simpeg.maps.InjectActiveCells#
- class simpeg.maps.InjectActiveCells(mesh, indActive=None, valInactive=0.0, nC=None)[source]#
Bases:
IdentityMap
Map active cells model to all cell of a mesh.
The
InjectActiveCells
class is used to define the mapping when the model consists of physical property values for a set of active mesh cells; e.g. cells below topography. For a discrete set of model parameters \(\mathbf{m}\) defined on a set of active cells, the mapping \(\mathbf{u}(\mathbf{m})\) is defined as:\[\mathbf{u}(\mathbf{m}) = \mathbf{Pm} + \mathbf{d}\, m_\perp\]where \(\mathbf{P}\) is a (nC , nP) projection matrix from active cells to all mesh cells, and \(\mathbf{d}\) is a (nC , 1) matrix that projects the inactive cell value \(m_\perp\) to all inactive mesh cells.
- Parameters:
- mesh
discretize.BaseMesh
A discretize mesh
- indActive
numpy.ndarray
Active cells array. Can be a boolean
numpy.ndarray
of length mesh.nC or anumpy.ndarray
ofint
containing the indices of the active cells.- valInactive
float
ornumpy.ndarray
The physical property value assigned to all inactive cells in the mesh
- mesh
Attributes
- Returns:
Determine whether or not this mapping is a linear operation.
The mesh used for the mapping
Number of parameters the model acts on.
Dimensions of the mapping
The physical property value assigned to all inactive cells in the mesh.
Methods
deriv
(m[, v])Derivative of the mapping with respect to the input parameters.
dot
(map1)Multiply two mappings to create a
simpeg.maps.ComboMap
.inverse
(u)Recover the model parameters (active cells) from a set of physical property values defined on the entire mesh.
test
([m, num, random_seed])Derivative test for the mapping.
Galleries and Tutorials using simpeg.maps.InjectActiveCells
#
PF: Gravity: Tiled Inversion Linear
Magnetic inversion on a TreeMesh
Magnetic inversion on a TreeMesh
Magnetic Amplitude inversion on a TreeMesh
3D DC inversion of Dipole Dipole array
2D inversion of Loop-Loop EM Data
EM: TDEM: 1D: Inversion with VTEM waveform
Predict Response from a Conductive and Magnetically Viscous Earth
Method of Equivalent Sources for Removing VRM Responses
Heagy et al., 2017 1D RESOLVE and SkyTEM Bookpurnong Inversions
Heagy et al., 2017 1D RESOLVE Bookpurnong Inversion
Heagy et al., 2017 Casing Example
Heagy et al., 2017 1D FDEM and TDEM inversions
PF: Gravity: Laguna del Maule Bouguer Gravity
Forward Simulation of VRM Response on a Tree Mesh
Forward Simulation Including Inductive Response
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
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
3D Forward Simulation on a Cylindrical Mesh
3D Forward Simulation on a Tree Mesh
3D Forward Simulation for Transient Response on a Cylindrical Mesh
3D Forward Simulation with User-Defined Waveforms
2.5D Forward Simulation of a DCIP Line
DC/IP Forward Simulation in 3D
2.5D DC Resistivity and IP Least-Squares Inversion
3D Least-Squares Inversion of DC and IP Data
Cross-gradient Joint Inversion of Gravity and Magnetic Anomaly Data
Forward Simulation of Total Magnetic Intensity Data
Forward Simulation of Gradiometry Data for Magnetic Vector Models
Sparse Norm Inversion for Total Magnetic Intensity Data on a Tensor Mesh
DC Resistivity Forward Simulation in 2.5D
DC Resistivity Forward Simulation in 3D
2.5D DC Resistivity Least-Squares Inversion
2.5D DC Resistivity Inversion with Sparse Norms
3D Least-Squares Inversion of DC Resistivity Data