Predictive Geological Mapping Using Closed-Form Non-stationary Covariance Functions with Locally Varying Anisotropy: Case Study at El Teniente Mine (Chile)
| Autor: | Francky Fouedjio, Serge Antoine Séguret |
|---|---|
| Přispěvatelé: | Équipe Géostatistique, Centre de Géosciences (GEOSCIENCES), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL) |
| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
Covariance function Geostatistics 010502 geochemistry & geophysics 01 natural sciences Mining 010104 statistics & probability Kriging Statistics::Methodology Applied mathematics Limit (mathematics) [MATH]Mathematics [math] 0101 mathematics Variogram 0105 earth and related environmental sciences General Environmental Science Non-stationarity Covariance [STAT]Statistics [stat] [SDU]Sciences of the Universe [physics] Kernel (statistics) Kernel smoother Locally varying anisotropy Simulation Geology |
| Zdroj: | Natural Resources Research Natural Resources Research, Springer Verlag, 2016, ⟨10.1007/s11053-016-9293-4⟩ |
| ISSN: | 1573-8981 1520-7439 |
| Popis: | International audience; This paper is concerned with the problem of predicting the surface elevation of the Bradenbreccia pipe at the El Teniente mine in Chile. This mine is one of the worlds largest andmost complex porphyry-copper ore systems. As the pipe surface constitutes the limit of thedeposit and the mining operation, predicting it accurately is important. The problem istackled by applying a geostatistical approach based on closed-form non-stationary covariancefunctions with locally varying anisotropy. This approach relies on the mild assumptionof local stationarity and involves a kernel-based experimental local variogram a weightedlocal least-squares method for the inference of local covariance parameters and a kernelsmoothing technique for knitting the local covariance parameters together for kriging purpose.According to the results, this non-stationary geostatistical method outperforms thetraditional stationary geostatistical method in terms of prediction and prediction uncertaintyaccuracies. |
| Databáze: | OpenAIRE |
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