A non-homogeneous model for kriging dosimetric data
| Autor: | Yvan Caffari, Didier Renard, Alexis Quentin, Vincent Le Guen, Christian Lajaunie |
|---|---|
| Přispěvatelé: | 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), EDF (EDF), Performance, Risque Industriel, Surveillance pour la Maintenance et l’Exploitation (EDF R&D PRISME), EDF R&D (EDF R&D), EDF (EDF)-EDF (EDF), CEDRIC. Données complexes, apprentissage et représentations (CEDRIC - VERTIGO), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Hydrogeology
0208 environmental biotechnology Random function 02 engineering and technology Geostatistics 010502 geochemistry & geophysics 01 natural sciences 020801 environmental engineering Term (time) Mathematics (miscellaneous) Kriging [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Non homogeneous General Earth and Planetary Sciences Applied mathematics Dose rate ComputingMilieux_MISCELLANEOUS 0105 earth and related environmental sciences Interpolation Mathematics |
| Zdroj: | Mathematical Geosciences Mathematical Geosciences, Springer Verlag, 2019, 52 (7), pp.847-863. ⟨10.1007/s11004-019-09823-7⟩ |
| ISSN: | 1874-8961 1874-8953 |
| Popis: | This paper deals with kriging-based interpolation of dosimetric data. Such data typically show some inhomogeneities that are difficult to take into account by means of the usual non-stationary models available in geostatistics. By including the knowledge of suspected potential sources (such as pipes or reservoirs) better estimates can be obtained, even when no hard data are available on these sources. The proposed method decomposes the measured dose rates into a diffuse homogeneous term and the contribution from the sources. Accordingly, two random function models are introduced, the first associated with the diffuse term, and the second with the unknown and unmeasured source contribution. Estimation of the model parameters is based on cross-validation quadratic error. As a bonus, the resulting model makes it possible to estimate the source activity. The efficiency of this approach is demonstrated on data simulated according to the physical equations of the system. The method is available to researchers through an R-package provided by the authors upon request. |
| Databáze: | OpenAIRE |
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