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28 October 2006 Application of RS-based multivariate geological information for mineral resources prediction in vegetation zones
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Proceedings Volume 6418, Geoinformatics 2006: GNSS and Integrated Geospatial Applications; 64181I (2006) https://doi.org/10.1117/12.713134
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China
Abstract
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.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhifang Zhao, Jianping Chen, Jian Bai, and Chengxing Jiang "Application of RS-based multivariate geological information for mineral resources prediction in vegetation zones", Proc. SPIE 6418, Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 64181I (28 October 2006); https://doi.org/10.1117/12.713134
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