Many ETM (Enhanced Thematic Mapper) digital image processing methods such as ratio and principal component analysis (PCA) have been developed in exploration with spectral feature reflected in ETM data. Unfortunately, by our knowledge, there is no clear idea in ascertainment on abnormality component in PCA and no quantitative scale to classify abnormality for alteration. In this study, to improve exploration efficiency, the rules of ascertainment on abnormality component and classification on abnormality for alteration were established. PCA with ETM bands 1, 4, 5, 7 and ETM bands 1, 3, 4, 5 with interferential factors masked for OH- alteration and Fe2+ and Fe3+ alteration respectively were conducted. Meanwhile, the rules for ascertainment on alteration abnormality by PCA for OH- or Fe2+ and Fe3+ alteration were well established by the contribution of their diagnostic spectral bands. That is, the abnormity component of principal components (PCs) for OH- alteration can be ascertained by obtaining positive contribution from ETM band 5 but negative from ETM band 7, while that of Fe2+ and Fe3+ alteration obtained positive contribution from ETM band 3 but negative from ETM bands 1, 4 and 5. Alteration grades were delineated by the standard deviation σ and average μof pixel value of abnormality component which coincides with probability density function. Accordingly, drill holes conducted in potential areas of alteration, a famous mineralization belt in China, Northwestern Yunnan, also reveals that alteration-related information extracted from ETM data is practical and effective in mineral application.
To get a sound method for mineral prediction in dense vegetation zones, this study applies RS and GIS technologies to predict mineral resources in Genma and Cangyuan of Yunnan, P.R.C., where mineralization is concentrative but little breakthrough is achieved in exploring mineral deposits resulting from dense vegetation covers. Methods on the geological application of RS in dense vegetation zones are developed in the study, and practically proven to be effective. Based on GIS, mineralization and alteration indicators for vegetation zones are formulated by applying the ETM RS multi-functional image processing techniques. Along with RS-based multivariate geological indicators, geological, geophysical and geochemical data are integrated and used to construct quantitative models for mineral resources prediction and assessment using Information Quantification Method. Based on the models, mineral deposits are digitally predicted, and accordingly information on deposit formation and control is effectively derived and optimized. The information is verified through all-around field surveys in the target areas, and satisfactory results are obtained. Hence, the techniques and methods in the study are worthy of extension.
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