Environmental Earth Sciences

, Volume 72, Issue 12, pp 5159–5169 | Cite as

Identification of REE mineralization-related geochemical anomalies using fractal/multifractal methods in the Nanling belt, South China

  • Y. Liu
  • Q. Cheng
  • Q. Xia
  • X. Wang
Original Article


Singularity analysis in the multifractal context adds new tools to conventional approaches in dealing with geochemical data, which can be applied to characterize how the statistical behavior varies as measuring scale changes. Hybrid frequency distribution patterns can be detected by singularity analysis because of the regularity of enrichment and dispersion of geochemical elements in the Earth’s crust. In the present study, a case study of anomaly identification of REE mineralization-related La and Y concentration values from 1,617 stream sediment samples in the Nanling belt, South China, has been used to demonstrate the application of two-fractal/multifractal methods, singularity analysis and concentration–area (C-A) fractal method. First, singularity analysis was used to identify weak anomalies hidden within geochemical background for the prediction of the present of REE mineralization. And then, the C-A fractal method was applied to determine threshold values of singularity indices for separating anomalies from background. The results indicate that nonlinear models and methods related to fractal/multifractal (singularity analysis and C-A method) can provide powerful tools for the quantification of geochemical anomaly characteristics, and hybrid frequency distribution patterns can be identified by combining singularity analysis and C-A method due to different distribution patterns of background and anomaly of geochemical data.


Geochemical anomaly Singularity analysis C-A fractal method Ion-adsorbed REE mineralization Nanling belt 



The authors thank Prof. Pablo Gumiel (University of Alcalá, Spain) and two anonymous reviewers for reviewing and improving the manuscript. This research has been supported by a Basic Research and Public Service Special Fund project from the Institute of Geophysical and Geochemical Exploration CAGS (WHS201208), a Program of Integrated Prediction of Mineral Resources in Covered Areas (No. 1212011085468), and a research project on “Quantitative models for prediction of strategic mineral resources in China” (201211022) by China Geological Survey.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.State Key Laboratory of Geological Processes and Mineral ResourcesChina University of GeosciencesWuhanChina
  2. 2.Faculty of Earth ResourcesChina University of GeosciencesWuhanChina
  3. 3.Department of Earth and Space Science and EngineeringYork UniversityTorontoCanada

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