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A fuzzy logic based explicit declustering technique.

Khan Muhammad1, Hylke J Glass2

  • 1Intelligent Information Processing Lab, National Centre of Artificial Intelligence and Department of Mining Engineering, University of Engineering and Technology Peshawar 25000, Pakistan.

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|July 24, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel declustering technique for geoscience resource estimation. It improves accuracy by accounting for both spatial and attribute similarity in clustered samples, leading to unbiased statistical distributions.

Keywords:
DeclusteringFuzzy logicFuzzy-c-meansGlobal estimation

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Area of Science:

  • Geosciences
  • Geostatistics
  • Data Science

Background:

  • Spatial estimation of geoscience resources relies on accurate statistical distributions.
  • Preferential sampling leads to biased parameters in resource estimation.
  • Traditional declustering methods overlook attribute similarity within spatial clusters.

Purpose of the Study:

  • To develop a declustering technique that accounts for spatial and attribute similarity.
  • To improve the accuracy of statistical distributions in resource estimation.
  • To provide an unbiased approach for conditional simulations and uncertainty modeling.

Main Methods:

  • Fuzzy c-means algorithm for classifying samples into spatial and geochemical clusters.
  • Mamdani-based Fuzzy Inference System for deriving declustering weights.
  • Application and validation on GSLib and Walker Lake datasets.

Main Results:

  • The proposed declustering technique explicitly considers spatial and attribute clustering.
  • Fuzzy clustering and fuzzy inference system effectively derived declustering weights.
  • The novel method demonstrated superior accuracy compared to traditional cell declustering.

Conclusions:

  • The developed declustering method enhances the accuracy of resource estimation by addressing sample attribute similarity.
  • This approach offers a more robust way to model uncertainty in spatially distributed geoscience variables.
  • The technique provides a significant improvement over conventional declustering methods.