摘要
针对在地表被沉积物覆盖的地区开展遥感蚀变矿物信息提取存在很大困难这一问题,以超大型镍矿——东昆仑夏日哈木地区及与其临区为实验区,在分析区内蚀变矿物光谱特征后,利用ASTER和WorldView-2遥感数据,通过主成分分析法提取遥感蚀变异常,并对其分布特征和地质意义进行分析,进而探讨浅覆盖区遥感蚀变异常有效识别与地质应用问题。实验表明:浅覆盖的东昆仑地区,只要地表含有一定的蚀变矿物,在测试并分析蚀变矿物光谱特征后再提取蚀变信息会有效提高结果的精确性;利用WorldView-2数据提取的高分铁染异常与研究区存在的地面探槽及已知矿床的开采范围高度吻合,且很小面积的异常区也被有效捕捉,显示出WorldView-2数据较ASTER数据能够更精确地识别铁染异常,这为今后快速寻找存在"铁帽"的矿床提供了一个高精度指示线索。
How to use remote sensing images to recognize the mineralization alternation anomalies at shallow loess covering and poor exposed bedrock areas is a hotspot in geological remote sensing researches.In this paper,we firstly made a detailed investigation and analysis for altered mineral spectral characteristic,and then,we processed the ASTER and WorldView-2 images using principal component analysis to extract the alteration anomalies in Xiarihamu Ni sulfide deposit and Haxiyatu-lalinggaoli test area.Furthermore,the anomaly distribution and geological features were thoroughly discussed in order to efficiently discriminate altered rocks and promote geological applications.The experimental results show that the extraction accuracy can be improved after analyzing the spectral absorption characteristics if the earth surface contains alteration minerals at shallow loess covering and poor exposed bedrock areas.Moreover,the iron alteration anomalies extracted by WorldView-2 shows a very high coincidence with exploratory trench and exploitation range of known deposits than ASTER,and some very small abnormal areas are also recognized,so we can believe that WorldView-2 is better than ASTER in iron alteration anomaly extraction.This finding will provide a high accuracy indication clues to find the ore deposit with the“iron cap”phenomenon in the future.
作者
韩海辉
王艺霖
张转
李健强
高婷
HAN Haihui;WANG Yilin;ZHANG Zhuan;LI Jianqiang;GAO Ting(Xi’an Center of China Geological Survey,Xi’an 710054,China;Key Laboratory for the Study of Focused Magmatism and Giant Ore Deposits,MLR,Xi’an 710054,China;Institute of Geological Engineering and Surveying,Chang’an University,Xi’an 710054,China)
出处
《遥感信息》
CSCD
北大核心
2018年第4期72-79,共8页
Remote Sensing Information
基金
国家自然科学基金(41502312)
中国地质调查局项目(DD20160009)
