Multivariate outlier detection in exploration geochemistry

A new method for multivariate outlier detection able to distinguish between extreme values of a normal distribution and values originating from a different distribution (outliers) is presented. To facilitate visualising multivariate outliers spatially on a map, the multivariate outlier plot, is introduced. In this plot different symbols refer to a distance measure from the centre of the distribution, taking into account the shape of the distribution, and different colours are used to signify the magnitude of the values for each variable. The method is illustrated using a real geochemical data set from far-northern Europe. It is demonstrated that important processes such as the input of metals from contamination sources and the contribution of sea-salts via marine aerosols to the soil can be identified and separated.

[1]  Ramanathan Gnanadesikan,et al.  Methods for statistical data analysis of multivariate observations , 1977, A Wiley publication in applied statistics.

[2]  H. E. Hawkes,et al.  Geochemistry in Mineral Exploration , 1962 .

[3]  P. Révész,et al.  Strong approximations in probability and statistics , 1981 .

[4]  V. Yohai,et al.  Robust Estimation of Multivariate Location and Scatter , 2006 .

[5]  C. Y. Chork.  Unmasking multivariate anomalous observations in exploration geochemical data from sheeted-vein tin mineralization near Emmaville, N.S.W., Australia , 1990 .

[6]  Georg Ch. Pflug,et al.  Mathematical statistics and applications , 1985 .

[7]  C. Y. Chork,et al.  Interpreting exploration geochemical data from Outokumpu, Finland: a MVE-robust factor analysis , 1993 .

[8]  E. M. Cameron,et al.  Book Review: Geochemistry in mineral exploration. Second edition by A. W. ROSE, H. E. HAWKES and J. S. WEBB. Academic Press, 1979, 657 pp. 64.50 (hardcover), 29.00 (softcover) , 1981 .

[9]  P. Rousseeuw.  Least Median of Squares Regression , 1984 .

[10]  Clemens Reimann,et al.  Processes influencing the chemical composition of the O-horizon of podzols along a 500-km north-south profile from the coast of the Barents Sea to the Arctic Circle. , 2000 .

[11]  Katrien van Driessen,et al.  A Fast Algorithm for the Minimum Covariance Determinant Estimator , 1999, Technometrics.

[12]  D. Ruppert.  Robust Statistics: The Approach Based on Influence Functions , 1987 .

[13]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[14]  D. G. Simpson,et al.  Unmasking Multivariate Outliers and Leverage Points: Comment , 1990 .

[15]  P. Rousseeuw,et al.  Unmasking Multivariate Outliers and Leverage Points , 1990 .

[16]  J. Eriksson,et al.  Agricultural soils in Northern Europe: a geochemical atlas. , 2003 .

[17]  Clemens Reimann,et al.  Factor analysis applied to regional geochemical data: problems and possibilities , 2002 .

[18]  R. Garrett.  The chi-square plot: a tool for multivariate outlier recognition , 1989 .

[19]  P. Rousseeuw.  Multivariate estimation with high breakdown point , 1985 .

[20]  C. Jennison,et al.  Robust Statistics: The Approach Based on Influence Functions , 1987 .

[21]  Daniel Gervini,et al.  A robust and efficient adaptive reweighted estimator of multivariate location and scatter , 2003 .

[22]  Clemens Reimann,et al.  Background and threshold: critical comparison of methods of determination. , 2005, The Science of the total environment.