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 - BaseObjectiveFunctionclass 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 inherit- simpeg.data_misfit.BaseDataMisfit.- Parameters:
- nPint
- Number of model parameters. 
- mappingsimpeg.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.
- counterNoneorsimpeg.utils.Counter
- Assign a SimPEG - Counterobject 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
 
Sparse Inversion with Iteratively Re-Weighted Least-Squares
 
2.5D DC Resistivity and IP Least-Squares Inversion
 
1D Inversion of Time-Domain Data for a Single Sounding
 
Sparse Norm Inversion of 2D Seismic Tomography Data
 
Cross-gradient Joint Inversion of Gravity and Magnetic Anomaly 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
 
     
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
