RESEARCH ON EXTRACTION OF REMOTE SENSING ALTERATION ANOMALIES IN JIAMA COPPER DEPOSIT IN GANGDISE METALLOGENIC BELT, TIBET
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摘要: 以西藏冈底斯成矿带甲马铜矿作为研究区，基于围岩矿化蚀变理论，利用ASTER遥感影像对矿化异常信息进行提取。首先对ASTER影像进行预处理，包括重采样、辐射定标、大气校正和去干扰；然后对甲马矿床内典型矿物蚀变（矽卡岩化、绿泥石化、碳酸盐化）信息进行探测，以USGS光谱库内典型蚀变矿物的波谱特征为参照编制矿物特征分析表并进行波谱特征分析，在对前人已有方法的实验、比较和总结的基础上，选择研究区内的各蚀变类型特征向量组，提取对应的3类蚀变矿物信息；最后综合研究区内3类异常信息圈定了蚀变异常靶区，证明了该方法在类似地区提取蚀变工作的指导性。Abstract: Taking Jiama Copper of Gangdise area in Tibet Province as research area, based on the theory of surrounding rock mineralized alteration, the alterative information was extracted by ASTER data. First, ASTER data was preprocessed which included resampling, radiometric calibration, atmospheric correction and filtering interference. Then the typical mineral alteration (skarnization, chloritization, carbonatization) in Jiama copper was detected. Taking the spectral characteristics of typical altered minerals in USGS spectral library as reference, the analysis table of mineral characteristics, and analysis the spectral character were carried. Based on the previous experiment, we contrasted and summarized the methods, and then chosen eigenvector groups of each alterative type, with extracting three corresponding kinds of alterative mineralize information. Finally, compositing three kinds of alteration overlap regions; it coincided with the former research results, which indicated that this method had guidance in similar area for extracting alterative information.
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Key words:
- metallogenic belt of Gangdise /
- Jiama copper deposits /
- ASTER /
- alterative information
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图 6 甲马地区综合异常图
（绿色为矽卡岩化蚀变，蓝色为绿泥石化蚀变，红色为碳酸盐化蚀变）
Figure 6. Composite anomaly map of alteration in Jiama area
表 1 大气校正参数（据文献[23]修改）
Table 1. The atmosphere correction parameters
表 2 地物干扰消除方法（据杨金中[24]修改）
Table 2. Methods of removing interferences from surface features
表 3 研究区ASTER数据光谱特征分析（据张玉君等[7, 25]修改）
Table 3. Analysis of spectral characteristics of ASTER data in the study area
表 4 主成分1, 3, 4, (8+9)/2特征向量表
Table 4. Eigenvectors of PCA 1, 3, 4, (8+9)/2
表 5 主成分1, 2, 3, 4, 8, 9特征向量表
Table 5. Eigenvectors of PCA 1, 2, 3, 4, 8, 9
表 6 主成分1, 3, 4, (5+8)/2特征向量表
Table 6. Eigenvectors of PCA 1, 3, 4, (5+8)/2
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