Multi-element association analysis of stream sediment geochemistry data for predicting gold deposits in south-central Yunnan Province, China

The use of traditional statistical and new methods of spatial analysis can provide suitable indicators of geochemical element dispersion, and aid in targeting potential areas for mineral exploration. A case study involving the analysis of stream sediment geochemistry data from an area in Yunnan province, South China, is presented. The area has two known areas of gold deposits where several mines are present. The study starts with an appraisal of the descriptive statistics of the data. Geological studies were conducted in the study area to obtain a thorough understanding of the regional geology, ore geology of known gold deposits, and mineralization, and to determine the mineral deposit model. Most of the GIS analysis was done using the stream sediment geochemical data. An inverse distance weighting interpolation algorithm was used to convert the point data to continuous surface (grid) maps for each element. Principal component analysis (PCA) was used to compress the information to a few maps and to assist in determining multi-element associations. The study revealed that most of the high element concentrations in stream sediments were found in the Ailaoshan metamorphic belt. Gold deposits were found to be associated with ultramafic intrusives within the Ailaoshan metamorphic belt and the ultramafic instrusives are associated with principal component images that represent multi-element associations related to gold mineralization. The first two principal components possibly represent two different types of gold associations and phases of mineralization. The study demonstrates the usefulness of applying PCA to geochemical data to produce maps that reveal different associations useful for gold exploration.

[1]  U. Schärer,et al.  The Ailao Shan-Red River shear zone (Yunnan, China), Tertiary transform boundary of Indochina , 1995 .

[2]  Q. Cheng,et al.  Modelling lake sediment geochemical distribution using principal component, indicator kriging and multifractal power-spectrum analysis: a case study from Gowganda, Ontario , 2004, Geochemistry: Exploration, Environment, Analysis.

[3]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[4]  J. Harris,et al.  Effective use and interpretation of lithogeochemical data in regional mineral exploration programs: application of Geographic Information Systems (GIS) technology☆ , 2000 .

[5]  Qiuming Cheng,et al.  A fractal filtering technique for processing regional geochemical maps for mineral exploration , 2001, Geochemistry: Exploration, Environment, Analysis.

[6]  Q. Cheng,et al.  The separation of geochemical anomalies from background by fractal methods , 1994 .

[7]  Q. Cheng,et al.  Applications of Spatial Factor Analysis to Multivariate Geochemical Data , 1996 .

[8]  F. Agterberg,et al.  Spatial relationships of multivariate data , 1992 .

[9]  Xie Xuejing,et al.  Geochemical mapping in China , 1997 .

[10]  G. Shi,et al.  Permian of West Yunnan, Southwest China: a biostratigraphic synthesis , 2002 .

[11]  J. Harris,et al.  Analysis of geochemical data for mineral exploration using a GIS — A case study from the Swayze greenstone belt, northern Ontario, Canada , 2001, Geological Society, London, Special Publications.

[12]  Q. Cheng,et al.  Multifractal power spectrum and singularity analysis for modelling stream sediment geochemical distribution patterns to identify anomalies related to gold mineralization in Yunnan Province, South China , 2007, Geochemistry: Exploration, Environment, Analysis.

[13]  J. Campbell.  Introduction to remote sensing , 1987 .

[14]  E. C. Grunsky,et al.  The differentiation of soil types and mineralization from multi-element geochemistry using multivariate methods and digital topography , 1999 .