Recognizing and Validating Structural Processes in Geochemical Data: Examples from a Diamondiferous Kimberlite and a Regional Lake Sediment Geochemical Survey

Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 187)


Geochemical data are compositional in nature and are subject to the problems typically associated with data that are restricted in real positive number space, the simplex. Geochemistry is a proxy for mineralogy, and minerals are comprised of atomically ordered structures that define the placement and abundance of elements in the mineral lattice structure. The arrangement of elements within one or more minerals that comprise rocks, soils, and surficial sediments define a linear model in the Euclidean geometry of real space in terms of their geochemical expression. When methods such as principal component analysis are applied to multielement geochemical data, the dominant components generally reflect features related to mineralogy and describe geologic processes that are both independent and partially codependent. The dominant principal components can be used as a filter to eliminate noise or under-sampled processes in the data. These dominant components can be used to create predictive geological maps, or maps displaying recognizable geochemical processes. Using these techniques, we demonstrate that stoichiometrically controlled geochemical processes can be “discovered” and “validated” from two sets of data, one derived from drill-hole lithogeochemistry of a series of kimberlite eruptions and a second from a suite of granitic, metamorphic, volcanic, and sedimentary rocks.


Geochemistry Classification Lithologic prediction Compositional data analysis 


  1. 1.
    Aitchison, J.: The Statistical Analysis of Compositional Data (Reprinted in 2003 by The Blackburn Press), p. 416. Chapman & Hall Ltd., London (1986)Google Scholar
  2. 2.
    Aitchison, J.: Logratios and natural laws in compositional data analysis. Math. Geol. 31(5), 563–580 (1999)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bivand, R., Pebesma, E., Gomez-Rubio, V.: Applied Spatial Data Analysis with R, 2nd edn, 405pp. Springer (2013)Google Scholar
  4. 4.
    Buccianti, A., Mateu-Figueras, G., Pawlowsky-Glahn, V.: Compositional data analysis in the geosciences: from theory to practice. Geol. Soc. Spec. Publ. 264, 212p (2006)MATHGoogle Scholar
  5. 5.
    Eade, K.E.: Geology, Nueltin Lake, District of Keewatin, Geological Survey of Canada. Preliminary Map 4-1972, 1 sheet (1973a). doi:10.4095/108984
  6. 6.
    Eade, K.E.: Edehon Lake Area, West Half, District of Keewatin, Geological Survey of Canada. Preliminary Map 3-1972, 1973, 1 sheet (1973b). doi:10.4095/108978
  7. 7.
    Egozcue, J.J., Pawlowsky-Glahn, V., Mateu-Figueras, G., Barcelo-Vidal, C.: Isometric Logratio transformations for compositional data analysis. Math. Geol. 35, 279–300 (2003)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Grunsky, E.C.: A program for computing RQ-mode principal components analysis for S-plus and R. Comput. Geosci. 27, 229–235 (2001)CrossRefGoogle Scholar
  9. 9.
    Grunsky, E.C.: The interpretation of geochemical survey data. Geochem. Explor. Environ. Anal. 10, 27–74 (2010)CrossRefGoogle Scholar
  10. 10.
    Grunsky, E.C.: Predicting archean volcanogenic massive sulfide deposit potential from lithogeochemistry: application to the Abitibi greenstone belt. Geochem. Explor. Environ. Anal. 13(2013), 317–336 (2013). doi:10.1144/geochem2012-176 CrossRefGoogle Scholar
  11. 11.
    Grunsky, E.C., Kjarsgaard, B.A.: Classification of eruptive phases of the Star Kimberlite, Saskatchewan, Canada based on statistical treatment of whole-rock geochemical analyses. Appl. Geochem. 23(12), 3321–3336 (2008). (ESS Contribution # 20080330)Google Scholar
  12. 12.
    Grunsky, E.C., Bacon-Shone, J.: The stoichiometry of mineral compositions. In: Proceedings of the 4th International Workshop on Compositional Data Analysis. Sant Feliu de Guixols, Spain (2011)Google Scholar
  13. 13.
    Grunsky, E.C., Corrigan, D., Mueller, U., Bonham-Carter. G-F.: Predictive geologic mapping using lake sediment geochemistry in the Melville Peninsula Geological Survey of Canada, Open File 7171, 1 sheet (2012a). doi:10.4095/291901
  14. 14.
    Grunsky, E.C., McCurdy, M.W., Pehrsson, S.J., Peterson, T.D., Bonham-Carter, G.F.: Predictive geologic mapping and assessing the mineral potential in NTS 65A/B/C, Nunavut, with new regional lake sediment geochemical data; Geological Survey of Canada, Open File 7175, 1 sheet (2012b). doi:10.4095/291920
  15. 15.
    Grunsky, E.C., Mueller, U.A., Corrigan, D.: A study of the lake sediment geochemistry of the Melville Peninsula using multivariate methods: applications for predictive geological mapping. J. Geochem. Explor. 141, 15–41 (2014). doi:10.1016/j.gexplo.2013.07.013 CrossRefGoogle Scholar
  16. 16.
    Hron, K., Templ, M., Filzmoser, P.: Imputation of missing values for compositional data using classical and robust methods. Comput. Stat. Data Anal. 54(12), 3095–3107 (2010)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Martín-Fernández, J.A., Barceló-Vidal, C., Pawlowsky-Glahn, V.: Dealing with zeros and missing values in compositional data sets using nonparametric imputation. Math. Geol. 35(3), 253–278 (2003)Google Scholar
  18. 18.
    Martín-Fernández, J.A., Palarea, J., Olea, R.: Dealing with Zeros, pp. 43-58j, 378 p. Wiley (2011)Google Scholar
  19. 19.
    McCurdy, M.W., McNeil, R.J., Day, S.J.A., Pehrsson, S.J.: Regional lake sediment and water geochemical data, Nueltin Lake area, Nunavut (NTS 65A, 65B and 65C), Geological Survey of Canada, Open File 6986, 13 pp (2012) 1 CD-ROM. doi:10.4095/289888
  20. 20.
    Palarea-Albaladejo, J., Martín-Fernández, J.A.: A modified EM alr-algorithm for replacing rounded zeros in compositional data sets. Comput. Geosci. 34(8), 902–917 (2008)Google Scholar
  21. 21.
    Palarea-Albaladejo, J., Martín-Fernández, J.A., Buccianti, A.: Compositional methods for estimating elemental concentrations below the limit of detection in practice using R. J. Geochem. Explor. 141, 71–77 (2014). doi:10.1016/j.gexplo.2013.09.003 CrossRefGoogle Scholar
  22. 22.
    Pawlowsky-Glahn, V., Buccianti, A. (eds.): Compositional Data Analysis: Theory and Application. Wiley, New York (2011)Google Scholar
  23. 23.
    Pawlowsky-Glahn, V., Egozcue, J.J.: Spatial analysis of compositional data: a historical review. J. Geochem. Explor. (2016). doi:10.1016/j.gexplo.2015.12.010
  24. 24.
    Pawlowsky-Glahn, V., Olea, R.A.: Geostatistical Analysis of Compositional Data, Studies in Mathematical Geology, vol. 7, 181 p. Oxford University PressGoogle Scholar
  25. 25.
    Pearce, T.H.: A contribution to the theory of variation diagrams. Contrib. Mineral. Petrol. 19(2), 142–157 (1968)CrossRefGoogle Scholar
  26. 26.
    Pebesma, E.J.: Multivariable geostatistics in S: the gstat package. Comput. Geosci. 30, 683–691 (2004)CrossRefGoogle Scholar
  27. 27.
    Peterson, T.D., Scott, J.M.J., Jefferson, C.W., Tschirhart, V.: Regional potassic alteration corridors spatially related to the 1750 Ma Nueltin Suite in the northeast Thelon Basin region, Nunavut—guides to uranium, gold and silver? In: Geological Association of Canada-Mineralogical Association of Canada, Joint Annual Meeting, Programs with Abstracts, vol. 35, p. 1 (2012)Google Scholar
  28. 28.
    Peterson, T.D., Scott, J.M.J., Lecheminant, A.N., Chorlton, L.B., D’Aoust, B.M.A.: Geological Survey of Canada, Canadian Geoscience Map 158, 1 sheet (2014). doi:10.4095/293892
  29. 29.
    Peterson, T.D., Scott, J.M.J., LeCheminant, A.N., Jefferson, C.W., Pehrsson, S.J.: The Kivalliq igneous suite: anorogenic bimodal magmatism at 1.75 Ga in the western Churchill Province, Canada. Precambr. Res. 262, 101–119 (2015).
  30. 30.
    QGIS Development Team: QGIS Geographic Information System. Version 2.8.1-Wien. Open Source Geospatial Foundation (2015).
  31. 31.
    R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2014).
  32. 32.
    Stanley, C.R.: Effects of non-conserved denominators on Pearce element ratio diagrams. Math. Geol. 25(8), 1049–1070 (1993)CrossRefGoogle Scholar
  33. 33.
    Tella, S., Paul, D., Berman, R.G., Davis, W.J., Peterson, T.D., Pehrsson, S.J., Kerswill, J.A.: Geological Survey of Canada, Open File 5441, 3 sheets (2007) 1 CD-ROM. doi:10.4095/224573
  34. 34.
    Tolosana-Delgado, R.: Geostatistics for constrained variables: positive data, compositions and probabilities. Applications to environmental hazard monitoring, Ph.D. Thesis, University of Girona, 215p (2006)Google Scholar
  35. 35.
    Urqueta, E., Kyser, T.K., Clark, A.H., Stanley, C.R., Oates, C.J.: Lithogeochemistry of the collahuasi porphyry Cu-Mo and epithermal Cu-Ag (-Au) cluster, northern Chile: pearce element ratio vectors to ore. Geochem. Explor. Environ. Anal. 9(1), 9–17 (2009)CrossRefGoogle Scholar
  36. 36.
    van Breemen, O., Peterson, T.D., Sandeman, H.A.: U-Pb zircon geochronology and Nd isotope geochemistry of proterozoic granitoids in the western Churchill Province: intrusive age pattern and Archean source domains. Can. J. Earth Sci. 42, 339–377 (2005)CrossRefGoogle Scholar
  37. 37.
    Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S, 4th edn, 495 p. Springer, Berlin (2002)Google Scholar

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Authors and Affiliations

  1. 1.Department of Earth and Environmental SciencesUniversity of WaterlooWaterlooCanada
  2. 2.Geological Survey of CanadaOttawaCanada

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