A spatially weighted principal component analysis for multi-element geochemical data for mapping locations of felsic intrusions in the Gejiu mineral district of Yunnan, China

  • Authors:

  • Qiuming Cheng;Greame Bonham-Carter;Wenlei Wang;Shengyuan Zhang;Wenchang Li;Xia Qinglin

  • Affiliations:

  • Department of Earth and Space Science and Engineering, Department of Geography, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3 and State Key Lab of Geological Processes and M ...;Geological Survey of Canada, 601 Booth St., Ottawa, Ontario, Canada K1A 0E8;Department of Earth and Space Science and Engineering, Department of Geography, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3;Department of Earth and Space Science and Engineering, Department of Geography, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3 and Institute of Resources and Environment, Shi ...;Yunnan Academy of Geological Survey, Kunming 650051, Yunnan, China;State Key Lab of Geological Processes and Mineral Resources, China University of Geosciences, China

  • Venue:
  • Computers & Geosciences
  • Year:
  • 2011

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Abstract

Principal component analysis (PCA) is frequently used in geosciences for information extraction. In many applications, masking PCA has been used to create subsets of samples or sub-areas to enhance the effect of the main objects of interest. In this paper we suggest how the representativeness of samples or pixels can be quantified using a fuzzy membership function based on fuzzy set theory. In this new method, the relative importance of pixels or samples can be taken into account using a multivariate statistical method such as PCA. A Fuzzy Masking PCA is proposed and implemented in GeoDAS GIS on the basis of a spatially weighted PCA (SWPCA). This paper introduces the mathematical treatment of the fuzzy masking PCA and follows a case study of identifying the locations of intrusive bodies from geochemical data in the Gejiu mineral district in Yunnan, China. Power-law functions based on the inverse distance from mapped felsic intrusions are applied as weighting functions in FMPCA. The results indicate that fuzzy mask PCA increases the signal-noise ratio of the component representing igneous intrusions and decreases the influence of sedimentary rocks. The areas delineated as potential areas for new intrusions (including buried intrusions) are valuable guides for Sn mineral prospecting.