SimPEG.regularization.BaseSparse

class SimPEG.regularization.BaseSparse(mesh, norm=2.0, irls_scaled=True, irls_threshold=1e-08, **kwargs)

Bases: BaseRegularization

Base class for sparse-norm regularization.

The BaseSparse class defines properties and methods inherited by sparse-norm regularization classes. Sparse-norm regularization in SimPEG is implemented using an iteratively re-weighted least squares (IRLS) approach. The BaseSparse class however, is not directly used to define the regularization for the inverse problem.

Parameters:

meshSimPEG.regularization.RegularizationMesh, discretize.base.BaseMesh

Mesh on which the regularization is discretized. This is not necessarily the same as the mesh on which the simulation is defined.

active_cellsNone, (n_cells, ) numpy.ndarray of bool

Boolean array defining the set of RegularizationMesh cells that are active in the inversion. If None, all cells are active.

mappingNone, SimPEG.maps.BaseMap

The mapping from the model parameters to the active cells in the inversion. If None, the mapping is the identity map.

reference_modelNone, (n_param, ) numpy.ndarray

Reference model values used to constrain the inversion. If None, the starting model is set as the reference model.

unitsNone, str

Units for the model parameters. Some regularization classes behave differently depending on the units; e.g. ‘radian’.

weightsNone, dict

Weight multipliers to customize the least-squares function. Each key points to a (n_cells, ) numpy.ndarray that is defined on the RegularizationMesh.

normfloat

The norm used in the regularization function. Must be between within the interval [0, 2].

irls_scaledbool

If True, scale the IRLS weights to preserve magnitude of the regularization function. If False, do not scale.

irls_thresholdfloat

Constant added to IRLS weights to ensures stability in the algorithm.

Attributes

irls_scaled	Scale IRLS weights.
irls_threshold	Stability constant for computing IRLS weights.
norm	Norm for the sparse regularization.

Methods

get_lp_weights(f_m)

Compute and return iteratively re-weighted least-squares (IRLS) weights.