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Open AccessArticle

Identification of Hydrothermal Alteration Minerals for Exploring Gold Deposits Based on SVM and PCA Using ASTER Data: A Case Study of Gulong

by Kai Xu 1,2,3, Xiaofeng Wang 1, Chunfang Kong 1,2,4,*, Ruyi Feng 1, Gang Liu 1 and Chonglong Wu 1,2
1
School of Computer, China University of Geosciences, Wuhan 430074, China
2
Innovation Center of Mineral Resources Exploration Engineering Technology in Bedrock Area, Ministry of Natural Resources, Guiyang 550081, China
3
Key Laboratory of Tectonics and Petroleum Resources (China University of Geosciences), Ministry of Education, Wuhan 430074, China
4
National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns, Hengyang 421000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(24), 3003; https://doi.org/10.3390/rs11243003
Received: 3 December 2019 / Accepted: 11 December 2019 / Published: 13 December 2019
Dayaoshan, as an important metal ore-producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing methods are used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference was removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information was extracted based on principal component analysis (PCA) and support vector machine (SVM) methods; thirdly, the favorable areas of gold mining in Gulong was delineated by using the ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; and lastly, field surveys verified that the extracted alteration mineralization information is correct and effective. The results show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing. View Full-Text
Keywords: gold deposit; alteration information; ASTER image; support vector machine (SVM); principal component analysis (PCA) gold deposit; alteration information; ASTER image; support vector machine (SVM); principal component analysis (PCA)
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Xu, K.; Wang, X.; Kong, C.; Feng, R.; Liu, G.; Wu, C. Identification of Hydrothermal Alteration Minerals for Exploring Gold Deposits Based on SVM and PCA Using ASTER Data: A Case Study of Gulong. Remote Sens. 2019, 11, 3003.

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