Marginalized iterative ensemble smoothers for data assimilation

被引：0
作者：
Andreas S. Stordal
Rolf J. Lorentzen
Kristian Fossum
机构：
[1] NORCE,Department of Mathematics
[2] Norwegian Research Center,undefined
[3] University of Bergen,undefined
来源：
Computational Geosciences | 2023年 / 27卷
关键词：
Measurement uncertainty; Ensemble methods; Bayesian inversion; Data assimilation; History matching; Hierarchical models;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Data assimilation is an important tool in many geophysical applications. One of many key elements of data assimilation algorithms is the measurement error that determines the weighting of the data in the cost function to be minimized. Although the algorithms used for data assimilation treat the measurement uncertainty as known, it is in many cases estimated or set based on some expert opinion. Here we treat the measurement uncertainty as a hyperparameter in a fully Bayesian hierarchical model and derive a new class of iterative ensemble methods for data assimilation where the measurement uncertainty is integrated out. The proposed algorithms are compared with the standard iterative ensemble smoother on a 2D synthetic reservoir model.
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页码：975 / 986
页数：11
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