4.6 Article

Identifying geochemical anomalies associated with Cu and Pb-Zn skarn mineralization using principal component analysis and spectrum-area fractal modeling in the Gangdese Belt, Tibet (China)

Journal

JOURNAL OF GEOCHEMICAL EXPLORATION
Volume 111, Issue 1-2, Pages 13-22

Publisher

ELSEVIER
DOI: 10.1016/j.gexplo.2011.06.012

Keywords

Geochemical mapping; Principal component analysis; Spectrum-area fractal modeling; Cu and Pb-Zn Skarn mineralization; Gangdese

Funding

  1. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences [MSFGPMR200912]
  2. China University of Geosciences (Wuhan)
  3. National Natural Science Foundation of China [41002118]
  4. China Postdoctoral Science Foundation [20100480356]

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The Gangdese Belt is now recognized as an important Cu polymetallic mineralization belt. Recent studies suggest that apart from porphyry copper deposits, polymetallic skarn deposits are another significant deposit type in this belt. In this paper, principal component analysis (PCA) and spectrum-area (S-A) fractal modeling are used to identify geochemical anomalies associated with Cu and Pb-Zn skarn mineralization based on Cu, Pb, Zn and Ag stream sediment data. Firstly, the raster maps of Cu, Pb, Zn and Ag were obtained by multifractal inverse distance weighted (MIDW). Secondly, PCA was used to combine the Cu, Pb, Zn and Ag concentration values. Finally, S-A analysis was used to decompose the first component pattern obtained by the PCA. The results show that major anomalies of Cu, Pb, Zn and Ag are mostly located around mapped intrusions, and along E-W trending faults, where Cu and Pb-Zn skarn deposits favorably occur. These results indicate that: (1) the Gangdese Belt potentially hosts undiscovered Cu and Pb-Zn skarn deposits, and the integrated anomalies for Cu, Pb, Zn and Ag occurring around the intrusions and in the vicinity of faults in the center part of the Gangdese Belt should be further investigated in the next step of mineral resource exploration; and (2) the hybrid method combining PCA and S-A modeling is an effective tool to identify geochemical anomalies. (C) 2011 Elsevier B.V. All rights reserved.

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