EXTRACTION OF ALTERED MINERAL INFORMATION BASED ON WorldView-3 DATA: An Application of Pobei Area in Xinjiang
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摘要:
使用WorldView-3数据, 采用主成分分析法, 确定反映铁氧化物、含Al(OH)矿物、含Fe, Mg(OH)矿物的主成分, 利用主成分提取新疆坡北地区蚀变矿物信息, 预测成矿远景区. 采用主成分分析法分析了WorldView-3数据的主成分特征向量矩阵, 使用USGS波谱库分析了蚀变矿物组合的吸收反射波谱特征. 上述二者共同确定了反映蚀变矿物信息的主成分. 利用确定的主成分采用密度分割法, 并结合遥感地质解译图提取蚀变矿物信息. 利用已提取的蚀变矿物信息, 结合研究区内含矿地层、与矿化相关的石英脉、控制矿体分布的北东向断裂构造, 预测成矿远景区.
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关键词:
- WorldView-3 /
- 主成分分析法 /
- 蚀变矿物 /
- 成矿远景区 /
- 新疆
Abstract:Based on the WorldView-3 data, the principal component analysis (PCA) is adopted to determine the principal components reflecting iron oxides, and Al(OH)-bearing and Fe, Mg(OH)-bearing minerals, by which the altered mineral information in Pobei area of Xinjiang is extracted to predict the metallogenic prospect area. The principal component eigenvector matrix of WorldView-3 data is analyzed by PCA, and USGS spectral library is used to analyze the absorption reflectance spectra characteristics of altered mineral associations, which both determine the principal components reflecting altered mineral information. Based on the determined principal components, the density slicing method are used, with remote sensing interpretation map, for the extraction of altered mineral information. With the extracted altered mineral information, the metallogenic prospect area is predicted by combining with the ore-bearing strata, quartz veins related with mineralization, and NE-trending fault structure controlling the distribution of orebodies in the area.
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Key words:
- WorldView-3 /
- principal component analysis /
- altered mineral /
- metallogenic prospect area /
- Xinjiang
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表 1 WorldView－3数据波段特点
Table 1. Characteristics of WorldView-3 data band
波段号 波段 中心波长/μm 空间分辨率/m 波段号 波段 中心波长/μm 空间分辨率/m 1 VNIR 0.425 2 9 SWIR 1.21 7.5 2 0.48 2 10 1.57 7.5 3 0.545 2 11 1.66 7.5 4 0.605 2 12 1.73 7.5 5 0.66 2 13 2.165 7.5 6 0.725 2 14 2.205 7.5 7 0.825 2 15 2.26 7.5 8 0.91 2 16 2.33 7.5 
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表 2 WorldView－3数据VNIR主成分分析特征向量矩阵
Table 2. Eigenvector matrix of WorldView-3 VNIR data by PCA
Band1 Band2 Band3 Band4 Band5 Band6 Band7 Band8 PC1 -0.296 -0.278 -0.319 -0.354 -0.357 -0.39 -0.388 -0.421 PC2 -0.686 -0.452 -0.226 0.001 0.157 0.24 0.291 0.328 PC3 -0.437 0.288 0.437 -0.124 0.391 -0.247 0.284 -0.474 PC4 0.453 -0.275 -0.288 -0.415 0.157 -0.117 0.629 -0.175 PC5 0.052 -0.209 -0.149 0.46 0.023 0.513 0.008 -0.675 PC6 -0.196 0.646 -0.343 -0.232 -0.421 0.345 0.27 -0.047 PC7 -0.044 -0.169 0.292 0.394 -0.651 -0.307 0.458 0.003 PC8 -0.043 0.264 0.59 0.516 0.262 -0.489 0.07 0.035
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表 3 WorldView－3数据SWIR主成分分析特征向量矩阵
Table 3. Eigenvector matrix of WorldView-3 SWIR data by PCA
Band1 Band2 Band3 Band4 Band5 Band6 Band7 Band8 PC1 0.382 0.311 0.359 0.418 0.356 0.348 0.32 0.321 PC2 -0.824 0.379 0.163 -0.083 0.354 0.085 -0.038 0.095 PC3 0.22 0.659 0.211 -0.213 -0.298 -0.501 -0.201 0.216 PC4 0.058 0.155 0.265 0.431 0.102 -0.037 -0.435 -0.718 PC5 -0.301 -0.295 0.203 0.641 -0.302 -0.424 0.071 0.307 PC6 -0.164 0.137 0.318 -0.08 -0.675 0.403 0.394 -0.27 PC7 0.007 -0.174 0.233 -0.049 -0.21 0.469 -0.704 0.391 PC8 0.065 -0.406 0.73 -0.409 0.239 -0.24 0.1 -0.073
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