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hybrid_update⚓︎

ES, and Iterative ES updates with hybrid update matrix calculated from multi-fidelity runs.

hybrid_update ⚓︎

Class for hybrid update schemes as described in: Fossum, K., Mannseth, T., & Stordal, A. S. (2020). Assessment of multilevel ensemble-based data assimilation for reservoir history matching. Computational Geosciences, 24(1), 217–239. https://doi.org/10.1007/s10596-019-09911-x

Note that the scheme is slightly modified to be inline with the standard (I)ES approximate update scheme. This enables the scheme to efficiently be coupled with multiple updating strategies via class MixIn

scale(data, scaling) ⚓︎

Scale the data perturbations by the data error standard deviation.

Args: data (np.ndarray): data perturbations scaling (np.ndarray): data error standard deviation

Returns: np.ndarray: scaled data perturbations

update(enX, enY, enE, **kwargs) ⚓︎

Perform the hybrid update.

Parameters: 
enX : list of np.ndarray 
    List of state ensemble matrices for each level (nx, ne)

enY : list of np.ndarray
    List of predicted data ensemble matrices for each level (nd, ne)

enE : list of np.ndarray
    List of ensemble of perturbed observations for each level (nd, ne)