simpeg.potential_fields.gravity.Simulation3DIntegral#

class simpeg.potential_fields.gravity.Simulation3DIntegral(mesh, rho=None, rhoMap=None, engine='geoana', numba_parallel=True, **kwargs)[source]#

Bases: BasePFSimulation

Gravity simulation in integral form.

Important

Density model is assumed to be in g/cc.

Important

Acceleration components (“gx”, “gy”, “gz”) are returned in mgal (\(10^{-5} m/s^2\)).

Important

Gradient components (“gxx”, “gyy”, “gzz”, “gxy”, “gxz”, “gyz”) are returned in Eotvos (\(10^{-9} s^{-2}\)).

Parameters:

meshdiscretize.TreeMesh or discretize.TensorMesh

Mesh use to run the gravity simulation.

surveysimpeg.potential_fields.gravity.Survey

Gravity survey with information of the receivers.

active_cells(n_cells) numpy.ndarray, optional

Array that indicates which cells in mesh are active cells.

rhonumpy.ndarray, optional

Density array for the active cells in the mesh.

rhoMapMapping, optional

Model mapping.

sensitivity_dtypenumpy.dtype, optional

Data type that will be used to build the sensitivity matrix.

store_sensitivities{“ram”, “disk”, “forward_only”}

Options for storing sensitivity matrix. There are 3 options

‘ram’: sensitivities are stored in the computer’s RAM
‘disk’: sensitivities are written to a directory
‘forward_only’: you intend only do perform a forward simulation and sensitivities do not need to be stored

sensitivity_pathstr, optional

Path to store the sensitivity matrix if store_sensitivities is set to "disk". Default to “./sensitivities”.

engine{“geoana”, “choclo”}, optional

Choose which engine should be used to run the forward model.

numba_parallelbool, optional

If True, the simulation will run in parallel. If False, it will run in serial. If engine is not "choclo" this argument will be ignored.

ind_activenp.ndarray of int or bool

Deprecated since version 0.23.0: Argument ind_active is deprecated in favor of active_cells and will be removed in SimPEG v0.24.0.

Attributes

`G`	Gravity forward operator
`active_cells`	Active cells in the mesh.
`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.
`engine`	Engine that will be used to run the simulation.
`gtg_diagonal`	Diagonal of GtG
`ind_active`	active_cells.ind_active has been deprecated.
`linear_model`	The model for a linear problem physical property model.
`mesh`	Mesh for the integral potential field simulations.
`model`	The inversion model.
`model_deriv`	Derivative of The model for a linear problem wrt the model.
`model_map`	Mapping of the inversion model to The model for a linear problem.
`n_processes`	Number of processes to use for forward modeling.
`needs_model`	True if a model is necessary
`numba_parallel`	Run simulation in parallel or single-threaded when using Numba.
`rho`	Density physical property model.
`rhoDeriv`	Derivative of Density wrt the model.
`rhoMap`	Mapping of the inversion model to Density.
`sensitivity_dtype`	dtype of the sensitivity matrix.
`sensitivity_path`	Path to directory where sensitivity file is stored.
`store_sensitivities`	Options for storing sensitivities.
`survey`	The survey for the simulation.
`verbose`	Verbose progress printout.

Methods

`Jtvec`(m, v[, f])	Sensitivity transposed times a vector
`Jtvec_approx`(m, v[, f])	Approximation of the Jacobian transpose times a vector for the model provided.
`Jvec`(m, v[, f])	Sensitivity times a vector
`Jvec_approx`(m, v[, f])	Approximation of the Jacobian times a vector for the model provided.
`dpred`([m, f])	Predicted data for the model provided.
`evaluate_integral`(receiver_location, components)	Compute the forward linear relationship between the model and the physics at a point and for all components of the survey.
`fields`(m)	Forward model the gravity field of the mesh on the receivers in the survey
`getJ`(m[, f])	Sensitivity matrix
`getJtJdiag`(m[, W, f])	Return the diagonal of JtJ
`linear_operator`()	Return linear operator.
`make_synthetic_data`(m[, relative_error, ...])	Make synthetic data for the model and Gaussian noise provided.
`residual`(m, dobs[, f])	The data residual.