Remote sensing alteration anomaly is an important indicator of ore deposits. However, the previous studies of the geological genesis of remote sensing alteration anomaly and its indicative significance are insufficient and, as a result, the interpretation of remote sensing anomalies is often uncertain. In this paper, the authors established a multi-data source and multi-method collaborative processing method that can quantitatively explain the geological genesis and indication significance of remote sensing alteration anomaly. In this method, multi-spectral remote sensing is used to analyze the distribution characteristics of remote sensing alteration anomaly, hyperspectral remote sensing is used to decompose the remote sensing alteration anomaly information development pattern of typical geological bodies, and X-diffraction-rock identification-spectral solution is used to accurately identify altered mineral types. On the basis of the above experimental results, the geological genesis and indication significance are comprehensively explained. The test conducted in Fangshankou area of Beishan shows that the types of remote sensing alteration anomalies are basically consistent with the types of altered minerals developed on the surface, and the surrounding rock alterations of different ore-forming types of deposits can be effectively reflected by the specific altered mineral information combination. According to this regularity, lots of mineralization clues were discovered in this experiment, which realized the rapid transformation of remote sensing information into geological information. The study results show that this collaborative processing method would overcome the incompleteness of the analytical results of a single data source or a single method, and could improve the credibility of remote sensing alteration anomalies in geological applications.
韩海辉,任广利,王艺霖,杨敏,姚安强,张转. 协同处理方式在遥感蚀变异常成因分析中的应用——以北山方山口地区为例[J]. 国土资源遥感, 2020, 32(1): 138-147.
Haihui HAN,Guangli REN,Yilin WANG,Min YANG,Anqiang YAO,Zhuan ZHANG. Application of collaborative processing method to the analysis of remote sensing alteration abnormal causes: A case study of Fangshankou area in Beishan. Remote Sensing for Land & Resources, 2020, 32(1): 138-147.
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