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# Straigh-ray 2D tomography with SrTomo (fatiando.seismic.srtomo)¶

Straight-ray 2D travel-time tomography (i.e., does not consider reflection or refraction)

Solver

Functions

class fatiando.seismic.srtomo.SRTomo(ttimes, srcs, recs, mesh)[source]

2D travel-time straight-ray tomography.

Use the fit method to run the tomography and produce a velocity estimate. The estimate is stored in the estimate_ attribute.

Generaly requires regularization, like Damping or Smoothness2D.

Parameters:

• ttimes
: array

Array with the travel-times of the straight seismic rays.

• srcs
: list of lists

List of the [x, y] positions of the sources.

• recs
: list of lists

List of the [x, y] positions of the receivers.

• mesh
: SquareMesh or compatible

The mesh where the inversion (tomography) will take place.

The ith travel-time is the time between the ith element in srcs and the ith element in recs.

config(method, **kwargs)

Configure the optimization method and its parameters.

This sets the method used by fit and the keyword arguments that are passed to it.

Parameters:

• method
: string

The optimization method. One of: 'linear', 'newton', 'levmarq', 'steepest', 'acor'

Other keyword arguments that can be passed are the ones allowed by each method.

Some methods have required arguments:

• newton, levmarq and steepest require the initial argument (an initial estimate for the gradient descent)
• acor requires the bounds argument (min/max values for the search space)

copy(deep=False)

Make a copy of me together with all the cached methods.

estimate_

A nicely formatted version of the estimate.

If the class implements a fmt_estimate method, this will its results. This can be used to convert the parameter vector to a more useful form, like a fatiando.mesher object.

fit()

Solve for the parameter vector that minimizes this objective function.

Uses the optimization method and parameters defined using the config method.

The estimated parameter vector can be accessed through the p_ attribute. A (possibly) formatted version (converted to a more manageable type) of the estimate can be accessed through the property estimate_.

fmt_estimate(p)[source]

Convert the estimated slowness to velocity.

gradient(p)

The gradient vector of the misfit function.

$\bar{g} = -2\bar{\bar{J}}^T\bar{r}$

where $$\bar{\bar{J}}$$ is the Jacobian matrix and $$\bar{r}$$ is the residual vector.

Parameters:

• p
: 1d-array

The parameter vector where the gradient is evaluated

Returns:

: 1d-array

hessian(p)

The Hessian of the misfit function with respect to the parameters.

Calculated using the Gauss approximation:

$\bar{\bar{H}} \approx 2\bar{\bar{J}}^T\bar{\bar{J}}$

where $$\bar{\bar{J}}$$ is the Jacobian matrix.

For linear problems, the Hessian matrix is cached in memory, so calling this method again will not trigger a re-calculation.

Parameters:

• p
: 1d-array

The parameter vector where the Hessian is evaluated

Returns:

• hessian
: 2d-array

The Hessian matrix

jacobian(p)[source]

Build the Jacobian (sensitivity) matrix.

The matrix will contain the length of the path takes by the ray inside each cell of the mesh.

Parameters:

• p
: 1d-array

An estimate of the parameter vector or None.

Returns:

• jac
: 2d-array (sparse CSR matrix from scipy.sparse)

The Jacobian

predicted(p)[source]

Calculate the travel time data predicted by a parameter vector.

Parameters:

• p
: 1d-array

An estimate of the parameter vector

Returns:

• pred
: 1d-array

The predicted travel time data.

regul_param

The regularization parameter (scale factor) for the objetive function.

Defaults to 1.

residuals(p=None)

Calculate the residuals vector (observed - predicted data).

Parameters:

• p
: 1d-array or None

The parameter vector used to calculate the residuals. If None, will use the current estimate stored in estimate_.

Returns:

• residuals
: 1d-array or list of 1d-arrays

The residual vector. If this is the sum of 1 or more Misfit instances, will return the residual vector from each of the summed misfits in the order of the sum.

set_weights(weights)

Set the data weights.

Using weights for the data, the least-squares data-misfit function becomes:

$\phi = \bar{r}^T \bar{\bar{W}}\bar{r}$

Parameters:

• weights
: 1d-array or 2d-array or None

Weights for the data vector. If None, will remove any weights that have been set before. If it is a 2d-array, it will be interpreted as the weight matrix $$\bar{\bar{W}}$$. If it is a 1d-array, it will be interpreted as the diagonal of the weight matrix (all off-diagonal elements will default to zero). The weight matrix can be a sparse array from scipy.sparse.

value(p)

Calculate the value of the misfit for a given parameter vector.

The value is given by:

$\phi(\bar{p}) = \bar{r}^T\bar{\bar{W}}\bar{r}$

where $$\bar{r}$$ is the residual vector and $$bar{\bar{W}}$$ are optional data weights.

Parameters:

• p
: 1d-array or None

The parameter vector.

Returns:

• value
: float

The value of the misfit function.

fatiando.seismic.srtomo.slowness2vel(slowness, tol=1e-08)[source]

Safely convert slowness to velocity.

Almost 0 slowness is mapped to 0 velocity.

Parameters:

• slowness
: array

The slowness values

• tol
: float

Slowness < tol will be set to 0 velocity

Returns:

• velocity
: array

The converted velocities

Examples:

>>> import numpy as np
>>> slow = np.array([1, 2, 0.000001, 4])
>>> slowness2vel(slow, tol=0.00001)
array([ 1.  ,  0.5 ,  0.  ,  0.25])