simpeg.simulation.BaseTimeSimulation#

class simpeg.simulation.BaseTimeSimulation(t0=0.0, time_steps=None, **kwargs)[source]#

Bases: BaseSimulation

Base class for time domain simulations.

The BaseTimeSimulation defines properties and methods that are required when the finite volume approach is used to solve time-dependent forward simulations. Presently, SimPEG discretizes in time using the backward Euler approach. And as such, the user must now define the step lengths for the forward simulation.

Parameters:

t0float, optional: Initial time, in seconds, for the time-dependent forward simulation.
time_steps(n_steps, ) numpy.ndarray, optional: The time step lengths, in seconds, for the time domain simulation. This property can be also be set using a compact form; see Notes.

Attributes

`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.
`model`	The inversion model.
`nT`	Total number of time steps.
`needs_model`	True if a model is necessary
`sensitivity_path`	Path to directory where sensitivity file is stored.
`survey`	The survey for the simulation.
`t0`	Initial time, in seconds, for the time-dependent forward simulation.
`time_mesh`	Time mesh for easy interpolation to observation times.
`time_steps`	Time step lengths, in seconds, for the time domain simulation.
`times`	Evaluation times.
`verbose`	Verbose progress printout.

Methods

`Jtvec`(m, v[, f])	Compute the Jacobian transpose times a vector for the model provided.
`Jtvec_approx`(m, v[, f])	Approximation of the Jacobian transpose times a vector for the model provided.
`Jvec`(m, v[, f])	Compute the Jacobian times a vector for the model provided.
`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.
`fields`([m])	Return the computed geophysical fields for the model provided.
`make_synthetic_data`(m[, relative_error, ...])	Make synthetic data for the model and Gaussian noise provided.
`residual`(m, dobs[, f])	The data residual.

Notes

There are two ways in which the user can set the time_steps property for the forward simulation. The most basic approach is to use a (n_steps, ) numpy.ndarray that explicitly defines the step lengths in order. I.e.:

>>> sim.time_steps = np.r_[1e-6, 1e-6, 1e-6, 1e-5, 1e-5, 1e-4, 1e-4]

We can define also define the step lengths in compact for when the same step length is reused multiple times in succession. In this case, the time_steps property is set using a list of tuple. Each tuple contains the step length and number of times that step is repeated. The time stepping defined above can be set equivalently with:

>>> sim.time_steps = [(1e-6, 3), (1e-5, 2), (1e-4, 2)]

When set, the discretize.utils.unpack_widths() utility is used to convert the list of tuple to its (n_steps, ) numpy.ndarray representation.

Galleries and Tutorials using `simpeg.simulation.BaseTimeSimulation`#

Time-domain CSEM for a resistive cube in a deep marine setting

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Heagy et al., 2017 1D RESOLVE and SkyTEM Bookpurnong Inversions

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FLOW: Richards: 1D: Celia1990

3D Forward Simulation for Transient Response on a Cylindrical Mesh

3D Forward Simulation with User-Defined Waveforms

Forward Simulation Including Inductive Response

simpeg.simulation.BaseTimeSimulation#

Galleries and Tutorials using simpeg.simulation.BaseTimeSimulation#

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Galleries and Tutorials using `simpeg.simulation.BaseTimeSimulation`#