Weighted RML using ensemble-methods for data assimilation

Yuming Ba; Dean S. Oliver

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arXiv · 2301.05448

Weighted RML using ensemble-methods for data assimilation

Yuming Ba·Dean S. Oliver

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Abstract

The weighting of critical-point samples in the weighted randomized maximum likelihood method depend on the magnitude of the data mismatch at the critical points and on the Jacobian of the transformation from the prior density to the proposal density. When standard iterative ensemble smoothers are applied for data assimilation, the Jacobian is identical for all samples. If a hybrid data assimilation method is applied, however, there is the possibility that each ensemble member can have a distinct Jacobian and that the posterior density can be multimodal. In order to apply a hybrid method iterative ensemble smoother, it is necessary that a part of the transformation from the prior Gaussian random variable to the data be analytic. Examples might include analytic transformation from a latent Gaussian random variable to permeability followed by a black-box transformation from permeability to state variables in porous media flow, or a Gaussian hierarchical model for variables followed by a similar black-box transformation from permeability to state variables. In this paper, we investigate the application of weighting to both types of examples.

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Yuming Ba, Dean S. Oliver. 2023-01-13. Weighted RML using ensemble-methods for data assimilation. https://arxiv.org/abs/2301.05448

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