EXPLORATION AND PRACTICE OF 1: 50000 GEOLOGICAL MAPPING TECHNIQUES FOR ALPINE-GORGE AREA: A CASE STUDY IN BEISHAN AREA OF WUSHI, XINJIANG
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摘要: 新疆乌什北山填图试点项目充分发挥遥感技术的先导作用,探索1:50000高山峡谷区填图方法。不同分辨率遥感数据在岩性、构造解译等方面的差异表明多源遥感数据综合解译能有效提高解译程度。研究认为同一遥感数据最佳波段组合图像、Landsat-8和Worldview-2数据协同图像增强了对岩性和构造识别的能力。高光谱遥感矿物填图和岩性分类、基于ASTER热红外遥感数据的岩石化学成分填图等是高山峡谷区填图有效技术方法。利用ETM和ASTER数据开展矿化蚀变信息提取,结果表明ASTER较ETM数据在铁染异常、羟基异常等提取方面具有更大的优势。分析认为多元信息综合预测是区域找矿的重要途径。根据乌什北山地质地貌特征,选择其中有效技术方法或技术方法组合开展1:50000地质填图,结果显示在减少剖面测制和路线地质调查数量的同时,达到了填图精度,并取得了若干重要研究成果,为区域构造演化和成矿规律分析总结提供了资料支撑。Abstract: The 1:50000 geological mapping project makes sufficient use of remote sensing technologies to explore new methods of geological mapping in alpine-gorge area of Wushi, Xinjiang. Different spatial resolution remote sensing data can reveal different characteristics of lithology and structures. Integrated interpretations of multi-source remote sensing data can enhance the ability of interpretation effectively. Higher accuracy interpretations of lithologic classification can be obtained by combination of different bands of the same remote sensing image according to the optimum index factor (OIF) and synergestic images of Landsat-8 and worldview-2. Hyperspectral mineral mapping and lithology identification, petrochemistry components mapping using ASTER thermal infrared remote sensing data are useful technologies of geological mapping in alpine-gorge area. Both of the ETM and ASTER data were used to extract alteration information. The result shows that the ASTER data is more useful than ETM data in extracting alteration anomalies, such as ferric contamination anomaly and hydroxyl anomaly. Therefore, it is an important way of multivariate information comprehensive analysis to prospect. Based on the geological and geomorphological features, effective techniques have been selected to complete 1:50000 geological mapping of the North Mountain, Wushen area, and important research results have been obtained. The sufficient use of remote sensing data can reduce the amount of geologic section and geological survey routes, and achieve the mapping precision.
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表 1 Spot5影像各波段数据统计特征值及各波段间相关系数矩阵
Table 1. Statistic eigenvalues and correlation coefficient matrix for bands of SPOT5
波段 最小值 最大值 均值 标准差 相关系数 Band 1 Band 2 Band 3 Band 4 Band 1 0 255 77.485 33.134 Band 1 1.000000 0.897210 0.886567 0.848133 Band 2 0 255 56.163 31.990 Band 2 0.897210 1.000000 0.994379 0.707432 Band 3 0 255 56.588 30.646 Band 3 0.886567 0.994379 1.000000 0.668707 Band 4 15 201 87.089 24.271 Band 4 0.848133 0.707432 0.668707 1.000000 表 2 Quickbird影像各波段数据统计特征值及各波段间相关系数矩阵
Table 2. Statistic eigenvalues and correlation coefficient matrix for bands of Quickbird
波段 最小值 最大值 均值 标准差 相关系数 Band 1 Band 2 Band 3 Band 4 Band 1 0 1342 507.711 166.118 Band 1 1.000000 0.799445 0.817561 0.775338 Band 2 0 1178 384.326 129.077 Band 2 0.799445 1.000000 0.986813 0.954843 Band 3 0 1425 482.756 142.270 Band 3 0.817561 0.986813 1.000000 0.984581 Band 4 0 802 294.572 76.028 Band 4 0.775338 0.954843 0.984580 1.000000 表 3 Geoeye-1影像各波段数据统计特征值及各波段间相关系数矩阵
Table 3. Statistic eigenvalues and correlation coefficient matrix for bands of Geoeye-1
波段 最小值 最大值 均值 标准差 相关系数 Band 1 Band 2 Band 3 Band 4 Band 1 0 3061 246.776 490.529 Band 1 1.000000 0.990143 0.979206 0.953965 Band 2 0 1682 117.880 212.023 Band 2 0.990143 1.000000 0.993400 0.973462 Band 3 0 2655 262.247 296.175 Band 3 0.979206 0.993400 1.000000 0.991808 Band 4 0 2086 233.266 173.932 Band 4 0.953965 0.973462 0.991808 1.000000 表 4 ETM1, 3, 4, 5波段主成分变换特征向量矩阵
Table 4. Eigenvector statistics for ETM band 1, 3, 4 and 5
主成分 TM1 TM3 TM4 TM5 PC1 0.45460 0.55101 0.47559 0.51336 PC2 -0.55332 -0.39852 0.24776 0.68821 PC3 -0.10604 0.38590 -0.80948 0.42962 PC4 -0.68988 0.62342 0.23910 -0.27974 表 5 ETM 1, 4, 5, 7波段主成分变换特征向量矩阵
Table 5. Eigenvector statistics for ETM band 1, 4, 5 and 7
主成分 TM1 TM4 TM5 TM7 PC1 0.46652 0.50298 0.55470 0.47083 PC2 0.77310 0.16771 -0.47247 -0.38855 PC3 -0.40502 0.80432 -0.02006 -0.43431 PC4 0.14367 -0.26823 0.68460 -0.66237 表 6 ASTER 1, 2, 3, 4波段主成分变换特征向量矩阵
Table 6. Eigenvector statistics for ASTER band 1, 2, 3 and 4
主成分 ASTER1 ASTER2 ASTER3 ASTER4 PC1 -0.32791 -0.36411 -0.46219 -0.73910 PC2 0.52264 0.53843 0.20825 -0.62736 PC3 0.24623 0.37636 -0.85951 0.24283 PC4 -0.74746 0.66020 0.06524 -0.03442 表 7 ASTER 1, 3, 4, (5+6)/2主成分变换特征向量矩阵
Table 7. Eigenvector statistics for ASTER band 1、3、4 and (5+6)/2
主成分 ASTER1 ASTER3 ASTER4 ASTER(5+6)/2 PC1 0.29182 0.41810 0.68145 0.52503 PC2 0.63120 0.57799 -0.30069 -0.42084 PC3 -0.51442 0.35968 0.47487 -0.61685 PC4 -0.50180 0.60146 -0.46873 0.40833 表 8 ASTER 1, 3, 4, 8主成分变换特征向量矩阵
Table 8. Eigenvector statistics for ASTER band 1、3、4 and 8
主成分 ASTER1 ASTER3 ASTER4 ASTER8 PC1 0.29166 0.41401 0.68170 0.52803 PC2 0.49962 0.64353 -0.17682 -0.55225 PC3 0.70933 -0.30017 -0.46186 0.43982 PC4 -0.40270 0.56953 -0.53918 0.47197 表 9 ASTER1、ASTER3、ASTER4、ASTER5主成分变换特征向量矩阵
Table 9. Eigenvector statistics for ASTER band 1、3、4 and 5
主成分 ASTER1 ASTER3 ASTER4 ASTER5 PC1 0.28050 0.39882 0.65579 0.57637 PC2 0.57972 0.64566 -0.27923 -0.41119 PC3 -0.70521 0.53074 0.29824 -0.36338 PC4 0.29652 -0.37733 0.63484 -0.60554 -
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