SimPEG.regularization.Sparse#
- class SimPEG.regularization.Sparse(mesh, active_cells=None, norms=None, gradient_type='total', irls_scaled=True, irls_threshold=1e-08, **kwargs)[source]#
Bases:
SimPEG.regularization.base.WeightedLeastSquares
The regularization is:
\[R(m) = \frac{1}{2}\mathbf{(m-m_\text{ref})^\top W^\top R^\top R W(m-m_\text{ref})}\]where the IRLS weight
\[R = \eta \text{diag} \left[\mathbf{r}_s \right]^{1/2} \ r_{s_i} = {\Big( {({m_i}^{(k-1)})}^{2} + \epsilon^2 \Big)}^{p_s/2 - 1}\]where k denotes the iteration number. So the derivative is straight forward:
\[R(m) = \mathbf{W^\top R^\top R W (m-m_\text{ref})}\]The IRLS weights are re-computed after each beta solves using
Update_IRLS
within the inversion directives.Attributes
0.gradientType has been deprecated.
Choice of gradient measure used in the irls weights
Scale irls weights.
Constant added to the denominator of the IRLS weights for stability.
Value of the norm
Methods
update_weights
(model)Trigger irls update on all children
Galleries and Tutorials using SimPEG.regularization.Sparse
#
PF: Gravity: Tiled Inversion Linear
Magnetic inversion on a TreeMesh
Magnetic Amplitude inversion on a TreeMesh
PF: Gravity: Laguna del Maule Bouguer Gravity
1D Inversion of for a Single Sounding
Sparse Norm Inversion of Gravity Anomaly Data
Sparse 1D Inversion of Sounding Data
2.5D DC Resistivity Inversion with Sparse Norms
Sparse Norm Inversion for Total Magnetic Intensity Data on a Tensor Mesh
Sparse Norm Inversion of 2D Seismic Tomography Data
1D Inversion of Time-Domain Data for a Single Sounding
Sparse Inversion with Iteratively Re-Weighted Least-Squares