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

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 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. using different methods in different locations for mineral exploration, including alteration mineral mapping in the Northwestern Junggar basin using Landsat thematic mapper (TM) data and principal component analysis (PCA) [17], predictive mineral prospectivity modeling for Cu deposits in Varzaghan district, NW Iran based on the support vector machine (SVM) [18], as well as predictive models for Rodalquilar mining district mineral prospectivity in the southeast of Spain with machine learning [19]. All of these studies contribute to the development of metallogenic prognosis in arid and semi-arid areas. However, research on altered mineral extraction in high vegetation-covered areas is still limited, particularly in a specific region as an important metal mineral area [20,21], where many old mines are facing resource depletion after long-term exploitation, such as Gulong, China.In response to the above problems, the objectives of this paper are to: (1) select the image identification marks of altered minerals according to the distribution of gold deposits and the ore-bearing strata in Dayaoshan; (2) remove vegetation interference by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; (3) extract altered mineral information based on PCA and SVM methods by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data; and (4) verify the altered mineral extraction result using field survey method. Lastly, the research results not only delineate the favorable metallogenic area of gold deposits, but also provide reasonable suggestions for mineral exploration with remote sensing data. Study Area and MaterialsThe study area, located at the junction of the Yangtze paleo-plate and Cathaysia paleo-plate, is part of the Guangxi Dayaoshan Au ore belt, dotted with gold deposits [22][23][24][25] (Figure 1). It belongs to the subtropical monsoon climate region, where the annual average temperature is 21 • C, the frost-free days are about 320 days, and the average annual rainfall is...

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