simpeg.meta.RepeatedSimulation

class simpeg.meta.RepeatedSimulation(simulation, mappings)

Bases: MetaSimulation

A MetaSimulation where a single simulation is used repeatedly.

This is most useful for linear simulations where a sensitivity matrix can be reused with different models. For non-linear simulations it will often be quicker to use the MetaSimulation class with multiple copies of the same simulation.

Parameters:

simulationsimpeg.simulation.BaseSimulation

The simulation to use repeatedly with different mappings.

mappings(n_sim) list of simpeg.maps.IdentityMap

The list of different mappings to use.

Attributes

clean_on_model_update	A list of solver objects to clean when the model is updated
counter	SimPEG Counter object to store iterations and run-times.
deleteTheseOnModelUpdate	HasModel.deleteTheseOnModelUpdate has been deprecated.
mappings	The mappings paired to each simulation.
model	The inversion model.
needs_model	True if a model is necessary
sensitivity_path	Path to directory where sensitivity file is stored.
simulation	The internal simulation.
simulations	The list of simulations.
survey	The survey for the simulation.
verbose	Verbose progress printout.

Methods

Jtvec(m, v[, f])	Compute the Jacobian transpose times a vector for the model provided.
Jtvec_approx(m, v[, f])	Approximation of the Jacobian transpose times a vector for the model provided.
Jvec(m, v[, f])	Compute the Jacobian times a vector for the model provided.
Jvec_approx(m, v[, f])	Approximation of the Jacobian times a vector for the model provided.
dpred([m, f])	Predicted data for the model provided.
fields(m)	Create fields for every simulation.
getJtJdiag(m[, W, f])	Return the squared sum of columns of the Jacobian.
make_synthetic_data(m[, relative_error, ...])	Make synthetic data for the model and Gaussian noise provided.
residual(m, dobs[, f])	The data residual.