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Mapping hidden risks in mine tailings: A multi-variable geostatistical framework for environmental management.

Collins G Adoko1, Nasser Madani2, Mohammad Maleki3

  • 1School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan; WH Bryan Mining Geology Research Centre, Sustainable Minerals Institute, University of Queensland, Brisbane, Australia; ARC Centre in Critical Resources for the Future, Perth, Australia.

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Summary

This study introduces multi-Gaussian cokriging (MGCOK) for mine tailings, improving spatial estimation of environmental risks and resource potential. MGCOK enhances accuracy in assessing element concentrations and exceeding thresholds, crucial for environmental monitoring and remediation.

Keywords:
Environmental risk mappingHeterotopic samplingLocal uncertaintyMine tailingsMulti-Gaussian cokrigingMultivariate geostatisticsResource recovery

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Area of Science:

  • Geosciences
  • Environmental Science
  • Geostatistics

Background:

  • Mine tailings pose environmental risks and offer resource recovery opportunities.
  • Accurate spatial estimation is vital for assessing liabilities and guiding remediation.
  • Univariate geostatistics struggle with complex datasets and inter-variable correlations.

Purpose of the Study:

  • Introduce a novel multivariate geostatistical method, multi-Gaussian cokriging (MGCOK).
  • Enable joint estimation of recoverable functions, including threshold exceedance probabilities.
  • Apply MGCOK to the Haveri tailings deposit for environmental risk assessment.

Main Methods:

  • Developed MGCOK as an extension of multi-Gaussian kriging (MGK).
  • Applied the method to sulfur, iron, and cobalt in the Haveri tailings deposit.
  • Leveraged cross-variable correlations and a linear model of coregionalization.

Main Results:

  • MGCOK improved predictions of local recoverable functions compared to standard MGK.
  • Achieved reduced estimation variance and better delineation of elevated concentration zones.
  • Cross-validation confirmed superior performance, especially for under-sampled elements.

Conclusions:

  • Multivariate non-linear geostatistical modeling is valuable for mine tailings environmental risk assessment.
  • MGCOK offers enhanced spatial estimation for complex, variable datasets.
  • The method has potential for both environmental evaluation and resource assessment.