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Kriging parameter optimisation: global versus local search strategies

Kriging parameter optimisation: global versus local search strategies Kriging methods require parameters to define search strategy (kriging neighbourhood). These parameters affect the precision and accuracy of its estimates. Frequently, the choice of these parameters is merely subjective. Some practitioners prioritise estimates that lead to models with a reduced smoothing effect or a regression slope as close as possible to one. However, it is prevalent to use the same kriging neighbourhood or search strategy for all blocks estimated within a stationary domain. This study presents a contribution that challenges this concept by using a block-by-block optimisation approach focused on the localised kriging parameter optimisation (LKPO) methodology. A comparative study is carried out, and some of the metrics analysed include the kriging efficiency and the slope of regression (typical in optimising methodologies in the mining industry). The results indicate that the LKPO methodology provides more accurate and precise estimates than those based on a global kriging neighbourhood. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Earth Science Taylor & Francis

Kriging parameter optimisation: global versus local search strategies

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Kriging parameter optimisation: global versus local search strategies

Abstract

Kriging methods require parameters to define search strategy (kriging neighbourhood). These parameters affect the precision and accuracy of its estimates. Frequently, the choice of these parameters is merely subjective. Some practitioners prioritise estimates that lead to models with a reduced smoothing effect or a regression slope as close as possible to one. However, it is prevalent to use the same kriging neighbourhood or search strategy for all blocks estimated within a stationary...
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Publisher
Taylor & Francis
Copyright
© 2021 Institute of Materials, Minerals and Mining and The AusIMM
ISSN
2572-6838
eISSN
2572-6846
DOI
10.1080/25726838.2021.1930964
Publisher site
See Article on Publisher Site

Abstract

Kriging methods require parameters to define search strategy (kriging neighbourhood). These parameters affect the precision and accuracy of its estimates. Frequently, the choice of these parameters is merely subjective. Some practitioners prioritise estimates that lead to models with a reduced smoothing effect or a regression slope as close as possible to one. However, it is prevalent to use the same kriging neighbourhood or search strategy for all blocks estimated within a stationary domain. This study presents a contribution that challenges this concept by using a block-by-block optimisation approach focused on the localised kriging parameter optimisation (LKPO) methodology. A comparative study is carried out, and some of the metrics analysed include the kriging efficiency and the slope of regression (typical in optimising methodologies in the mining industry). The results indicate that the LKPO methodology provides more accurate and precise estimates than those based on a global kriging neighbourhood.

Journal

Applied Earth Science – Taylor & Francis

Published: Jul 3, 2021

Keywords: Kriging parameters; kriging efficiency; slope of regression; search strategy; kriging neighbourhood

References

  • Going local: innovating resource estimates to improve investment decisions
  • Checks and measures of performance for kriging estimates
  • Localised kriging parameter optimization based on absolute error minimization
  • A practical analysis of the effects of spatial structure and of data available and accessed, on conditional biases in ordinary kriging
  • Principles of geostatistics
  • Two key parameters when choosing the kriging neighbourhood
  • Quantitative kriging neighbourhood analysis for the mining geologist: a description of the method with worked case examples