Forward Simulation¶
Simulation Class¶
The problem is a partial differential equation of the form:
Here, \(m\) is the model and u is the field (or fields). Given the model, \(m\), we can calculate the fields \(u(m)\), however, the data we collect is a subset of the fields, and can be defined by a linear projection, \(P\).
For the inverse problem, we are interested in how changing the model transforms the data, as such we can take write the Taylor expansion:
We can linearize and define the sensitivity matrix as:
The sensitivity matrix, and it’s transpose will be used in the inverse problem to (locally) find how model parameters change the data, and optimize!
Working with the general PDE, \(c(m, u) = 0\), where m is the model and u is the field, the sensitivity is defined as:
We can take the derivative of the PDE:
If the forward problem is invertible, then we can rearrange for \(\frac{\partial u}{\partial m}\):
This can often be computed given a vector (i.e. \(J(v)\)) rather than stored, as \(J\) is a large dense matrix.
The API¶
Simulation¶

class
SimPEG.simulation.
BaseSimulation
(*args, **kwargs)[source]¶ BaseSimulation is the base class for all geophysical forward simulations in SimPEG.
Required Properties:
counter (
Counter
): A SimPEG.utils.Counter object, an instance of Countermesh (
BaseMesh
): a discretize mesh instance, an instance of BaseMeshsensitivity_path (
String
): path to store the sensitivty, a unicode string, Default: ./sensitivity/None
solver_opts (
Dictionary
): solver options as a kwarg dict, a dictionarysurvey (
BaseSurvey
): a survey object, an instance of BaseSurvey
Optional Properties:
model (
Model
): Inversion model., a numpy array of <class ‘float’>, <class ‘int’> with shape (*, *) or (*)

property
mesh
¶ mesh (
BaseMesh
): a discretize mesh instance, an instance of BaseMesh

property
survey
¶ survey (
BaseSurvey
): a survey object, an instance of BaseSurvey

property
sensitivity_path
¶ sensitivity_path (
String
): path to store the sensitivty, a unicode string, Default: ./sensitivity/

property
solver_opts
¶ solver_opts (
Dictionary
): solver options as a kwarg dict, a dictionary

deleteTheseOnModelUpdate
= []¶

clean_on_model_update
= []¶ List of matrix names to have their factors cleared on a model update

property
Solver
¶ Solver has been deprecated. See simulation.solver for documentation

property
solverOpts
¶ solverOpts has been deprecated. See solver_opts for documentation

property
solver
¶ None

fields
(m=None)[source]¶ u = fields(m) The field given the model. :param numpy.ndarray m: model :rtype: numpy.ndarray :return: u, the fields

dpred
(m, f=None)[source]¶ Create the projected data from a model. The fields, f, (if provided) will be used for the predicted data instead of recalculating the fields (which may be expensive!).
\[d_\text{pred} = P(f(m))\]Where P is a projection of the fields onto the data space.

Jvec
(m, v, f=None)[source]¶ Jv = Jvec(m, v, f=None) Effect of J(m) on a vector v. :param numpy.ndarray m: model :param numpy.ndarray v: vector to multiply :param Fields f: fields :rtype: numpy.ndarray :return: Jv

Jtvec
(m, v, f=None)[source]¶ Jtv = Jtvec(m, v, f=None) Effect of transpose of J(m) on a vector v. :param numpy.ndarray m: model :param numpy.ndarray v: vector to multiply :param Fields f: fields :rtype: numpy.ndarray :return: JTv

Jvec_approx
(m, v, f=None)[source]¶ Approximate effect of J(m) on a vector v :param numpy.ndarray m: model :param numpy.ndarray v: vector to multiply :param Fields f: fields :rtype: numpy.ndarray :return: approxJv

Jtvec_approx
(m, v, f=None)[source]¶ Approximate effect of transpose of J(m) on a vector v. :param numpy.ndarray m: model :param numpy.ndarray v: vector to multiply :param Fields f: fields :rtype: numpy.ndarray :return: JTv

residual
(m, dobs, f=None)[source]¶ The data residual:
\[\mu_\text{data} = \mathbf{d}_\text{pred}  \mathbf{d}_\text{obs}\] Parameters
m (numpy.ndarray) – geophysical model
f (numpy.ndarray) – fields
 Return type
 Returns
data residual

make_synthetic_data
(m, relative_error=0.05, noise_floor=0.0, f=None, add_noise=False, **kwargs)[source]¶ Make synthetic data given a model, and a standard deviation. :param numpy.ndarray m: geophysical model :param numpy.ndarray relative_error: standard deviation :param numpy.ndarray noise_floor: noise floor :param numpy.ndarray f: fields for the given model (if precalculated)

class
SimPEG.simulation.
BaseTimeSimulation
(*args, **kwargs)[source]¶ Base class for a time domain simulation
Required Properties:
counter (
Counter
): A SimPEG.utils.Counter object, an instance of Countermesh (
BaseMesh
): a discretize mesh instance, an instance of BaseMeshsensitivity_path (
String
): path to store the sensitivty, a unicode string, Default: ./sensitivity/None
solver_opts (
Dictionary
): solver options as a kwarg dict, a dictionarysurvey (
BaseSurvey
): a survey object, an instance of BaseSurveyt0 (
Float
): Origin of the time discretization, a float, Default: 0.0 time_steps (
TimeStepArray
): Sets/gets the time steps for the time domain simulation. You can set as an array of dt’s or as a list of tuples/floats. Tuples must be length two with […, (dt, repeat), …] For example, the following setters are the same:
sim.time_steps = [(1e6, 3), 1e5, (1e4, 2)] sim.time_steps = np.r_[1e6,1e6,1e6,1e5,1e4,1e4]
, an array or list of tuples specifying the mesh tensor of <class ‘float’> with shape (*)
 time_steps (
Optional Properties:
model (
Model
): Inversion model., a numpy array of <class ‘float’>, <class ‘int’> with shape (*, *) or (*)

property
time_steps
¶ time_steps (
TimeStepArray
): Sets/gets the time steps for the time domain simulation. You can set as an array of dt’s or as a list of tuples/floats. Tuples must be length two with […, (dt, repeat), …] For example, the following setters are the same:sim.time_steps = [(1e6, 3), 1e5, (1e4, 2)] sim.time_steps = np.r_[1e6,1e6,1e6,1e5,1e4,1e4]
, an array or list of tuples specifying the mesh tensor of <class ‘float’> with shape (*)

property
time_mesh
¶

property
nT
¶

property
times
¶ Modeling times

property
timeSteps
¶ timeSteps has been deprecated. See time_steps for documentation

property
timeMesh
¶ time_mesh.timeMesh has been deprecated. See time_mesh for documentation

class
SimPEG.simulation.
LinearSimulation
(*args, **kwargs)[source]¶ Class for a linear simulation of the form
\[d = Gm\]where \(d\) is a vector of the data, G is the simulation matrix and \(m\) is the model. Inherit this class to build a linear simulation.
Required Properties:
counter (
Counter
): A SimPEG.utils.Counter object, an instance of Countermesh (
BaseMesh
): a discretize mesh instance, an instance of BaseMeshsensitivity_path (
String
): path to store the sensitivty, a unicode string, Default: ./sensitivity/None
solver_opts (
Dictionary
): solver options as a kwarg dict, a dictionarysurvey (
BaseSurvey
): a survey object, an instance of BaseSurvey
Optional Properties:
linear_model (
PhysicalProperty
): The model for a linear problem, a physical propertymodel (
Model
): Inversion model., a numpy array of <class ‘float’>, <class ‘int’> with shape (*, *) or (*)model_map (
Mapping
): Mapping of The model for a linear problem to the inversion model., a SimPEG Map
Other Properties:
model_deriv (
Derivative
): Derivative of The model for a linear problem wrt the model.

property
linear_model
¶ The model for a linear problem

property
model_map
¶ Mapping of The model for a linear problem to the inversion model.

property
model_deriv
¶ Derivative of The model for a linear problem wrt the model.

property
mesh
¶ mesh (
BaseMesh
): a discretize mesh instance, an instance of BaseMesh

property
survey
¶ survey (
BaseSurvey
): a survey object, an instance of BaseSurvey

property
G
¶

fields
(m)[source]¶ u = fields(m) The field given the model. :param numpy.ndarray m: model :rtype: numpy.ndarray :return: u, the fields

dpred
(m, f=None)[source]¶ Create the projected data from a model. The fields, f, (if provided) will be used for the predicted data instead of recalculating the fields (which may be expensive!).
\[d_\text{pred} = P(f(m))\]Where P is a projection of the fields onto the data space.
Fields¶

class
SimPEG.fields.
Fields
(*args, **kwargs)[source]¶  Fancy Field Storage
Required Properties:
 aliasFields (
Dictionary
): a dictionary of the aliased fields with [alias, location, function], e.g. {“b”:[“e”,”F”,lambda(F,e,ind)]} , a dictionary
 aliasFields (
 knownFields (
Dictionary
): a dictionary with the names of the know fields and their location on a mesh e.g. {“e”: “E”, “phi”: “CC”} , a dictionary
 knownFields (
simulation (
BaseSimulation
): a SimPEG simulation, an instance of BaseSimulation

property
knownFields
¶ knownFields (
Dictionary
): a dictionary with the names of the know fields and their location on a mesh e.g. {“e”: “E”, “phi”: “CC”} , a dictionary

property
aliasFields
¶ aliasFields (
Dictionary
): a dictionary of the aliased fields with [alias, location, function], e.g. {“b”:[“e”,”F”,lambda(F,e,ind)]} , a dictionary

dtype
¶ alias of
builtins.float

property
simulation
¶ simulation (
BaseSimulation
): a SimPEG simulation, an instance of BaseSimulation

property
mesh
¶

property
survey
¶

property
approxSize
¶ The approximate cost to storing all of the known fields.

class
SimPEG.fields.
TimeFields
(*args, **kwargs)[source]¶  Fancy Field Storage for time domain problems
fields = TimeFields(simulation=simulation, knownFields={'phi':'CC'}) fields[:,'phi', timeInd] = phi print(fields[src0,'phi'])
Required Properties:
 aliasFields (
Dictionary
): a dictionary of the aliased fields with [alias, location, function], e.g. {“b”:[“e”,”F”,lambda(F,e,ind)]} , a dictionary
 aliasFields (
 knownFields (
Dictionary
): a dictionary with the names of the know fields and their location on a mesh e.g. {“e”: “E”, “phi”: “CC”} , a dictionary
 knownFields (
simulation (
BaseTimeSimulation
): a SimPEG time simulation, an instance of BaseTimeSimulation

property
simulation
¶ simulation (
BaseTimeSimulation
): a SimPEG time simulation, an instance of BaseTimeSimulation
Survey¶

class
SimPEG.survey.
BaseSurvey
(*args, **kwargs)[source]¶ Survey holds the sources and receivers for a survey
Required Properties:
counter (
Counter
): A SimPEG counter object, an instance of Countersource_list (a list of
BaseSrc
): A list of sources for the survey, a list (each item is an instance of BaseSrc)

property
source_list
¶ source_list (a list of
BaseSrc
): A list of sources for the survey, a list (each item is an instance of BaseSrc)

property
nD
¶ Number of data

property
vnD
¶ Vector number of data

property
nSrc
¶ Number of Sources

property
srcList
¶ srcList has been deprecated. See source_list for documentation

class
SimPEG.survey.
BaseTimeSurvey
(*args, **kwargs)[source]¶ Required Properties:
counter (
Counter
): A SimPEG counter object, an instance of Countersource_list (a list of
BaseSrc
): A list of sources for the survey, a list (each item is an instance of BaseSrc)

property
unique_times
¶

property
times
¶ unique_times.times has been deprecated. See unique_times for documentation
Data¶

class
SimPEG.data.
Data
(*args, **kwargs)[source]¶ Data storage. This class keeps track of observed data, relative error of those data and the noise floor.
data = Data(survey, dobs=dobs, relative_error=relative, noise_floor=floor)
or
data = Data(survey, dobs=dobs, standard_deviation=standard_deviation)
Required Properties:
 dobs (
Array
): Vector of the observed data. The data can be set using the survey parameters:
data = Data(survey) for src in survey.source_list: for rx in src.receiver_list: data[src, rx] = datum
, a list or numpy array of <class ‘float’>, <class ‘int’> with shape (*)
 dobs (
 noise_floor (
UncertaintyArray
): Noise floor of the data. This can be set using an array of the same size as the data (e.g. if you want to assign a different noise floor to each datum) or as a scalar if you would like to assign a the same noise floor to all data.
The standard_deviation is constructed as follows:
relative_error * np.abs(dobs) + noise_floor
For example, if you set
data = Data(survey, dobs=dobs) data.noise_floor = 1e10
then the contribution to the standard_deviation is equal to
data.noise_floor
, An array that can be set by a scalar value or numpy array of <class ‘float’>, <class ‘int’> with shape (*)
 noise_floor (
 relative_error (
UncertaintyArray
): Relative error of the data. This can be set using an array of the same size as the data (e.g. if you want to assign a different relative error to each datum) or as a scalar if you would like to assign a the same relative error to all data.
The standard_deviation is constructed as follows:
relative_error * np.abs(dobs) + noise_floor
For example, if you set
data = Data(survey, dobs=dobs) data.relative_error = 0.05
then the contribution to the standard_deviation is equal to
data.relative_error * np.abs(data.dobs)
, An array that can be set by a scalar value or numpy array of <class ‘float’>, <class ‘int’> with shape (*)
 relative_error (
survey (
BaseSurvey
): a SimPEG survey object, an instance of BaseSurvey

property
survey
¶ survey (
BaseSurvey
): a SimPEG survey object, an instance of BaseSurvey

property
dobs
¶ dobs (
Array
): Vector of the observed data. The data can be set using the survey parameters:data = Data(survey) for src in survey.source_list: for rx in src.receiver_list: data[src, rx] = datum
, a list or numpy array of <class ‘float’>, <class ‘int’> with shape (*)

property
relative_error
¶ relative_error (
UncertaintyArray
): Relative error of the data. This can be set using an array of the same size as the data (e.g. if you want to assign a different relative error to each datum) or as a scalar if you would like to assign a the same relative error to all data.The standard_deviation is constructed as follows:
relative_error * np.abs(dobs) + noise_floor
For example, if you set
data = Data(survey, dobs=dobs) data.relative_error = 0.05
then the contribution to the standard_deviation is equal to
data.relative_error * np.abs(data.dobs)
, An array that can be set by a scalar value or numpy array of <class ‘float’>, <class ‘int’> with shape (*)

property
noise_floor
¶ noise_floor (
UncertaintyArray
): Noise floor of the data. This can be set using an array of the same size as the data (e.g. if you want to assign a different noise floor to each datum) or as a scalar if you would like to assign a the same noise floor to all data.The standard_deviation is constructed as follows:
relative_error * np.abs(dobs) + noise_floor
For example, if you set
data = Data(survey, dobs=dobs) data.noise_floor = 1e10
then the contribution to the standard_deviation is equal to
data.noise_floor
, An array that can be set by a scalar value or numpy array of <class ‘float’>, <class ‘int’> with shape (*)

property
standard_deviation
¶ Data standard deviations. If a relative error and noise floor are provided, the standard_deviation is
data.standard_deviation = ( data.relative_error*np.abs(data.dobs) + data.noise_floor )
otherwise, the standard_deviation can be set directly
data.standard_deviation = 0.05 * np.absolute(self.dobs) + 1e12
Note that setting the standard_deviation directly will clear the relative_error and set the value to the noise_floor property.

property
nD
¶

property
shape
¶

property
index_dictionary
¶ Dictionary of data indices by sources and receivers. To set data using survey parameters:
data = Data(survey) for src in survey.source_list: for rx in src.receiver_list: index = data.index_dictionary[src][rx] data.dobs[index] = datum

property
std
¶ std has been deprecated. See relative_error for documentation

property
eps
¶ eps has been deprecated. See noise_floor for documentation

class
SimPEG.data.
SyntheticData
(*args, **kwargs)[source]¶ Data class for synthetic data. It keeps track of observed and clean data
Required Properties:
 dclean (
Array
): Vector of the clean synthetic data. The data can be set using the survey parameters:
data = Data(survey) for src in survey.source_list: for rx in src.receiver_list: index = data.index_dictionary(src, rx) data.dclean[indices] = datum
, a list or numpy array of <class ‘float’>, <class ‘int’> with shape (*)
 dclean (
 dobs (
Array
): Vector of the observed data. The data can be set using the survey parameters:
data = Data(survey) for src in survey.source_list: for rx in src.receiver_list: data[src, rx] = datum
, a list or numpy array of <class ‘float’>, <class ‘int’> with shape (*)
 dobs (
 noise_floor (
UncertaintyArray
): Noise floor of the data. This can be set using an array of the same size as the data (e.g. if you want to assign a different noise floor to each datum) or as a scalar if you would like to assign a the same noise floor to all data.
The standard_deviation is constructed as follows:
relative_error * np.abs(dobs) + noise_floor
For example, if you set
data = Data(survey, dobs=dobs) data.noise_floor = 1e10
then the contribution to the standard_deviation is equal to
data.noise_floor
, An array that can be set by a scalar value or numpy array of <class ‘float’>, <class ‘int’> with shape (*)
 noise_floor (
 relative_error (
UncertaintyArray
): Relative error of the data. This can be set using an array of the same size as the data (e.g. if you want to assign a different relative error to each datum) or as a scalar if you would like to assign a the same relative error to all data.
The standard_deviation is constructed as follows:
relative_error * np.abs(dobs) + noise_floor
For example, if you set
data = Data(survey, dobs=dobs) data.relative_error = 0.05
then the contribution to the standard_deviation is equal to
data.relative_error * np.abs(data.dobs)
, An array that can be set by a scalar value or numpy array of <class ‘float’>, <class ‘int’> with shape (*)
 relative_error (
survey (
BaseSurvey
): a SimPEG survey object, an instance of BaseSurvey

property
dclean
¶ dclean (
Array
): Vector of the clean synthetic data. The data can be set using the survey parameters:data = Data(survey) for src in survey.source_list: for rx in src.receiver_list: index = data.index_dictionary(src, rx) data.dclean[indices] = datum
, a list or numpy array of <class ‘float’>, <class ‘int’> with shape (*)
Sources¶

class
SimPEG.survey.
BaseSrc
(*args, **kwargs)[source]¶ SimPEG Source Object
Required Properties:
receiver_list (a list of
BaseRx
): receiver list, a list (each item is an instance of BaseRx)
Optional Properties:
location (
SourceLocationArray
): Location of the source [x, y, z] in 3D, a 1D array denoting the source location of <class ‘float’>, <class ‘int’> with shape (*)

property
loc
¶ loc has been deprecated. See location for documentation

property
rxList
¶ rxList has been deprecated. See receiver_list for documentation

property
nD
¶ Number of data

property
vnD
¶ Vector number of data

property
receiver_list
¶ receiver_list (a list of
BaseRx
): receiver list, a list (each item is an instance of BaseRx)

property
location
¶ location (
SourceLocationArray
): Location of the source [x, y, z] in 3D, a 1D array denoting the source location of <class ‘float’>, <class ‘int’> with shape (*)
Receivers¶

class
SimPEG.survey.
BaseRx
(*args, **kwargs)[source]¶ SimPEG Receiver Object
Required Properties:
locations (
RxLocationArray
): Locations of the receivers (nRx x nDim), an array of receiver locations of <class ‘float’>, <class ‘int’> with shape (*, *)projGLoc (
StringChoice
): Projection grid location, default is CC, any of “CC”, “Fx”, “Fy”, “Fz”, “Ex”, “Ey”, “Ez”, “N”, Default: CCstoreProjections (
Boolean
): Store calls to getP (organized by mesh), a boolean, Default: True

property
projGLoc
¶ projGLoc (
StringChoice
): Projection grid location, default is CC, any of “CC”, “Fx”, “Fy”, “Fz”, “Ex”, “Ey”, “Ez”, “N”, Default: CC

property
storeProjections
¶ storeProjections (
Boolean
): Store calls to getP (organized by mesh), a boolean, Default: True

property
locations
¶ locations (
RxLocationArray
): Locations of the receivers (nRx x nDim), an array of receiver locations of <class ‘float’>, <class ‘int’> with shape (*, *)

property
locs
¶ locs has been deprecated. See locations for documentation

property
nD
¶ Number of data in the receiver.

class
SimPEG.survey.
BaseTimeRx
(*args, **kwargs)[source]¶ SimPEG Receiver Object for timedomain simulations
Required Properties:
locations (
RxLocationArray
): Locations of the receivers (nRx x nDim), an array of receiver locations of <class ‘float’>, <class ‘int’> with shape (*, *)projGLoc (
StringChoice
): Projection grid location, default is CC, any of “CC”, “Fx”, “Fy”, “Fz”, “Ex”, “Ey”, “Ez”, “N”, Default: CCprojTLoc (
StringChoice
): location on the time mesh where the data are projected from, either “N” or “CC”, Default: NstoreProjections (
Boolean
): Store calls to getP (organized by mesh), a boolean, Default: Truetimes (
Array
): times where the recievers measure data, a list or numpy array of <class ‘float’>, <class ‘int’> with shape (*)

property
projTLoc
¶ projTLoc (
StringChoice
): location on the time mesh where the data are projected from, either “N” or “CC”, Default: N

property
times
¶ times (
Array
): times where the recievers measure data, a list or numpy array of <class ‘float’>, <class ‘int’> with shape (*)

property
nD
¶ Number of data in the receiver.

getSpatialP
(mesh)[source]¶ Returns the spatial projection matrix.
Note
This is not stored in memory, but is created on demand.