simpeg.simulation.LinearSimulation#

class simpeg.simulation.LinearSimulation(mesh=None, linear_model=None, model_map=None, G=None, **kwargs)[source]#

Bases: BaseSimulation

Linear forward simulation class.

The LinearSimulation class is used to define forward simulations of the form:

\[\mathbf{d} = \mathbf{G \, f}(\mathbf{m})\]

where \(\mathbf{m}\) are the model parameters, \(\mathbf{f}\) is a mapping operator (optional) from the model space to a user-defined parameter space, \(\mathbf{d}\) is the predicted data vector, and \(\mathbf{G}\) is an (n_data, n_param) linear operator.

The LinearSimulation class is generally used as a base class that is inherited by other simulation classes within SimPEG. However, it can be used directly as a simulation class if the G property is used to set the linear forward operator directly.

By default, we assume the mapping operator \(\mathbf{f}\) is the identity map, and that the forward simulation reduces to:

\[\mathbf{d} = \mathbf{G \, m}\]

Parameters:

meshdiscretize.BaseMesh, optional: Mesh on which the forward problem is discretized. This is not necessarily the same as the mesh on which the simulation is defined.
model_mapsimpeg.maps.BaseMap: Mapping from the model parameters to vector that the linear operator acts on.
G(n_data, n_param) numpy.ndarray or scipy.sparse.csr_matrx: The linear operator. For a model_map that maps within the same vector space (e.g. the identity map), the dimension n_param equals the number of model parameters. If not, the dimension n_param of the linear operator will depend on the mapping.

Attributes

`G`	The linear operator.
`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
`linear_model`	The model for a linear problem physical property model.
`mesh`	Mesh for the simulation.
`model`	The inversion model.
`model_deriv`	Derivative of The model for a linear problem wrt the model.
`model_map`	Mapping of the inversion model to The model for a linear problem.
`needs_model`	True if a model is necessary
`sensitivity_path`	Path to directory where sensitivity file is stored.
`solver_opts`	Solver-specific parameters.
`survey`	The survey for the simulation.
`verbose`	Verbose progress printout.

solver

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)	Return the computed geophysical fields for the model provided.
`getJ`(m[, f])	Returns the full Jacobian.
`make_synthetic_data`(m[, relative_error, ...])	Make synthetic data for the model and Gaussian noise provided.
`residual`(m, dobs[, f])	The data residual.