Working Paper Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 19 October 2019 / Approved: 22 October 2019 / Online: 22 October 2019 (04:26:18 CEST)

How to cite: Xu, K.; Wang, X.; Kong, C.; Feng, R.; Liu, G.; Wu, C. Identification of Hydrothermal Alteration Minerals for Exploring Gold Deposit Based on SVM and PCA Using ASTER Data: A Case Study of Gulong. Preprints 2019, 2019100251 Xu, K.; Wang, X.; Kong, C.; Feng, R.; Liu, G.; Wu, C. Identification of Hydrothermal Alteration Minerals for Exploring Gold Deposit Based on SVM and PCA Using ASTER Data: A Case Study of Gulong. Preprints 2019, 2019100251

Abstract

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 method is used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference bas been removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information has been extracted based on principal component analysis (PCA) and support vector machine (SVM) method; Thirdly, the favorable areas of gold mining in Gulong has been delineated by using ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; Lastly, field survey 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.

Subject Areas

gold deposit; alteration information; ASTER image; support vector machine (SVM); principal component analysis (PCA)

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