Information Extraction of Typical Alteration Zone of Porphyry Copper Deposit and Delineation of Prospective Areas in Southern Peru
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摘要: 不同斑岩铜矿的构造地质、成矿时代和控矿因素具有其特殊性, 所以需要针对具体的矿区开展地质调查以获取矿区标志性蚀变矿物组合信息, 从而利用遥感技术手段提取矿区蚀变信息, 确定找矿远景区.通过综合分析秘鲁南部斑岩铜矿地质特征、控矿因素, 确立了以泥化-绢英岩化和青磐岩化组合蚀变矿物带为遥感找矿指示标志, 并以ASTER数据为遥感数据源开展蚀变矿物信息提取技术研究, 结合已有矿区地质资料、高光谱影像和实地勘查结果, 验证了典型蚀变带矿物信息提取结果的可靠性, 另圈定了2处找矿远景区.在综合分析、梳理已有研究基础上, 构建了多光谱遥感找矿模式, 并在智利、阿根廷等其他多个斑岩铜矿区取得了较好的应用效果.Abstract: Every porphyry copper deposit has different geological structure, metallogenic epoch and ore controlling factors. Therefore, it is necessary to carry out geological investigation to obtain the information of the iconic alteration mineral assemblage to extract the alteration information by using remote sensing technology and determine prospective areas. A comprehensive analysis of geological characteristics and ore controlling factors of porphyry copper deposit in southern Peru is carried out in this study. The combined information of argillic- and phyllic-alteration and propylitic alteration is established as the ore prospecting indicator and the technology of alteration information extraction is explored with ASTER data. Combined with existing mining geological data, hyperspectral image and field survey results, alteration extraction results are confirmed as reliable and effective and the other two prospective areas are delineated. The multi-spectral remote sensing prospecting model is constructed and its application proves successful in other porphyry copper districts such as Chile, Argentina.
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图 1 研究区位置及1∶10万地质图
l.Q-fl.冲积和崩积；2.Q-al.河流沉积物；3.Qp-m.冰碛沉积；4.KTi-di/gd.花岗闪长岩；5.KTi-gr.花岗岩；6.KTi-in.Inogoya地层；7.KTi-qp.Quellaveco斑岩；8.KTi-qsa.Alta系列；9.KTi-qst.Toquepala系列；10.KTi-qt.Tinajones流纹岩；11.KTi-qy.Yarito流纹岩；12.KTi-to.Toquepala地层；13.KTi-vpa.Paralaque火山岩；14.Ti-da/mz.英安岩和二长岩；15.TQ-vba.Barroso火山岩；16.Ts-ca.Capillune地层；17.Ts-moi.下Moquegua地层；18.Ts-mos.上Moquegua地层；19.Ts-vhu.Huailillas火山岩
Fig. 1. Study area location and 1∶100 000 geological sketch
图 2 标准波谱库中蚀变矿物光谱曲线(a)及重采样至ASTER波段后波谱曲线(b)
Fig. 2. The spectral curves of alteration minerals in USGS (a) and in ASTER bandpasses (b) after resample
图 4 AST_01泥化-绢英岩化类(a)和青磐岩化带(b)蚀变矿物信息分布
Fig. 4. The argillic- and phyllic-altered mineral assemblage (a) and propylitic altered mineral assemblage (b) with AST_01 data
图 5 AST_02泥化-绢英岩化类(a)和青磐岩化带(b)蚀变矿物信息分布
Fig. 5. The argillic- and phyllic-altered mineral assemblage (a) and propylitic altered mineral assemblage (b) with AST_02 data
图 6 奎拉维科斑岩铜矿区蚀变带矿物信息分布(a)和地质图(b)
1.流纹质凝灰岩；2.角砾岩；3.二长斑岩；4.花岗闪长岩；5.流纹岩；6.第四系；7.矿化范围
Fig. 6. The alteration mineral assemblage (a) and geological map (b) of porphyry copper deposit in Quellaveco
图 7 4-A区东部浅层腐蚀土下的大面积泥化-绢英岩化蚀变
Fig. 7. A large area of argillic and phyllic alteration under humus surface in the eastern part of 4-A region
图 8 4-B区斑岩铜矿蚀变带Quick Bird高分辨率卫星影像
Fig. 8. Quick Bird hyperspectra image of typical alteration of porphyry copper deposit in 4-B region
表 1 AST_01数据主成分分析特征向量矩阵
Table 1. Eigenvector matrix of principal component analysis of AST_01 data
Band 1 Band 4 Band 6 Band 7 Band 1 Band 3 Band 4 Band 8 PC1 -0.505 593 -0.505 047 -0.469 697 -0.518 351 PC1 0.511 250 0.479 195 0.506 965 0.501 979 PC2 -0.818 415 0.534 061 0.175 795 0.118 624 PC2 0.763 555 -0.102 731 -0.636 456 -0.036 810 PC3 0.271 987 0.674 424 -0.412 340 -0.548 772 PC3 0.001 662 -0.764 375 0.088 434 0.638 677 PC4 -0.024 388 0.069 702 -0.760 564 0.645 050 PC4 0.394 469 -0.418 987 0.574 535 -0.582 026 
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表 2 AST_02数据主成分分析特征向量矩阵
Table 2. Eigenvector matrix of principal component analysis of AST_02 data
Band 1 Band 4 Band 6 Band 7 Band 1 Band 3 Band 4 Band 8 PC1 0.447 876 0.560 978 0.477 413 0.506 742 PC1 0.408 123 0.513 387 0.563 064 0.502 821 PC2 0.893 562 -0.284 518 -0.261 890 -0.228 059 PC2 0.559 064 0.513 116 -0.492 022 -0.426 701 PC3 -0.003 431 -0.777 332 0.449 850 0.439 748 PC3 0.481 901 0.466 604 -0.450 188 0.589 392 PC4 -0.030 705 -0.010 386 -0.707 903 0.705 566 PC4 0.537 266 -0.505 397 0.488 061 -0.466 599
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