simpeg.electromagnetics.static.resistivity.simulation.BaseDCSimulation#
- class simpeg.electromagnetics.static.resistivity.simulation.BaseDCSimulation(mesh, survey=None, storeJ=False, miniaturize=False, surface_faces=None, **kwargs)[source]#
- Bases: - BaseElectricalPDESimulation- Base DC Problem - Attributes - Cell center inner product matrix. - Cell center property inner product matrix. - Cell center property inner product inverse matrix. - Cell center property inner product matrix. - Cell center property inner product inverse matrix. - Edge inner product matrix. - Edge inner product inverse matrix. - Edge property inner product matrix. - Edge property inner product inverse matrix. - Edge property inner product matrix. - Edge property inner product inverse matrix. - Face inner product matrix. - Face inner product inverse matrix. - Face property inner product matrix. - Face property inner product inverse matrix. - Face property inner product matrix. - Face property inner product inverse matrix. - Node inner product matrix. - Node inner product inverse matrix. - Node property inner product matrix. - Node property inner product inverse matrix. - Node property inner product matrix. - Node property inner product inverse matrix. - A list of solver objects to clean when the model is updated - SimPEG - Counterobject to store iterations and run-times.- HasModel.deleteTheseOnModelUpdate has been deprecated. - Mesh for the simulation. - The inversion model. - True if a model is necessary - Electrical resistivity (ohm m) physical property model. - Derivative of Electrical resistivity (Ohm m) wrt the model. - Mapping of the inversion model to Electrical resistivity (Ohm m). - Path to directory where sensitivity file is stored. - Electrical conductivity (s/m) physical property model. - Derivative of Electrical conductivity (S/m) wrt the model. - Mapping of the inversion model to Electrical conductivity (S/m). - Numerical solver used in the forward simulation. - Solver-specific parameters. - Whether to store the sensitivity matrix - Array defining which boundary faces to interpret as surfaces of Neumann boundary - The DC survey object. - Verbose progress printout. - Ainv - MccI - Vol - Methods - Jtvec(m, v[, f])- Compute adjoint sensitivity matrix (J^T) and vector (v) product. - Jtvec_approx(m, v[, f])- Approximation of the Jacobian transpose times a vector for the model provided. - Jvec(m, v[, f])- Compute sensitivity matrix (J) and vector (v) product. - Jvec_approx(m, v[, f])- Approximation of the Jacobian times a vector for the model provided. - MccRhoDeriv(u[, v, adjoint])- Derivative of MccProperty with respect to the model. - MccRhoIDeriv(u[, v, adjoint])- Derivative of MccPropertyI with respect to the model. - MccSigmaDeriv(u[, v, adjoint])- Derivative of MccProperty with respect to the model. - MccSigmaIDeriv(u[, v, adjoint])- Derivative of MccPropertyI with respect to the model. - MeRhoDeriv(u[, v, adjoint])- Derivative of MeProperty with respect to the model. - MeRhoIDeriv(u[, v, adjoint])- Derivative of MePropertyI with respect to the model. - MeSigmaDeriv(u[, v, adjoint])- Derivative of MeProperty with respect to the model. - MeSigmaIDeriv(u[, v, adjoint])- Derivative of MePropertyI with respect to the model. - MfRhoDeriv(u[, v, adjoint])- Derivative of MfProperty with respect to the model. - MfRhoIDeriv(u[, v, adjoint])- I Derivative of MfPropertyI with respect to the model. - MfSigmaDeriv(u[, v, adjoint])- Derivative of MfProperty with respect to the model. - MfSigmaIDeriv(u[, v, adjoint])- I Derivative of MfPropertyI with respect to the model. - MnRhoDeriv(u[, v, adjoint])- Derivative of MnProperty with respect to the model. - MnRhoIDeriv(u[, v, adjoint])- Derivative of MnPropertyI with respect to the model. - MnSigmaDeriv(u[, v, adjoint])- Derivative of MnProperty with respect to the model. - MnSigmaIDeriv(u[, v, adjoint])- Derivative of MnPropertyI with respect to the model. - dpred([m, f])- Predicted data for the model provided. - fields([m, calcJ])- Return the computed geophysical fields for the model provided. - getJtJdiag(m[, W, f])- Return the diagonal of JtJ - Evaluates the sources, and puts them in matrix form :rtype: tuple :return: q (nC or nN, nSrc) - make_synthetic_data(m[, relative_error, ...])- Make synthetic data for the model and Gaussian noise provided. - residual(m, dobs[, f])- The data residual. - getJ 
 
     
 
 
