Mukherjee A, Verma C B, Kumar K V, Rao P V N, Diwakar P G. 2022. Eigen vector based analysis of Landsat OLI principal components and constrained energy minimization maps for discriminating iron enriched zones in banded iron formation (BIF) in Sidhi, Madhya Pradesh[J]. Geocarto International, 37(7): 1880−1898. doi: 10.1080/10106049.2020.1801861
[9] Huang W X, Zhang L M, Si X L, Xu H Y, Chen H Y, Li X, Zhao Y H. 2020. On−orbit performance evaluation of on−board calibration component of GF−5 visible and infrared multispectral image[J]. Acta Optica Sinica, 40(20): 2029001. doi: 10.3788/AOS202040.2029001
[10] Jain R, Sharma R U. 2019. Airborne hyperspectral data for mineral mapping in Southeastern Rajasthan, India[J]. International Journal of Applied Earth Observation and Geoinformation, 81: 137−145. doi: 10.1016/j.jag.2019.05.007
[11] Jiang Qi, Dai Jingjing, Wang Denghong, Tian Shufang. 2021. Application of optical remote sensing to identifying granite pegmatite lithium deposits[J]. Mineral Deposits, 40(4): 793−804 (in Chinese with English abstract).
[12] Kumar C, Chatterjee S, Oommen T, Guha A. 2020. Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS−NG hyperspectral data in Gold−bearing granite−greenstone rocks in Hutti, India[J]. International Journal of Applied Earth Observation and Geoinformation, 86: 102006. doi: 10.1016/j.jag.2019.102006
[13] Lian Dunmei, Guo Na, Hu Zhenghua, Long Tuojiang, Yuan Shan, MiMa Wangjiu. 2020. Relationship between spectral characteristics of sericite and mineral composition and mineralization of Zhuxi Copper−Tungsten deposit in Jiangxi[J]. China Tungsten Industry, 35(5): 20−34 (in Chinese with English abstract).
[14] Liu H Z, Wu K, Xu H G, Xu Y. 2021. Lithology classification using TASI thermal infrared hyperspectral data with convolutional neural networks[J]. Remote Sensing, 13(16): 3117. doi: 10.3390/rs13163117
[15] Liu Shuai, Xing Guanglong. 2020. Multi−dimensional convolutional network collaborative unmixing method for hyperspectral image mixed pixels[J]. Journal of Geodesy and Geoinformation Science, 49(12): 1600−1608 (in Chinese with English abstract).
[16] Liu Tingyue, Dai Jingjing, Zhao Yuanyi, Tian Shufang, Ye Chuanyong. 2021. Remote sensing inversion of lithium concentration in salt lake using LightGBM: A case study of northern Zabuye salt lake in Tibet[J]. Acta Geologica Sinica, 95(7): 2249−2256 (in Chinese with English abstract).
[17] Lü Xinbiao, Zhu Jiang, Cao Xiaofeng, Chen Chao, Wu Chunming, Hu Qincheng. 2012. Magmatismand its metallogenetic effects duringthe Paleozoic−Triassiccontinental crustal construction in the Liuyuan area, south Beishan, NW China[J]. Geological Scienceand Technology Information, 31(5): 119−127, 135 (in Chinese with English abstract).
[18] Ouyang Yuan, Liu Hong, Li Guangming, Ma Dongfang, Zhang Linkui, Huang Hanxiao, Zhang Jinghua, Zhang Tengjiao, Liu Xiao, Zhao Yinbin, Li Fu. 2023. Mineral search prediction based on Random Forest algorithm: A case study on porphyry−epithermal copper polymetallic deposits in the western Gangdise meatallogenic belt[J]. Geology in China, 50(2): 303−330 (in Chinese with English abstract).
[19] Pi Yuanzheng, Chu Dong, Guan Xiaobin, Shen Huanfeng. 2020. Strip noise removal in GF−5 images considering the difference in land and water[J]. Journal of Remote Sensing, 24(4): 360−367 (in Chinese with English abstract).
[20] Rui Zongyao, Lu Yan, Li Guangming, Wang Longsheng, Wang Yitian. 2003. Looking forward to the prospects of porphyry copper deposits in Tibet[J]. Geology in China, 30(3): 302−308 (in Chinese with English abstract).
[21] Su Wei, Jiang Fangfang, Zhu Dehai, Zhan Junge, Ma Hongyuan, Zhang Xiaodong. 2015. Extraction of maize planting area based on decision tree and mixed−pixel unmixing methods[J]. Transactions of the Chinese Society of Agricultural Machinery, 46(9): 289−295, 301 (in Chinese with English abstract).
[22] Sun L, Khan S D, Sarmiento S, Lakshmikantha M R, Zhou H W. 2017. Ground−based hyperspectral imaging and terrestrial laser scanning for fracture characterization in the Mississippian Boone formation[J]. International Journal of Applied Earth Observation and Geoinformation, 63: 222−233. doi: 10.1016/j.jag.2017.08.008
[23] Sun Yongbin, Wang Ruijun, Wei Benzan, Wang Bing, Dong Shuangfa, Li Cunjin, Li Mingsong. 2018. The application of hyperspectral remote sensing ground−air integrated prediction method to the copper gold deposit prospecting in Kalatag area, Xinjiang[J]. Geology in China, 45(1): 178−191 (in Chinese with English abstract).
[24] Sun Yu, Liu Jiajun, Zhao Yingjun, Zhai Degao, Liu Zhenjiang, Zhang Fangfang, Qin Kai, Tian Feng, Kang Xiangyang, Pei Chengkai, Liu Pengfei. 2022a. Extraction of alteration minerals based on the airborne hyperspectral CASI−SASI data and the application to the Xiaojinwozi area in Dunhuang city, Gansu province[J]. Geology and Prospecting, 58(3): 653−664 (in Chinese with English abstract).
[25] Sun Yu, Liu Jiajun, Zhao Yingjun, Zhai Degao, Liu Zhenjiang, Zhang Yafeng, Zhang Fangfang, Tian Feng, Qin Kai. 2022b. Alteration mineral mapping based on the GF−5 hyperspectral data and its geological application: An example of the Huaniushan area in Guazhou County of Gansu Province[J]. Geology in China, 49(2): 558−574 (in Chinese with English abstract).
[26] Sun Yunzhu, Jiang Guangwei, Li Yunduan, Yang Yong, Dai Haishan, He Jun, Ye Qinghao, Cao Qiong, Dong Changzhe, Zhao Shaohua, Wang Weihe. 2018. GF−5 satellite: Overview and application prospects[J]. Spacecraft Recovery and Remote Sensing, 39(3): 1−13 (in Chinese with English abstract).
[27] Tang J X, Yang H H, Song Y, Wang L Q, Liu Z B, Li B L, Lin B, Peng B, Wang G H, Zeng Q G, Wang Q, Chen W, Wang N, Li Z J, Li Y B, Li Y B, Li H F, Lei C Y. 2021. The copper polymetallic deposits and resource potential in the Tibet Plateau[J]. China Geology, 4(1): 1−16.
[28] Tian Chenhua, Yang Liqiang, He Wenyan, Zhang Shaoying, Liu Shentai, Wu Cai. 2022. Characteristics of short wave infrared spectroscopy of sericite group minerals and their implications for exploration in the Yulong porphyry copper deposit, Tibet[J]. Sedimentary Geology and Tethyan Geology, 42(1): 40−49 (in Chinese with English abstract).
[29] Tian Qinglin, Pan Wei, Li Yao, Zhang Chuan, Chen Xuejiao, Yu Changfa. 2019. Extraction of alteration information from hyperspectral core imaging based on wavelet packet transform and weight spectral angle mapper[J]. Remote Sensing for Land and Resources, 31(4): 41−46 (in Chinese with English abstract).
[30] van Ruitenbeek F J A, Debba P, van der Meer F D, Cudahy T, van der Meijde M, Hale M. 2006. Mapping white micas and their absorption wavelengths using hyperspectral band ratios[J]. Remote Sensing of Environment, 102(3−4): 211−222. doi: 10.1016/j.rse.2006.02.012
[31] Wang Chongchang, Xue Rongrong, Zhao Shihu, Liu Shuhan, Wang Xia, Li Hongzhou, Liu Ziqin. 2021. Quality evaluation and analysis of GF−5 hyperspectral image data[J]. Geography and Geo−information Science, 37(1): 33−38, 125 (in Chinese with English abstract).
[32] Wang Xi, Cheng Sanyou, Lin Haixing, Chen Jing, Xiao Liang. 2022. Research on alteration information extraction in Saishiteng area of Qinghai based on Aster and Landsat8 data[J]. Geological Review, 68(1): 262−280 (in Chinese with English abstract).
[33] Wei Shaogang, Song Yang, Tang Juxing, Gao Ke, Feng Jun, Li Yanbo, Hou Lin. 2016. Geochronology, geochemistry and petrogenesis of quartz diorite porphyrite from the Sena copper (gold) deposit, Tibet[J]. Geology in China, 43(6): 1894−1912 (in Chinese with English abstract).
[34] Wei Xiangpo, Yu Xuchu, Fu Qiongying, Liu Bing, Xue Zhixiang. 2016. Spectral matching classification approach and experiment combined with spectral angle cosine and spectral correlation coefficient[J]. Geography and Geo−Information Science, 32(3): 29−33, 2 (in Chinese with English abstract).
[35] Ye Fawang, Meng Shu, Zhang Chuan, Xu Qingjun, Liu Hongcheng, Wu Ding. 2018. Minerageny study of high−Al, medium−Al−and low−Al sericites identified by airborne hyperspectral remote sensing technology[J]. Acta Geologica Sinica, 92(2): 395−412 (in Chinese with English abstract).
[36] 陈超, 赫春晓, 石善球, 周绍光. 2016. 一种基于决策树方法的遥感影像分类研究[J]. 地理空间信息, 14(8): 50−51,60,5. doi: 10.3969/j.issn.1672-4623.2016.08.017
[37] 代晶晶, 王润生. 2013. 常见透明矿物类波谱特征研究综述[J]. 地质科技情报, 32(2): 8−14.
[38] 房彩丽, 赵雅靓. 2012. 高光谱图像条带噪声去除算法研究[J]. 计算机工程与应用, 48(12): 158−16. doi: 10.3778/j.issn.1002-8331.2012.12.032
[39] 房森, 焦淑红. 2019. 分组寻优的多端元高光谱图像解混方法. 应用科技, 46(6): 20−24, 52.
[40] 高轲, 宋扬, 刘治博, 杨欢欢, 林彬, 李发桥. 2024. 西藏拿若铜(金)矿床隐爆角砾岩对成矿时代的约束[J]. 中国地质, 51(2): 385−398.
[41] 高扬, 奚歌, 陈春霞, 修连存, 郑志忠, 颜培胜, 董金鑫, 闫柏琨, 柳稼航, 赵宇浩, 俞正奎. 2023. 小型机载高光谱成像仪在高山峡谷地区遥感地质岩性解译应用[J]. 中国地质, 50(4): 1032−1043.
[42] 姜琪, 代晶晶, 王登红, 田淑芳. 2021. 光学遥感在识别花岗伟晶岩型锂矿床中的应用[J]. 矿床地质, 40(4): 793−804.
[43] 连敦梅, 郭娜, 胡正华, 龙沱江, 袁珊, 米玛旺久. 2020. 江西朱溪铜钨矿床绢云母光谱特征与矿物成分、矿化的关系研究[J]. 中国钨业, 35(5): 20−34.
[44] 刘帅, 邢光龙. 2020. 高光谱图像混合像元多维卷积网络协同分解法[J]. 测绘学报, 49(12): 1600−1608.
[45] 刘婷玥, 代晶晶, 赵元艺, 田淑芳, 叶传永. 2021. 基于LightGBM的盐湖锂浓度遥感反演研究—以西藏扎布耶盐湖北湖为例[J]. 地质学报, 95(7): 2249−2256.
[46] 吕新彪, 朱江, 曹晓峰, 陈超, 吴春明, 胡庆成. 2012. 北山南部柳园地区古生代−早中生代大陆地壳增生过程中的岩浆活动与成矿效应[J]. 地质科技情报, 31(5): 119−127, 135.
[47] 欧阳渊, 刘洪, 李光明, 马东方, 张林奎, 黄瀚霄, 张景华, 张腾蛟, 柳潇, 赵银兵, 李富. 2023. 基于随机森林算法的找矿预测—以冈底斯成矿带西段斑岩−浅成低温热液型铜多金属矿为例[J]. 中国地质, 50(2): 303−330.
[48] 皮原征, 储栋, 管小彬, 沈焕锋. 2020. 顾及水陆差异的高分五号影像条带去除[J]. 遥感学报, 24(4): 360−367.
[49] 芮宗瑶, 陆彦, 李光明, 王龙生, 王义天. 2003. 西藏斑岩铜矿的前景展望[J]. 中国地质, 30(3): 302−308.
[50] 苏伟, 姜方方, 朱德海, 展郡鸽, 马鸿元, 张晓东. 2015. 基于决策树和混合像元分解的玉米种植面积提取方法[J]. 农业机械学报, 46(9): 289−295, 301.
[51] 孙永彬, 王瑞军, 魏本赞, 汪冰, 董双发, 李存金, 李名松. 2018. 高光谱遥感地空综合预测方法在新疆卡拉塔格地区铜金矿床找矿中的应用[J]. 中国地质, 45(1): 178−191. doi: 10.12029/gc20180115
[52] 孙雨, 刘家军, 赵英俊, 翟德高, 柳振江, 张方方, 秦凯, 田丰, 康向阳, 裴承凯, 刘鹏飞. 2022a. 航空高光谱CASI−SASI数据蚀变矿物信息提取与应用—以甘肃省敦煌市小金窝子地区为例[J]. 地质与勘探, 58(3): 653−664.
[53] 孙雨, 刘家军, 赵英俊, 翟德高, 柳振江, 张亚峰, 张方方, 田丰, 秦凯. 2022b. 基于GF−5高光谱数据的蚀变矿物填图及地质应用—以甘肃省瓜州县花牛山地区为例. 中国地质, 49(2): 558−574.
[54] 孙允珠, 蒋光伟, 李云端, 杨勇, 代海山, 何军, 叶擎昊, 曹琼, 董长哲, 赵少华, 王维和. 2018. “高分五号”卫星概况及应用前景展望[J]. 航天返回与遥感, 39(3): 1−13.
[55] 田成华, 杨立强, 和文言, 张少颖, 刘申态, 吴才. 2022. 西藏玉龙斑岩铜矿床绢云母族蚀变矿物短波红外光谱特征及对勘查的指示意义[J]. 沉积与特提斯地质, 42(1): 40−49.
[56] 田青林, 潘蔚, 李瑶, 张川, 陈雪娇, 余长发. 2019. 基于小波包变换和权重光谱角制图的岩心高光谱蚀变信息提取[J]. 国土资源遥感, 31(4): 41−46.
[57] 王崇倡, 薛荣荣, 赵世湖, 刘书含, 王霞, 李鸿洲, 刘梓钦. 2021. GF−5卫星高光谱影像数据质量评价与分析[J]. 地理与地理信息科学, 37(1): 33−38, 125. doi: 10.3969/j.issn.1672-0504.2021.01.006
[58] 王曦, 程三友, 林海星, 陈静, 肖良. 2022. 基于Aster和Landsat8数据在青海赛什腾地区蚀变信息提取研究[J]. 地质论评, 68(1): 262−280.
[59] 韦少港, 宋扬, 唐菊兴, 高轲, 冯军, 李彦波, 侯淋. 2016. 西藏色那铜(金)矿床石英闪长玢岩年代学、地球化学与岩石成因[J]. 中国地质, 43(6): 1894−1912.
[60] 魏祥坡, 余旭初, 付琼莹, 刘冰, 薛志祥. 2016. 光谱角余弦与相关系数测度组合的光谱匹配分类方法与实验[J]. 地理与地理信息科学, 32(3): 29−33, 2. doi: 10.3969/j.issn.1672-0504.2016.03.006
[61] 叶发旺, 孟树, 张川, 徐清俊, 刘洪成, 武鼎. 2018. 航空高光谱识别的高、中、低铝绢云母矿物成因学研究[J]. 地质学报, 92(2): 395−412. doi: 10.3969/j.issn.0001-5717.2018.02.013
-
图(11)
表(3)
计量
- 文章访问数: 112
- PDF下载数: 10
- 施引文献: 0