# Simulation#

class emg3d.simulations.Simulation(survey, model, max_workers=4, gridding='single', **kwargs)[source]#

Bases: object

Create a simulation for a given survey on a given model.

A simulation can be used to compute responses for an entire survey, hence for an arbitrary amount of sources, receivers, and frequencies. The responses are computed in parallel over sources and frequencies. It can also be used to compute the misfit with the data and to compute the gradient of the misfit function.

The computational grid(s) can either be provided, or automatic gridding can be used; see the description of the parameters gridding and gridding_opts for more details.

Note

The automatic gridding does its best to generate meshes that are suitable for the provided model and survey. However, CSEM spans a wide range of acquisition layouts, and both frequencies and conductivities or resistivities span many orders of magnitude. This makes it hard to have a function that fits all purposes. Check the meshes with your expert knowledge. Also, the automatic gridding is conservative in its estimate, trying to be on the save side (correct results over speed). This means, however, that often smaller grids could be used by providing the appropriate options in gridding_opts or directly providing your own computational grids.

Note

The package xarray has to be installed in order to use Simulation: pip install xarray or conda install -c conda-forge xarray.

Parameters
surveySurvey

The survey; a emg3d.surveys.Survey instance. The survey contains sources, receivers, frequencies, and optionally data.

The survey-data will be modified in place. Provide survey.copy() if you want to avoid this.

modelModel

The model; a emg3d.models.Model instance.

max_workersint, default: 4

The maximum number of processes that can be used to execute the given calls.

griddingstr, default: ‘single’

Method to create the computational grids.

The different methods are:

• 'same': Same grid as for the input model.

• 'single': A single grid for all sources and frequencies.

• 'frequency': Frequency-dependent grids.

• 'source': Source-dependent grids.

• 'both': Frequency- and source-dependent grids.

• 'input': Same as 'single', but the grid has to be provided in gridding_opts instead of being automatically created.

• 'dict': Same as 'both', but the grids have to be provided in gridding_opts in the form of dict[source][frequency] instead of being automatically created.

See the parameter gridding_opts for more details.

If gridding is 'same' or 'input', the input grid is checked if it is a sensible grid for emg3d; if not, it throws a warning. In the other cases the grids are created by emg3d itself, they will be fine. (If 'dict' we assume the user knows how to provide good grids.)

gridding_opts{dict, TensorMesh}, default: {}

Input format depends on gridding:

• 'same': Nothing, gridding_opts is not permitted.

• 'single', 'frequency', 'source', 'both': Described below.

• 'input': A emg3d.meshes.TensorMesh.

• 'dict': Dictionary of the format dict[source][frequency] containing a emg3d.meshes.TensorMesh for each source-frequency pair.

The dict in the case of 'single', 'frequency', 'source', 'both’ is passed to emg3d.meshes.construct_mesh; consult the corresponding documentation for more information. Parameters that are not provided are estimated from the provided model, grid, and survey using emg3d.meshes.estimate_gridding_opts, which documentation contains more information too.

There are two notably differences to the parameters described in emg3d.meshes.construct_mesh:

• vector: besides the normal possibility it can also be a string containing one or several of 'x', 'y', and 'z'. In these cases the corresponding dimension of the input mesh is provided as vector. See emg3d.meshes.estimate_gridding_opts.

• expand: in the format of [property_sea, property_air]; if provided, the input model is expanded up to the seasurface with sea water, and an air layer is added. The actual height of the seasurface can be defined with the key seasurface. See emg3d.models.expand_grid_model. NOTE: expand is deprecated in v1.7.0, and will be removed in v1.9.0. A property-complete model has to be provided.

solver_optsdict, default: {‘verb’: 1’}

Passed through to emg3d.solver.solve. The dict can contain any parameter that is accepted by the emg3d.solver.solve except for model, sfield, efield, return_info, and log.

verbint, default: 0

Level of verbosity. Possible options:

• -1: Errors.

• 0: Warnings.

• 1: Info.

namestr, default: None

Name of the simulation.

infostr, default: None

Simulation info or any other info (e.g., what was the purpose of this simulation).

Interpolation method to obtain the response at receiver location; ‘cubic’ or ‘linear’. Cubic is more precise. However, if you are interested in the gradient, you need to choose ‘linear’ at the moment, as there are only linearly interpolated source functions. To be the proper adjoint for the gradient the receiver has to be interpolated linearly too.

file_dirstr, default: None

Absolute or relative path, where temporary files should be stored. By default, everything is done in memory. However, for large models with many sources and frequencies this can become memory consuming. Providing a file_dir can help with this. Fields and models are then stored to disk, and each process accesses the files it needs. There is only a gain if there are more source-frequency pairs than concurrent running processes.

Note that the directory is created if it does not exist. However, the parent directory must exist.

Also note that the files are stored as .h5-files, and you need to have h5py installed to use this feature.

tqdm_opts{bool, dict}, default: True

Boolean if a progress bar should be shown (only if tqdm is installed). If a dict is provided it will be forwarded to tqdm.

layeredbool, default: False

If True, the responses are computed with approximated layered (1D) models for each source-receiver pair using empymod.

The computation happens in parallel for each source location. Each source-receiver pair is done separately, but all frequencies at once.

If layered is set, the gradient is computed using the finite-difference method, by perturbing each layer slightly.

Current limitations:

• Only point and dipole sources.

• Only isotropic and VTI models.

Setting this to True also means:

• There are no {e;h}fields, only the fields at receiver locations.

• gridding, most of gridding_opts, solver_opts, receiver_interpolation, and file_dir have no effect.

• The attribute emg3d.simulations.Simulation.jvec is not implemented.

layered_optsdict, default: {}

Options passed to emg3d.models.Model.extract_1d, defining how the layered model is obtained from the 3D model. p0 and p1 are taken to be the source and receiver locations. Consult that function for more information. Below are only things described which differ.

The possible methods are: cylinder, prism, midpoint, source, receiver. The last two are the same as midpoint, where just both points are set either to the source or receiver location, respectively.

The default method is 'cylinder'. If the ellipse parameters are not given for the methods cylinder and prism, they are set as follows:

• factor: 1.2.

• minor: 0.8.

• radius: one skin depth using the lowest frequency of the survey and the downwards property from the gridding options (or the minimum conductivity value in the lowest vertical layer).

Attributes Summary

 data Shortcut to survey.data. gradient Compute the discrete gradient using the adjoint-state method. layered If True, use layered computations (empymod). misfit Misfit or cost function.

Methods Summary

 clean([what]) Clean part of the data base. compute([observed]) Compute efields asynchronously for all sources and frequencies. copy([what]) Return a copy of the Simulation. from_dict(inp) Convert dict into emg3d.simulations.Simulation instance. from_file(fname[, name]) Load Simulation from a file. get_efield(source, frequency) Return electric field for given source and frequency. get_efield_info(source, frequency) Return the solver information of the corresponding computation. get_grid(source, frequency) Return computational grid of the given source and frequency. get_hfield(source, frequency) Return magnetic field for given source and frequency. get_model(source, frequency) Return model on the grid of the given source and frequency. jtvec(vector) Compute the sensitivity transpose times a vector. jvec(vector) Compute the sensitivity times a vector. print_grid_info([verb, return_info]) Print info for all generated grids. print_solver_info([field, verb, return_info]) Print solver info. to_dict([what, copy]) Store the necessary information of the Simulation in a dict. to_file(fname[, what, name]) Store Simulation to a file.

Attributes Documentation

data#

Shortcut to survey.data.

The discrete adjoint-state gradient for a single source at a single frequency is given by Equation (10) in [PlMu08],

$\begin{split}\nabla_p \phi(\textbf{p}) = -&\sum_{k,l,m}\mathbf{\bar{\lambda}}_{x; k+\frac{1}{2}, l, m} \frac{\partial S_{k+\frac{1}{2}, l, m}}{\partial \textbf{p}} \textbf{E}_{x; k+\frac{1}{2}, l, m}\\ -&\sum_{k,l,m}\mathbf{\bar{\lambda}}_{y; k, l+\frac{1}{2}, m} \frac{\partial S_{k, l+\frac{1}{2}, m}}{\partial \textbf{p}} \textbf{E}_{y; k, l+\frac{1}{2}, m}\\ -&\sum_{k,l,m}\mathbf{\bar{\lambda}}_{z; k, l, m+\frac{1}{2}} \frac{\partial S_{k, l, m+\frac{1}{2}}}{\partial \textbf{p}} \textbf{E}_{z; k, l, m+\frac{1}{2}}\, ,\end{split}$

where $$\textbf{E}$$ is the electric (forward) field and $$\mathbf{\lambda}$$ is the back-propagated residual field (from electric and magnetic receivers); $$\bar{~}$$ denotes conjugate. The $$\partial S$$-part takes care of the volume-averaged model parameters.

Warning

To obtain the proper adjoint-state gradient you have to choose linear interpolation for the receiver responses: emg3d.Simulation(..., receiver_interpolation='linear'). The reason is that the point-source is the adjoint of a tri-linear interpolation, so the residual should come from a linear interpolation.

Note

The currently implemented gradient does only work for models without relative electric permittivity nor relative magnetic permeability.

Note

If layered=True, the gradient is computed using finite differences.

Returns

• isotropic: (nx, ny, nz)

• HTI/VTI: (2, nx, ny, nz)

• triaxial: (3, nx, ny, nz)

layered#

If True, use layered computations (empymod).

misfit#

Misfit or cost function.

The data misfit or weighted least-squares functional using an $$l_2$$ norm is given by

(37)#$\phi = \frac{1}{2} \sum_s\sum_r\sum_f \left\lVert W_{s,r,f} \left( \textbf{d}_{s,r,f}^\text{pred} -\textbf{d}_{s,r,f}^\text{obs} \right) \right\rVert^2 \, ,$

where $$s, r, f$$ stand for source, receiver, and frequency, respectively; $$\textbf{d}^\text{obs}$$ are the observed electric and magnetic data, and $$\textbf{d}^\text{pred}$$ are the synthetic electric and magnetic data. As of now the misfit does not include any regularization term.

The data weight of observation $$d_i$$ is given by $$W_i = \varsigma^{-1}_i$$, where $$\varsigma_i$$ is the standard deviation of the observation, see emg3d.surveys.Survey.standard_deviation.

Note

You can easily implement your own misfit function (to include, e.g., a regularization term) by monkey patching this misfit function with your own:

@property  # misfit is a property
def my_misfit_function(self):
'''Returns the misfit as a float.'''

if self._misfit is None:
self.compute()  # Ensures fields are computed.

return self._misfit

# Monkey patch simulation.misfit:
emg3d.simulation.Simulation.misfit = my_misfit_function

# And now all the regular stuff, initiate a Simulation etc
simulation = emg3d.Simulation(survey, grid, model)
simulation.misfit
# => will return your misfit

Returns
misfitfloat

Value of the misfit function.

Methods Documentation

clean(what='computed')[source]#

Clean part of the data base.

Parameters
whatstr, default: ‘computed’

What to clean. Possibilities:

• 'computed': Removes all computed properties: electric and magnetic fields and responses at receiver locations.

• 'keepresults': Removes everything except for the responses at receiver locations.

• 'all': Removes everything (leaves it plain as initiated).

compute(observed=False, **kwargs)[source]#

Compute efields asynchronously for all sources and frequencies.

Parameters
observedbool, default: False

If True, it stores the current synthetic responses also as observed responses.

Boolean if to add noise to observed data (if observed=True). All remaining kwargs are forwarded to emg3d.surveys.Survey.add_noise.

copy(what='computed')[source]#

Return a copy of the Simulation.

See to_file for more information regarding what.

classmethod from_dict(inp)[source]#

Convert dict into emg3d.simulations.Simulation instance.

Parameters
inpdict

Dictionary as obtained from emg3d.simulations.Simulation.to_dict.

Returns
simulationSimulation

A emg3d.simulations.Simulation instance.

classmethod from_file(fname, name='simulation', **kwargs)[source]#

Parameters
fnamestr

Absolute or relative file name including extension.

namestr, default: ‘simulation’

Name under which the simulation is stored within the file.

kwargsKeyword arguments, optional

Passed through to io.load.

Returns
simulationSimulation

A emg3d.simulations.Simulation instance.

infostr, returned if verb<0

Info-string.

get_efield(source, frequency)[source]#

Return electric field for given source and frequency.

get_efield_info(source, frequency)[source]#

Return the solver information of the corresponding computation.

get_grid(source, frequency)[source]#

Return computational grid of the given source and frequency.

get_hfield(source, frequency)[source]#

Return magnetic field for given source and frequency.

get_model(source, frequency)[source]#

Return model on the grid of the given source and frequency.

jtvec(vector)[source]#

Compute the sensitivity transpose times a vector.

If vector=residual*weights, jtvec corresponds to the gradient.

(38)#$J^H v = G^H A^{-H} P^H v \ ,$

where $$v$$ has the shape of the data.

Parameters
vectorndarray, DataArray

An array with the shape of the data, or directly a DataArray as stored in the emg3d.surveys.Survey.

Returns
jtvecndarray

Adjoint-state gradient for the provided vector. Shape depends on the anisotropy type:

• isotropic: (nx, ny, nz)

• HTI/VTI: (2, nx, ny, nz)

• triaxial: (3, nx, ny, nz)

jvec(vector)[source]#

Compute the sensitivity times a vector.

(39)#$J v = P A^{-1} G v \ ,$

where $$v$$ has size of the model.

Parameters
vectorndarray

Vector applied to J. Shape depends on the anisotropy type:

• isotropic: (nx, ny, nz) or (1, nx, ny, nz)

• HTI/VTI: (2, nx, ny, nz)

• triaxial: (3, nx, ny, nz)

Returns
jvecndarray

Shape of the data.

print_grid_info(verb=1, return_info=False)[source]#

Print info for all generated grids.

print_solver_info(field='efield', verb=1, return_info=False)[source]#

Print solver info.

to_dict(what='computed', copy=False)[source]#

Store the necessary information of the Simulation in a dict.

to_file(fname, what='computed', name='simulation', **kwargs)[source]#

Store Simulation to a file.

Parameters
fnamestr

Absolute or relative file name including ending, which defines the used data format. See emg3d.io.save for the options.

whatstr, default: ‘computed’

What to store. Possibilities:

• 'computed', 'all': Stores all computed properties: electric fields and responses at receiver locations.

• results': Stores only the response at receiver locations.

• 'plain': Only stores the plain Simulation (as initiated).

Note that if file_dir is set, those files will remain there.

namestr, default: ‘simulation’

Name with which the simulation is stored in the file.

kwargsKeyword arguments, optional

Passed through to emg3d.io.save`.