Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Compositional Data Analysis (CoDaWork 2015)

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

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. Aitchison, J.: Logratios and natural laws in compositional data analysis. Math. Geol. 31(5), 563–580 (1999)

    Article  MathSciNet  Google Scholar 

  3. Bivand, R., Pebesma, E., Gomez-Rubio, V.: Applied Spatial Data Analysis with R, 2nd edn, 405pp. Springer (2013)

    Google Scholar 

  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)

    MATH  Google Scholar 

  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. 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. 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)

    Article  MathSciNet  MATH  Google Scholar 

  8. Grunsky, E.C.: A program for computing RQ-mode principal components analysis for S-plus and R. Comput. Geosci. 27, 229–235 (2001)

    Article  Google Scholar 

  9. Grunsky, E.C.: The interpretation of geochemical survey data. Geochem. Explor. Environ. Anal. 10, 27–74 (2010)

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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. 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. 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. 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. 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

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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. Martín-Fernández, J.A., Palarea, J., Olea, R.: Dealing with Zeros, pp. 43-58j, 378 p. Wiley (2011)

    Google Scholar 

  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. 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. 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

    Article  Google Scholar 

  22. Pawlowsky-Glahn, V., Buccianti, A. (eds.): Compositional Data Analysis: Theory and Application. Wiley, New York (2011)

    Google Scholar 

  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

    Google Scholar 

  24. Pawlowsky-Glahn, V., Olea, R.A.: Geostatistical Analysis of Compositional Data, Studies in Mathematical Geology, vol. 7, 181 p. Oxford University Press

    Google Scholar 

  25. Pearce, T.H.: A contribution to the theory of variation diagrams. Contrib. Mineral. Petrol. 19(2), 142–157 (1968)

    Article  Google Scholar 

  26. Pebesma, E.J.: Multivariable geostatistics in S: the gstat package. Comput. Geosci. 30, 683–691 (2004)

    Article  Google Scholar 

  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. 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. 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). http://dx.doi.org/10.1016/j.precamres.2015.02.019

    Google Scholar 

  30. QGIS Development Team: QGIS Geographic Information System. Version 2.8.1-Wien. Open Source Geospatial Foundation (2015). http://qgis.osgeo.org

  31. R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2014). http://www.R-project.org/

  32. Stanley, C.R.: Effects of non-conserved denominators on Pearce element ratio diagrams. Math. Geol. 25(8), 1049–1070 (1993)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  37. Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S, 4th edn, 495 p. Springer, Berlin (2002)

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the organizers of the CoDaWork15 meeting for inviting this contribution. In particular the authors wish to thank Santi Thió-Henestrosa and Josep Antoni Martín Fernández of the University of Girona for their support and guidance. Also, discussions and valuable advice from Vera Pawlowsky-Glahn (University of Girona) and Juan Jose Egozcue (Polytechnical University of Catalonia) is gratefully acknowledged. We thank Vera Pawlowsky-Glahn and an anonymous reviewer for their helpful comments, which improved the manuscript.

Author information

Authors and Affiliations

  1. Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, N2L 3G1, Canada

    E. C. Grunsky

  2. Geological Survey of Canada, Ottawa, K1A 0E8, Canada

    B. A. Kjarsgaard

Authors
  1. E. C. Grunsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. A. Kjarsgaard
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. C. Grunsky .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Applied Mathematics, University of Girona, Girona, Spain

    Josep Antoni Martín-Fernández

  2. Department of Computer Science and Applied Mathematics, University of Girona, Girona, Spain

    Santiago Thió-Henestrosa

Appendix A: Summary of the First Seven Principal Components Derived from the Logcentered Lake Sediment Geochemistry

Appendix A: Summary of the First Seven Principal Components Derived from the Logcentered Lake Sediment Geochemistry

Eigenvalues

PC1

PC2

PC3

PC4

PC5

PC6

PC7

\(\uplambda \)

10.63

8.21

4.21

3.17

2.87

2.07

1.58

\(\uplambda \% \)

23.63

18.25

9.36

7.05

6.38

4.60

3.51

\(\Sigma \uplambda \%\)

23.63

41.89

51.25

58.29

64.67

69.28

72.79

figure b
figure c
figure d
figure e

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Crown Copyright

About this paper

Cite this paper

Grunsky, E.C., Kjarsgaard, B.A. (2016). Recognizing and Validating Structural Processes in Geochemical Data: Examples from a Diamondiferous Kimberlite and a Regional Lake Sediment Geochemical Survey. In: Martín-Fernández, J., Thió-Henestrosa, S. (eds) Compositional Data Analysis. CoDaWork 2015. Springer Proceedings in Mathematics & Statistics, vol 187. Springer, Cham. https://doi.org/10.1007/978-3-319-44811-4_7

Download citation

Publish with us

Policies and ethics

Search

Navigation