Mathematical Geology

, Volume 20, Issue 7, pp 825–861 | Cite as

Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario

  • E. C. Grunsky
  • F. P. Agterberg


A study of the lithogeochemistry of metavolcanics in the Ben Nevis area of Ontario, Canada has shown that factor analysis methods can distinguish lithogeochemical trends related to different geological processes, most notably, the principal compositional variation related to the volcanic stratigraphy and zones of carbonate alteration associated with the presence of sulphides and gold. Auto- and cross-correlation functions have been estimated for the two-dimensional distribution of various elements in the area. These functions allow computation of spatial factors in which patterns of multivariate relationships are dependent upon the spatial auto- and cross-correlation of the components. Because of the anisotropy of primary compositions of the volcanics, some spatial factor patterns are difficult to interpret. Isotropically distributed variables such as CO 2 are delineated clearly in spatial factor maps. For anisotropically distributed variables (SiO 2 ), as the neighborhood becomes smaller, the spacial factor maps becomes better. Interpretation of spatial factors requires computation of the corresponding amplitude vectors from the eigenvalue solution. This vector reflects relative amplitudes by which the variables follow the spatial factors. Instability of some eigenvalue solutions requires that caution be used in interpreting the resulting factor patterns. A measure of the predictive power of the spatial factors can be determined from autocorrelation coefficients and squared multiple correlation coefficients that indicate which variables are significant in any given factor. The spatial factor approach utilizes spatial relationships of variables in conjunction with systematic variation of variables representing geological processes. This approach can yield potential exploration targets based on the spatial continuity of alteration haloes that reflect mineralization.

Key words

auto-correlation cross-correlation lithogeochemistry multiple correlation coefficients trend eigenvector amplitude eigenvector 

List of symbols


Scalar factor that minimizes the discrepancy between andUi


Radius of circular neighborhood used for estimating auto- and cross-correlation coefficients


Distance for which transition matrixU is estimated


Distance between observed valuesi andj


Expected value


Row vector of residuals in the standardized model


Quadratic function of distancedij F(dij)=a+bdij+cd ij 2


Diagonal matrix of the eigenvalues ofU


Eigenvalue of the matrixU;ith diagonal element ofL


Number of observations


Number of variables


Total predictive power ofU


Correlation matrix of the variables


Variance-covariance signal matrix of the standardized variables at origin;j is the index related tod andD (e.g.,j=1 ford=500 m,D=1000 m)


Matrix of auto- and cross-correlation coefficients evaluated at a given distance within the neighborhood


Multiple correlation coefficient squared for themth variable


Column vectori of the signal values


Residual variance for variablek


Amplitude vector corresponding toVi;ith row ofT=V−1


Total variation in the system


Nonsymmetric transition matrix formed by post-multiplyingR 01 −1 byRij


Componenti of the matrixU, corresponding to theith eigenvectorVi;Ui=λiViTi


ComponentUi multiplied byci


Sum of componentsUi+Uj


Eigenvector of the matrixU;ith column ofV withU′V=VL


Weighting factor; equal to the ratio of two eigenvalues


Random variable at pointi


Value of random variable at pointi


Residual ofxi


Row vectori for the standardized variables


Standardized value of variable


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Copyright information

© International Association for Mathematical Geology 1988

Authors and Affiliations

  • E. C. Grunsky
    • 1
  • F. P. Agterberg
    • 2
  1. 1.Ontario Geological SurveyTorontoCanada
  2. 2.Geological Survey of CanadaOttawaCanada

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