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:
- mesh
discretize.TreeMesh
ordiscretize.TensorMesh
Mesh use to run the gravity simulation.
- survey
simpeg.potential_fields.gravity.Survey
Gravity survey with information of the receivers.
- ind_active(
n_cells
)numpy.ndarray
,optional
Array that indicates which cells in
mesh
are active cells.- rho
numpy.ndarray
,optional
Density array for the active cells in the mesh.
- rhoMap
Mapping
,optional
Model mapping.
- sensitivity_dtype
numpy.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_path
str
,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.
- mesh
Attributes
Gravity forward operator
A list of solver objects to clean when the model is updated
SimPEG
Counter
object to store iterations and run-times.A list of properties stored on this object to delete when the model is updated
Engine that will be used to run the simulation.
Diagonal of GtG
Active topography cells.
The model for a linear problem physical property model.
Mesh for the simulation.
The inversion model.
Derivative of The model for a linear problem wrt the model.
Mapping of the inversion model to The model for a linear problem.
True if a model is necessary
Run simulation in parallel or single-threaded when using Numba.
Density physical property model.
Derivative of Density wrt the model.
Mapping of the inversion model to Density.
dtype of the sensitivity matrix.
Path to directory where sensitivity file is stored.
Numerical solver used in the forward simulation.
Solver-specific parameters.
Options for storing sensitivities.
The survey for the simulation.
Verbose progress printout.
n_processes
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
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.
Galleries and Tutorials using simpeg.potential_fields.gravity.Simulation3DIntegral
#
PF: Gravity: Tiled Inversion Linear
PF: Gravity: Laguna del Maule Bouguer Gravity
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
Forward Simulation of Gravity Anomaly Data on a Tensor Mesh
Forward Simulation of Gradiometry Data on a Tree Mesh
Least-Squares Inversion of Gravity Anomaly Data
Sparse Norm Inversion of Gravity Anomaly Data