simpeg.objective_function.BaseObjectiveFunction#
- class simpeg.objective_function.BaseObjectiveFunction(nP=None, mapping=None, has_fields=False, counter=None, debug=False)[source]#
Bases:
BaseSimPEG
Base class for creating objective functions.
The
BaseObjectiveFunction
class defines properties and methods inherited by other classes in SimPEG that represent objective functions; e.g. regularization, data misfit. These include convenient methods for testing the order of convergence and ajoint operations.Important
This class is not meant to be instantiated. You should inherit from it to create your own objective function class.
Important
If building a regularization function within SimPEG, please inherit
simpeg.regularization.BaseRegularization
, as this class has additional functionality related to regularization. And if building a data misfit function, please inheritsimpeg.data_misfit.BaseDataMisfit
.- Parameters:
- nP
int
Number of model parameters.
- mapping
simpeg.mapping.BaseMap
A SimPEG mapping object that maps from the model space to the quantity evaluated in the objective function.
- has_fieldsbool
If
True
, predicted fields for a simulation and a given model can be used to evaluate the objective function quickly.- counter
None
orsimpeg.utils.Counter
Assign a SimPEG
Counter
object to store iterations and run-times.- debugbool
Print debugging information.
- nP
Attributes
Mapping from the model to the quantity evaluated in the object function.
Number of model parameters.
Methods
__call__
(x[, f])Evaluate the objective function for a given model.
deriv
(m, **kwargs)Gradient of the objective function evaluated for the model provided.
deriv2
(m[, v])Hessian of the objective function evaluated for the model provided.
map_class
Base class of expected maps.
test
([x, num, random_seed])Run a convergence test on both the first and second derivatives.
Galleries and Tutorials using simpeg.objective_function.BaseObjectiveFunction
#

Method of Equivalent Sources for Removing VRM Responses

Petrophysically guided inversion (PGI): Linear example

Petrophysically guided inversion: Joint linear example with nonlinear relationships

Heagy et al., 2017 1D RESOLVE and SkyTEM Bookpurnong Inversions

Heagy et al., 2017 1D RESOLVE Bookpurnong Inversion

1D Inversion of Time-Domain Data for a Single Sounding

2.5D DC Resistivity and IP Least-Squares Inversion

Compare weighting strategy with Inversion of surface Gravity Anomaly Data

Sparse Norm Inversion for Total Magnetic Intensity Data on a Tensor Mesh

Sparse Inversion with Iteratively Re-Weighted Least-Squares

Sparse Norm Inversion of 2D Seismic Tomography Data

Joint PGI of Gravity + Magnetic on an Octree mesh using full petrophysical information

Joint PGI of Gravity + Magnetic on an Octree mesh without petrophysical information

Cross-gradient Joint Inversion of Gravity and Magnetic Anomaly Data