simpeg.electromagnetics.static.resistivity.Simulation1DLayers

class simpeg.electromagnetics.static.resistivity.Simulation1DLayers(survey=None, sigma=None, sigmaMap=None, rho=None, rhoMap=None, thicknesses=None, thicknessesMap=None, hankel_filter='key_201_2012', fix_Jmatrix=False, **kwargs)

Bases: BaseSimulation

1D DC Simulation

Attributes

clean_on_model_update	A list of solver objects to clean when the model is updated
counter	SimPEG Counter object to store iterations and run-times.
deleteTheseOnModelUpdate	HasModel.deleteTheseOnModelUpdate has been deprecated.
fix_Jmatrix	Whether to fix the sensitivity matrix between iterations.
hankel_filter	The hankel filter key.
model	The inversion model.
needs_model	True if a model is necessary
rho	Electrical resistivity (ohm m) physical property model.
rhoDeriv	Derivative of Electrical resistivity (Ohm m) wrt the model.
rhoMap	Mapping of the inversion model to Electrical resistivity (Ohm m).
sensitivity_path	Path to directory where sensitivity file is stored.
sigma	Electrical conductivity (s/m) physical property model.
sigmaDeriv	Derivative of Electrical conductivity (S/m) wrt the model.
sigmaMap	Mapping of the inversion model to Electrical conductivity (S/m).
storeJ	Whether to store the sensitivity matrix.
survey	The DC survey object.
thicknesses	Thicknesses of the layers physical property model.
thicknessesDeriv	Derivative of thicknesses of the layers wrt the model.
thicknessesMap	Mapping of the inversion model to thicknesses of the layers.
verbose	Verbose progress printout.

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.
dpred([m, f])	Project fields to receiver locations :param Fields u: fields object :rtype: numpy.ndarray :return: data
fields(m)	Return the computed geophysical fields for the model provided.
getJ(m[, f, factor])	Generate Full sensitivity matrix using central difference
make_synthetic_data(m[, relative_error, ...])	Make synthetic data for the model and Gaussian noise provided.
residual(m, dobs[, f])	The data residual.