Forward Problem¶
Problem 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¶
Problem¶

class
SimPEG.Problem.
BaseProblem
(mesh, **kwargs)¶  Problem is the base class for all geophysical forward problems
 in SimPEG.
Optional Properties:
 model (
Model
): Inversion model., a numpy array of <class ‘float’>, <class ‘int’> with shape (*) or (*, *)

Jtvec
(m, v, f=None)¶ Effect of transpose of J(m) on a vector v.
Parameters:  m (numpy.ndarray) – model
 v (numpy.ndarray) – vector to multiply
 f (Fields) – fields
Return type: Returns: JTv

Jtvec_approx
(m, v, f=None)¶ Approximate effect of transpose of J(m) on a vector v.
Parameters:  m (numpy.ndarray) – model
 v (numpy.ndarray) – vector to multiply
 f (Fields) – fields
Return type: Returns: JTv

Jvec
(m, v, f=None)¶ Effect of J(m) on a vector v.
Parameters:  m (numpy.ndarray) – model
 v (numpy.ndarray) – vector to multiply
 f (Fields) – fields
Return type: Returns: Jv

Jvec_approx
(m, v, f=None)¶ Approximate effect of J(m) on a vector v
Parameters:  m (numpy.ndarray) – model
 v (numpy.ndarray) – vector to multiply
 f (Fields) – fields
Return type: Returns: approxJv

clean_on_model_update
= []¶

counter
= None¶

curModel
¶ Setting the curModel is depreciated.
Use SimPEG.Problem.model instead.

deleteTheseOnModelUpdate
= []¶

fields
(m)¶ The field given the model.
Parameters: m (numpy.ndarray) – model Return type: numpy.ndarray Returns: u, the fields

ispaired
¶ True if the problem is paired to a survey.

mapPair
¶ alias of
IdentityMap

mapping
¶ Setting an unnamed mapping has been depreciated in v0.4.0. Please see the release notes for more details.

mesh
= None¶

pair
(d)¶ Bind a survey to this problem instance using pointers.

solverOpts
= {}¶

survey
¶ The survey object for this problem.

surveyPair
¶ alias of
BaseSurvey

unpair
()¶ Unbind a survey from this problem instance.

class
SimPEG.Problem.
BaseTimeProblem
(mesh, **kwargs)¶ Sets up that basic needs of a time domain problem.
Optional Properties:
 model (
Model
): Inversion model., a numpy array of <class ‘float’>, <class ‘int’> with shape (*) or (*, *)

nT
¶ Number of time steps.

t0
¶

timeMesh
¶

timeSteps
¶ Sets/gets the timeSteps for the time domain problem.
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:
prob.timeSteps = [(1e6, 3), 1e5, (1e4, 2)] prob.timeSteps = np.r_[1e6,1e6,1e6,1e5,1e4,1e4]

times
¶ Modeling times
 model (
Fields¶

class
SimPEG.Fields.
Fields
(mesh, survey, **kwargs)¶ Fancy Field Storage
u[:,’phi’] = phi print(u[src0,’phi’])

aliasFields
= None¶

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

knownFields
= None¶

startup
()¶


class
SimPEG.Fields.
TimeFields
(mesh, survey, **kwargs)¶ Fancy Field Storage for time domain problems
u[:,’phi’, timeInd] = phi print(u[src0,’phi’])
Survey¶

class
SimPEG.Survey.
BaseSurvey
(**kwargs)¶ Survey holds the observed data, and the standard deviations.

counter
= None¶

dobs
= None¶

dpred
(m, f=None)¶ 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.
Note
To use survey.dpred(), SimPEG requires that a problem be bound to the survey. If a problem has not been bound, an Exception will be raised. To bind a problem to the Data object:
survey.pair(myProblem)

dtrue
= None¶

eps
= None¶

eval
(f)¶ This function projects the fields onto the data space.
\[d_\text{pred} = \mathbf{P} f(m)\]

evalDeriv
(f)¶ This function s the derivative of projects the fields onto the data space.
\[\frac{\partial d_\text{pred}}{\partial u} = \mathbf{P}\]

getSourceIndex
(sources)¶

isSynthetic
¶ Check if the data is synthetic.

ispaired
¶

makeSyntheticData
(m, std=None, f=None, force=False)¶ Make synthetic data given a model, and a standard deviation.
Parameters:  m (numpy.ndarray) – geophysical model
 std (numpy.ndarray) – standard deviation
 u (numpy.ndarray) – fields for the given model (if precalculated)
 force (bool) – force overwriting of dobs

mesh
¶ Mesh of the paired problem.

mtrue
= None¶

nD
¶ Number of data

nSrc
¶ Number of Sources

pair
(p)¶ Bind a problem to this survey instance using pointers

prob
¶ The geophysical problem that explains this survey, use:
survey.pair(prob)

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

srcList
¶ Source List

srcPair
¶ alias of
BaseSrc

std
= None¶

unpair
()¶ Unbind a problem from this survey instance

vnD
¶ Vector number of data


class
SimPEG.Survey.
BaseSrc
(rxList, **kwargs)¶ SimPEG Source Object

loc
= None¶

nD
¶ Number of data

rxList
= None¶

rxPair
¶ alias of
BaseRx

vnD
¶ Vector number of data


class
SimPEG.Survey.
BaseRx
(locs, rxType, **kwargs)¶ SimPEG Receiver Object

getP
(mesh, projGLoc=None)¶ Returns the projection matrices as a list for all components collected by the receivers.
Note
Projection matrices are stored as a dictionary listed by meshes.

knownRxTypes
= None¶

locs
= None¶

nD
¶ Number of data in the receiver.

projGLoc
= 'CC'¶

rxType
¶ Receiver Type

storeProjections
= True¶


class
SimPEG.Survey.
BaseTimeRx
(locs, times, rxType, **kwargs)¶ SimPEG Receiver Object

getP
(mesh, timeMesh)¶ Returns the projection matrices as a list for all components collected by the receivers.
Note
Projection matrices are stored as a dictionary (mesh, timeMesh) if storeProjections is True

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

getTimeP
(timeMesh)¶ Returns the time projection matrix.
Note
This is not stored in memory, but is created on demand.

nD
¶ Number of data in the receiver.

projTLoc
= 'N'¶

times
= None¶


class
SimPEG.Survey.
Data
(survey, dobs=None, standard_deviation=None, floor=None)¶ Storage of data, standard_deviation and floor storage with fancy [Src,Rx] indexing.
Requried :param Survey survey: The survey descriping the layout of the data
Optional :param ndarray dobs: The data vector matching the src and rx in survey :param ndarray standard_deviation: The standard deviation vector matching the src and rx in survey :param ndarray floor: The floor vector for the data matching the src and rx in survey

calculate_uncertainty
()¶ Return the uncertainty base on standard_devation * np.abs(data) + floor
