class SimPEG.regularization.SmoothnessSecondOrder(mesh, orientation='x', reference_model_in_smooth=False, **kwargs)[source]#

Bases: SmoothnessFirstOrder

Second-order smoothness (flatness) least-squares regularization.

SmoothnessSecondOrder regularization is used to ensure that values in the recovered model have small second-order spatial derivatives. When a reference model is included, the regularization preserves second-order smoothness within the reference model along the direction specified (x, y or z). Optionally, custom cell weights can be used to control the degree of smoothness being enforced throughout different regions the model.

See the Notes section below for a comprehensive description.

meshdiscretize.base.BaseMesh mesh

The mesh on which the regularization is discretized.

orientation{‘x’, ‘y’, ‘z’}

The direction along which smoothness is enforced.

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. If None, the reference model in the inversion is set to the starting model. To include the reference model in the regularization, the reference_model_in_smooth property must be set to True.

reference_model_in_smoothbool, optional

Whether to include the reference model in the smoothness regularization.

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 regularization.RegularizationMesh.


We define the regularization function (objective function) for second-order smoothness along the x-direction as:

\[\phi (m) = \int_\Omega \, w(r) \, \bigg [ \frac{\partial^2 m}{\partial x^2} \bigg ]^2 \, dv\]

where \(m(r)\) is the model and \(w(r)\) is a user-defined weighting function.

For implementation within SimPEG, the regularization function and its variables must be discretized onto a mesh. The discretized approximation for the regularization function (objective function) is expressed in linear form as:

\[\phi (\mathbf{m}) = \sum_i \tilde{w}_i \, \bigg | \, \frac{\partial^2 m_i}{\partial x^2} \, \bigg |^2\]

where \(m_i \in \mathbf{m}\) are the discrete model parameter values defined on the mesh and \(\tilde{w}_i \in \mathbf{\tilde{w}}\) are amalgamated weighting constants that 1) account for cell dimensions in the discretization and 2) apply any user-defined weighting. This is equivalent to an objective function of the form:

\[\phi (\mathbf{m}) = \big \| \mathbf{W \, L_x \, m } \, \big \|^2\]


  • \(\mathbf{L_x}\) is a second-order derivative operator with respect to \(x\), and

  • \(\mathbf{W}\) is the weighting matrix.

Reference model in smoothness:

Second-order smoothness within a discrete reference model \(\mathbf{m}^{(ref)}\) can be preserved by including the reference model the smoothness regularization function. In this case, the objective function becomes:

\[\phi (\mathbf{m}) = \Big \| \mathbf{W L_x} \big [ \mathbf{m} - \mathbf{m}^{(ref)} \big ] \Big \|^2\]

This functionality is used by setting a reference model with the reference_model property, and by setting the reference_model_in_smooth parameter to True.

Custom weights and the weighting matrix:

Let \(\mathbf{w_1, \; w_2, \; w_3, \; ...}\) each represent an optional set of custom cell weights. The weighting applied within the objective function is given by:

\[\mathbf{\tilde{w}} = \mathbf{v} \odot \prod_j \mathbf{w_j}\]

where \(\mathbf{v}\) are the cell volumes. The weighting matrix used to apply the weights is given by:

\[\mathbf{W} = \textrm{diag} \Big ( \, \mathbf{\tilde{w}}^{1/2} \Big )\]

Each set of custom cell weights is stored within a dict as an (n_cells, ) numpy.ndarray. The weights can be set all at once during instantiation with the weights keyword argument as follows:

>>> reg = SmoothnessSecondOrder(mesh, weights={'weights_1': array_1, 'weights_2': array_2})

or set after instantiation using the set_weights method:

>>> reg.set_weights(weights_1=array_1, weights_2=array_2})



Weighting matrix.


Active cells defined on the regularization mesh.


Partial cell gradient operator.


Deprecated property for 'volume' and user defined weights.


active_cells.indActive has been deprecated.


Mapping from the inversion model parameters to the regularization mesh.


The model parameters.


reference_model.mref has been deprecated.


Number of model parameters.


Direction along which smoothness is enforced.


The parent objective function


Reference model.


regularization_mesh.regmesh has been deprecated.


Regularization mesh.


Units for the model parameters.


Return the keys for the existing cell weights




Evaluate the regularization function for the model provided.


Gradient of the regularization function evaluated for the model provided.

deriv2(m[, v])

Hessian of the regularization function evaluated for the model provided.


Evaluate the regularization kernel function.


Derivative of the regularization kernel function.


Cell weights for a given key.


alias of IdentityMap


Removes the weights for the key provided.


Adds (or updates) the specified weights to the regularization.

test([x, num])

Run a convergence test on both the first and second derivatives.