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_IRLSwithin 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#
 
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
 
 
 
 
 
 
 
 
