Geochemistry International

, Volume 56, Issue 12, pp 1233–1244 | Cite as

The Study of Stream Sediment Geochemical Data Processing by Using k-Means Algorithm and Centered Logratio Transformation—an Example of a District in Hunan, China

  • Mi TianEmail author
  • Libo Hao
  • Xinyun Zhao
  • Jilong Lu
  • Yuyan Zhao


The backgrounds of stream sediment geochemical samples are associated with the underlying geological bodies. Moreover, a stream sediment geochemical data set is a closed number system because it contains compositional variables that are parts of a whole. Consequently, the empirical frequency distributions of stream sediment geochemical data are often skewed or with multiple peaks. While it is clear that data should approach a symmetric distribution before any threshold estimation methods are applied, so the corresponding method for transforming data is required. In this study, a new method for transformation of stream sediment geochemical data is provided. Firstly, the samples are classified by k-means method into different clusters, samples in each of which are thought to be of the same background. Then samples in each cluster are centered logratio transformed. Finally, the data after processed are tested and they all satisfy normal distributions. Furthermore, a stream sediment geochemical data set of a district in Hunan, China is taken as an example. Maps of anomalies of raw and transformed metallogenic Pb, Zn, Cu and W are portrayed respectively for comparison. The results show that anomalies of raw data correspond worse with the known deposits. By contrast, the method of mapping anomalies with transformed data performs better.


stream sediment geochemical anomalies k-means compositional data 


  1. 1.
    J. Aitchison, “The statistical analysis of geochemical compositions,” Math. Geol. 16, 531-564 (1984).CrossRefGoogle Scholar
  2. 2.
    J. Aitchison, J. J. Egozcue, “Compositional data analysis: Where are we and where should we be heading?” Math. Geol. 37, 829–850 (2005).CrossRefGoogle Scholar
  3. 3.
    E. J. M. Carranza, “Usefulness of stream order to detect stream sediment geochemical anomalies,” Geochem. Explor. Environ. Anal. 4, 341–352 (2004).CrossRefGoogle Scholar
  4. 4.
    E. J. M. Carranza, “Mapping of anomalies in continuous and discrete fields of stream sediment geochemical landscapes,” Geochem. Explor. Environ. Anal. 10 (2), 171–187 (2010a).CrossRefGoogle Scholar
  5. 5.
    E. J. M. Carranza, “Catchment basin modeling of stream sediment anomalies revisited: incorporation of EDA and fractal analysis,” Geochem. Explor. Environ. Anal. 10(4), 365–381 (2010b).CrossRefGoogle Scholar
  6. 6.
    E. J. M. Carranza, “Analysis and mapping of geochemical anomalies using logratio transformed stream sediment data with censored values,” J. Geochem. Explor. 110, 167–185 (2011).CrossRefGoogle Scholar
  7. 7.
    J. Chen, Z. D. Li, and H. Zhong, “Comparison of multiple methods to determine the geochemical anomaly threshold,” Geochem. Sur Res. 37 (3), 187–192 (in Chinese with English abstract) (2014).Google Scholar
  8. 8.
    Y. Dong, “Discussion of applying factor analysis to the geochemical subareas measurement in stream sediment-A case study of Dulan area in Qinghai Province,” Mine. Res. Geo. 22(1), 78–82 (in Chinese with English abstract) (2008).Google Scholar
  9. 9.
    J. J. Egozcue, V. Pawlowsky-Glahn, G. Mateu-Figueras, and C. Barceló-Vidal, “Isometric logratio transformations for compositional data analysis,” Math. Geol. 35, 279–300 (2003).CrossRefGoogle Scholar
  10. 10.
    P. Filzmoser, K. Hron, and C. Reimann, “Principal components analysis for compositional data with outliers,” Environmetrics 20, 621–632 (2009a).CrossRefGoogle Scholar
  11. 11.
    P. Filzmoser, K. Hron, and C. Reimann, “Univariate statistical analysis of environmental (compositional) data: problems and possibilities,” Sci. Total. Environ. 407, 6100–6108 (2009b).CrossRefGoogle Scholar
  12. 12.
    L. B. Hao, W. Li, and J. L. Lu, “Method for determining the geochemical background and anomalies in areas with complex lithology,” Geol. Bull. China. 26 (12), 1531–1535 (in Chinese with English abstract) (2007).Google Scholar
  13. 13.
    L. B. Hao, X. Y. Zhao, Y. Y. Zhao, J. L. Lu, and L. J. Sun, “Determination of the geochemical background and anomalies in areas with variable lithologies,” J. Geochem. Explor. 139, 177–182 (2014).CrossRefGoogle Scholar
  14. 14.
    H. E. Hawkes, J. S. Webb, Geochemistry in Mineral Exploration (Harper, New York, 1962).Google Scholar
  15. 15.
    B. Q. Jiao, “The calculation of gravity anomaly of the three dimensional sphere-coronal model based on the triangle element method,” Geophy. Geochem. Explor. 33 (2), 165–169 (in Chinese with English abstract) (2009).Google Scholar
  16. 16.
    I. Joseph, B. K. Bhaumik, “Improved estimation of the Box-Cox transform parameter and its application to hydro geochemical data,” Math. Geol. 29, 963–976 (1997).CrossRefGoogle Scholar
  17. 17.
    L. Kaufman, P. Rousseeuw, Finding Groups in Data: an Introduction to Cluster Analysis (John Wiley and Sons, London, 1990).CrossRefGoogle Scholar
  18. 18.
    W. C. Lv, Y. Z. Zhou, and Y. Zhang, “Geochemical anomaly identification of stream sediments of Wendi sheet in the south segment of the Qinzhou-Hangzhou metallogenic belt,” Act. Scien. Natur. Univer. Sunya. 51 (5), 107–112 (in Chinese with English abstract) (2012).Google Scholar
  19. 19.
    J. B. MacQueen, “Some methods of classification and analysis of multivariate observations,” Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, 281–297 (1967).Google Scholar
  20. 20.
    S. P. McGrath, P. J. Loveland, The Soil Geochemical Atlas of England and Wales (Blackie Academic, London, 1992).Google Scholar
  21. 21.
    A. T. Miesch, “Log-transformation in geochemistry,” Math. Geol. 9, 191–194 (1977).CrossRefGoogle Scholar
  22. 22.
    B. Raskutti and C. Leckie, “An evaluation of criteria for measuring the quality of clusters,” Proceedings of the 16th International Joint Conference on Artificial Intelligence, 905–910 (1999).Google Scholar
  23. 23.
    C. Reimann and R. G. Garrett, “Geochemical background-concept and reality,” Sci. Total. Environ. 350 (1/3), 12–27 (2005).CrossRefGoogle Scholar
  24. 24.
    C. Reimann and P. Filzmoser, “Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data,” Environ. Geol. 39, 1001–1014 (2000).CrossRefGoogle Scholar
  25. 25.
    C. Reimann, P. Filzmoser, R. G. Garrett, and R. Dutter, Statistical Data Analysis Explained: Applied Environmental Statistics with R (John Wiley & Sons, Chichester, 2008).CrossRefGoogle Scholar
  26. 26.
    J. B. dos Santos, C. A. Heuser, V. P. Moreira, and L. K. Wives, “Automatic threshold estimation for data matching applications,” Inf. Sci. 181, 2685–2699 (2011).CrossRefGoogle Scholar
  27. 27.
    Y. X. Shi, J. H. Jin, and J. L. Lu, “Factor analysis method and application of stream sediment geochemical partition,” Geol. Prosp. 40 (5), 73–76 (in Chinese with English abstract) (2004).Google Scholar
  28. 28.
    Y. X. Shi, L. B. Hao, J. L. Lu, and H.J. Ji, “Application of factor classification in geological mapping in Tahe, Heilongjiang Province,” Journal of Jilin University (Earth Science Edition). 38 (5), 899–903 (in Chinese with English abstract) (2008).Google Scholar
  29. 29.
    P. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining (Pearson Education, New Jersey, 2006).Google Scholar
  30. 30.
    M. Templ, P. Filzmoser, and C. Reimann, “Cluster analysis applied to regional geochemical data: Problems and possibilities,” Appl. Geochem. 23, 2198–2213 (2008).CrossRefGoogle Scholar
  31. 31.
    S. K. Tripathy, D. S. Jeere, and B. K. Bandyopadhyay, “Geochemistry of stream sediments and its relation with bedrock geology in parts of Sindhudurg District, Maharashtra,” Jour. Geol. Soci. India. 71 (3), 397 (2008).Google Scholar
  32. 32.
    A. B. Vistelius, “The skew frequency distributions and the fundamental law of the geochemical processes,” J. Geol. 68, 1–22 (1960).Google Scholar
  33. 95.
    DZ/T0167. 1 : 200 000 regional geochemical exploration standard of the People’s Republic of China. DissGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • Mi Tian
    • 1
    • 2
    Email author
  • Libo Hao
    • 3
  • Xinyun Zhao
    • 3
  • Jilong Lu
    • 3
  • Yuyan Zhao
    • 3
  1. 1.Institute of Geophysical and Geochemical Exploration (IGGE), Chinese Academy of Geological SciencesLangfangChina
  2. 2.International Centre on Global-scale Geochemistry (ICGG), LangfangChina
  3. 3.Department of Geochemistry, Jilin UniversityChangchunChina

Personalised recommendations