simpeg.meta.SumMetaSimulation#

class simpeg.meta.SumMetaSimulation(simulations, mappings)[source]#

Bases: MetaSimulation

An extension of the MetaSimulation that sums the data outputs.

This class requires the mappings have the same input length and each simulation to have the same number of data.

This could be useful for a linear problem where each simulation tackles a different subset of the model.

Parameters:
simulations(n_sim) list of simpeg.simulation.BaseSimulation
mappings(n_sim) list of simpeg.maps.IdentityMap

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.

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.