基于Aster数据的斑岩铜矿蚀变分带识别方法研究

蚀变矿物虽然在光谱上具有诊断特征，但是和其它地物目标相比属于一种弱信息，需要进行数据挖掘来展现隐含的信息。多年来遥感工作者不断探索蚀变矿物识别的方法，在多光谱中已成功应用比值法、主成分分析法等。但是比值法常针对的是单波段运算，忽略了蚀变矿物的组合光谱特征。本文针对这个问题，在新疆包古图斑岩铜矿Ⅱ号和Ⅴ号矿体中利用Aster数据，探讨其典型蚀变矿物在Aster光谱中的响应特征，利用由比值法原理衍生出的相关吸收波段深度法（RBD）和逻辑算法进行蚀变分带识别研究。结果表明，由于逻辑算法考虑了蚀变矿物组合的整体多个光谱特征， 其识别效果比基于单个光谱特征的RBD法减少了假异常，提高了识别精度。

Research on identification methods of porphyry alteration zones based on Aster data

Alteration minerals have diagnostic features in the spectrum， but it belongs to a kind of weak information in comparison with other targets， and it needs data mining to show the implicit information. Over the years， remote sensing workers explored methods of identification of alteration minerals， the methods of ratio and principal component analysis had been successfully applied in the multi—channel remote sensing. But the ratio methods often carried out band operations based on single spectral， ignoring the combination of spectral characteristics of altered minerals. For solving this problem， this paper used Aster data of Baogutu porphyry copper deposit， Xinjiang II and V ore body， to explore the Aster spectral response characteristics of the typical alteration minerals， using two methods of absorption band depth （RBD） and logical algorithm for alteration zoning identification research. The results showed that， due to multiple logical algorithms considering multi—spectral characteristics of alteration mineral assemblages， the recognition effect was better than single RBD method based on single spectral characteristics. It reduced the false anomalies， improved the accuracy of recognition.