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Abstract

Summary

The ensemble Kalman filter (EnKF) introduced in has been used successfully in many applications to do data assimilation in state-space models. EnKF represents the knowledge about the latent process in the state-space model through a set of realizations. The filter alternates between a prediction step and an update step. The prediction step can be performed analytically, while the update step must be approximated.

In a Bayesian model is used to perform the update step. Specifically, the model parameters are assigned priors and simulated. Moreover, a set of valid update procedures are derived, and the ensemble is updated according to an update procedure that satisfies a specified criterion. However, the procedure introduced in is computationally demanding.

In our work we introduce a computationally efficient alternative to the approach in . We formulate a prior for the model parameters which enables efficient sampling, and we partition the update of each ensemble realization into smaller blocks. Simulation studies suggest that the reduction in computational demands is considerable and that the results provided by our approach are essentially similar to the results obtained with the approach from .

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202335024
2023-11-27
2026-01-17

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References

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/content/papers/10.3997/2214-4609.202335024
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A Sparse Matrix Formulation of Model-Based Ensemble Kalman Filter
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202335024
/content/papers/10.3997/2214-4609.202335024
/content/papers/10.3997/2214-4609.202335024
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