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Localized kriging parameters optimization using local uncertainty

Localized kriging parameters optimization using local uncertainty Estimates of natural phenomena with spatial correlation, i.e. stationary domains, are more precise and accurate when performed using geostatistical techniques (e.g. kriging). The kriging estimates require the definition of the spatial continuity model and a search strategy. Many techniques, such as unfolding and dynamic anisotropy, try to give some improvement in the estimates, considering the variations of the distributions in the geological bodies, however, the definition of the search strategy in the other parameters is unique. This study presents an alternative to this, called Localized Kriging Parameters optimization (LKPO). LKPO considers the best local kriging parameters settings (block by block) through the local uncertainly (simulations). To illustrate this methodology, a synthetic dataset is presented, and the results are compared with the methodologies currently available in the geostatistical literature. Validation checks show a significant improvement in precision and accuracy on the estimates when using local kriging parameters. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Earth Science Taylor & Francis

Localized kriging parameters optimization using local uncertainty

12 pages

Localized kriging parameters optimization using local uncertainty

Abstract

Estimates of natural phenomena with spatial correlation, i.e. stationary domains, are more precise and accurate when performed using geostatistical techniques (e.g. kriging). The kriging estimates require the definition of the spatial continuity model and a search strategy. Many techniques, such as unfolding and dynamic anisotropy, try to give some improvement in the estimates, considering the variations of the distributions in the geological bodies, however, the definition of the search...
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Publisher
Taylor & Francis
Copyright
© 2023 Institute of Materials, Minerals and Mining and The AusIMM
ISSN
2572-6838
eISSN
2572-6846
DOI
10.1080/25726838.2023.2178803
Publisher site
See Article on Publisher Site

Abstract

Estimates of natural phenomena with spatial correlation, i.e. stationary domains, are more precise and accurate when performed using geostatistical techniques (e.g. kriging). The kriging estimates require the definition of the spatial continuity model and a search strategy. Many techniques, such as unfolding and dynamic anisotropy, try to give some improvement in the estimates, considering the variations of the distributions in the geological bodies, however, the definition of the search strategy in the other parameters is unique. This study presents an alternative to this, called Localized Kriging Parameters optimization (LKPO). LKPO considers the best local kriging parameters settings (block by block) through the local uncertainly (simulations). To illustrate this methodology, a synthetic dataset is presented, and the results are compared with the methodologies currently available in the geostatistical literature. Validation checks show a significant improvement in precision and accuracy on the estimates when using local kriging parameters.

Journal

Applied Earth ScienceTaylor & Francis

Published: Feb 23, 2023

Keywords: Search neighbourhood; Kriging parameters; local uncertainty; simulations; optimization; search parameters; search strategy; local optimisation; neighbourhood parameters

References