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	A list of properties stored on this object to delete when the model is updated
mappings	The mappings paired to each simulation.
mesh	Mesh for the 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.
solver	Numerical solver used in the forward simulation.
solver_opts	Solver-specific parameters.
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.