simpeg.data_misfit.L2DataMisfit#
- class simpeg.data_misfit.L2DataMisfit(data, simulation, debug=False, counter=None, **kwargs)[source]#
Bases:
BaseDataMisfit
Least-squares data misfit.
Define the data misfit as the L2-norm of the weighted residual between observed data and predicted data for a given model. I.e.:
where
is the observed data vector, is the predicted data vector for a model vector , and is the data weighting matrix. The diagonal elements of are the reciprocals of the data uncertainties . Thus:- Parameters:
- data
simpeg.data.Data
A SimPEG data object that has observed data and uncertainties.
- simulation
simpeg.simulation.BaseSimulation
A SimPEG simulation object.
- debugbool
Print debugging information.
- counter
None
orsimpeg.utils.Counter
Assign a SimPEG
Counter
object to store iterations and run-times.
- data
Attributes
The data weighting matrix.
SimPEG
Counter
object to store iterations and run-times.A SimPEG data object.
Print debugging information.
Mapping from the model to the quantity evaluated in the object function.
Number of data.
Number of model parameters.
Shape of the Jacobian.
A SimPEG simulation object.
Methods
__call__
(m[, f])Evaluate the residual for a given model.
deriv
(m[, f])Gradient of the data misfit function evaluated for the model provided.
deriv2
(m, v[, f])Hessian of the data misfit function evaluated for the model provided.
map_class
alias of
IdentityMap
residual
(m[, f])Computes the data residual vector for a given model.
test
([x, num, random_seed])Run a convergence test on both the first and second derivatives.
Galleries and Tutorials using simpeg.data_misfit.L2DataMisfit
#

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