es

ES type schemes

ES

Bases: EnKF

Ensemble Smoother (ES).

Assimilates all observations simultaneously in a single update, rather than sequentially in time as the filter does. It is :class:EnKF specialised to one data group, and shares its analysis step; only the iteration budget and the misfit bookkeeping differ.

A single conditioning step is cheap but can over-correct when the model is strongly non-linear. :class:ESMDA addresses this by spreading the same update over several inflated steps.

Parameters:

Name	Type	Description	Default
keys_da	dict	Parsed dataassim configuration. Besides the keys every scheme reads -- data, datavar, obsname, truedataindex -- the ones this scheme acts on are listed under Notes.	required
keys_en	dict	Parsed ensemble configuration: ensemble size ne, the state variable names, and the prior_<name> blocks describing each.	required
sim	object	Forward simulator instance, e.g. simulator.opm.flow.	required
analysis	(approx, full, subspace)	Analysis flavour, i.e. how the ensemble-approximated sensitivity is inverted. Defaults to the analysis key in keys_da, falling back to 'approx'. The flavours differ in cost and in how they handle a rank-deficient ensemble; they solve the same update equation.	'approx'

Attributes:

Name	Type	Description
ensemble	AssimilationEnsemble	Collaborator holding the state realisations, observed data and simulator. Its state is exposed as properties on the scheme, so scheme.enX and scheme.keys_da read straight through.
analysis	AnalysisBase	The bound analysis object. Note the constructor takes analysis as a name and this attribute holds the resulting object, the way Model(optimizer="adam").optimizer is an optimizer instance.
analysis_name	str	The flavour name that was resolved, e.g. 'approx'.
iteration	int	Accepted iterations completed so far.
data_misfit, prior_data_misfit	float	Current and initial mean data misfit.

Notes

assimindex is flattened to a single group at construction, so the ordering that matters for :class:EnKF has no effect here.

Because there is only one step, the full flavour coincides with approx -- the prior-increment term they differ over is only reached when iterating -- so :attr:EnKF.COMPATIBLE_ANALYSES, inherited unchanged here, points "full" at the cheaper approx analysis.

Examples:

>>> result = ES.assimilate(keys_da, keys_en, flow(keys_sim))
>>> result.nit
1

References

Evensen, Data Assimilation: The Ensemble Kalman Filter evensen2009a.

See Also

EnKF : Sequential form of the same update. ESMDA : Spreads the conditioning over several inflated steps.

__init__(keys_da, keys_en, sim, analysis=None, ensemble=None)

Build the ensemble from the config (or take the one given) and bind the analysis.

See the class docstring for the parameters.

check_convergence()

ES takes a single all-data-at-once step; nothing stops early.

score_and_commit()

Calculate the "convergence" of the method. Important to