Data Assimilation in Spatio-Temporal Models with Non-Gaussian Initial States-The Selection Ensemble Kalman Model

被引:4
作者
Conjard, Maxime [1 ]
Omre, Henning [1 ]
机构
[1] Norwegian Univ Sci & Technol, Dept Math Sci, N-7491 Trondheim, Norway
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 17期
关键词
data assimilation; EnKF; multimodality; SEQUENTIAL DATA ASSIMILATION; DISTRIBUTIONS; FILTER;
D O I
10.3390/app10175742
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Featured Application Porosity/permeability inversion in petroleum engineering; Log-conductivity inversion with heads and tracer data in hydrogeology; Source contribution identification in air pollution monitoring. Assimilation of spatio-temporal data poses a challenge when allowing non-Gaussian features in the prior distribution. It becomes even more complex with nonlinear forward and likelihood models. The ensemble Kalman model and its many variants have proven resilient when handling nonlinearity. However, owing to the linearized updates, conserving the non-Gaussian features in the posterior distribution remains an issue. When the prior model is chosen in the class of selection-Gaussian distributions, the selection Ensemble Kalman model provides an approach that conserves non-Gaussianity in the posterior distribution. The synthetic case study features the prediction of a parameter field and the inversion of an initial state for the diffusion equation. By using the selection Kalman model, it is possible to represent multimodality in the posterior model while offering a 20 to 30% reduction in root mean square error relative to the traditional ensemble Kalman model.
引用
收藏
页数:24
相关论文
共 30 条
[1]  
[Anonymous], 2005, SPR S STAT
[2]   A unified view on skewed distributions arising from selections [J].
Arellano-Valle, Reinaldo B. ;
Branco, Marcia D. ;
Genton, Marc G. .
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2006, 34 (04) :581-601
[3]   Sequential data assimilation techniques in oceanography [J].
Bertino, L ;
Evensen, G ;
Wackernagel, H .
INTERNATIONAL STATISTICAL REVIEW, 2003, 71 (02) :223-241
[4]  
BESAG J, 1974, J ROY STAT SOC B MET, V36, P192
[5]  
Burgers G, 1998, MON WEATHER REV, V126, P1719, DOI 10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO
[6]  
2
[7]  
Conjard M., 2020, ARXIVSTATME200614343
[8]   Multimodal ensemble Kalman filtering using Gaussian mixture models [J].
Dovera, Laura ;
Della Rossa, Ernesto .
COMPUTATIONAL GEOSCIENCES, 2011, 15 (02) :307-323
[10]  
Evensen G., 2006, Data Assimilation. The Ensemble Kalman Filter, DOI [10.1007/978-3-540-38301-7, DOI 10.1007/978-3-540-38301-7]