SVD (Dongarra, et al., 1979) was performed on the matrix after parametrizing the model as described by equations (3) , (4) and (5). Pratt and Chapman (1990) suggest a clear way of representing the resultant singular values and singular vectors in model space () of the matrices involved in tomographic problems. I will represent my results in a similar fashion including also in the representation the corresponding singular vectors in data space (). This will help to understand how the data and the model are resolved by using iterative techniques such as conjugate gradients.

The data space (spanned by the columns of ) is shown in Figure . It is a 2-D space of traveltimes where the horizontal axis contains the receiver depths and the vertical axis contains the source depths. If the origin of both axes is the same, the closer a given point is to the main diagonal (near offset), the closer is to zero the corresponding ray angle.

Figure 1

The geometry used to do the SVD for the different parametrizations is shown in Figure (Pratt and Chapman, 1990): five sources and five receivers in a constant slowness medium. When the model is isotropic, the matrix only depends on the ray geometry and when the model is anisotropic, depends on both the ray geometry and the slowness model (constant in this case).

Figure 2

Figure shows the SVD when the model is discretized using 6 horizontal isotropic layers (equation 3). Notice that the differences among the singular values are small. The largest singular values correspond to singular vectors in data and model space whose components are roughly of the same magnitude. We observe that with this parametrization only some ``big structures'' (averages) in data space can be explained whereas in model space all the parameters can be resolved well. By representing the data space as described by Figure 1, it is possible to identify which traveltimes belong to the null space and therefore, cannot be resolved. Errors in these particular traveltimes (noise) will also have little or no effect in the solution.

Figure 3

When lateral variations are allowed in the previous parameterization results a matrix whose SVD is shown in Figure . The largest singular value corresponds roughly to horizontal layers (in model space) and non-horizontal rays (in data space). As the singular values decrease, the eigenvectors in model space tend to contain more horizontal variations and the eigenvectors in data space tend to expand near and far offsets alternatively (diagonal and non-diagonal structures). The null space of the problem corresponds to ``pure'' horizontal variations in model space and high frequency variations data space (rapid changes among nearby traveltimes). These vectors in the null space of the data are probably not a problem in real applications because if the real velocities do not change rapidly, high frequency variations are unlikely to be found in the measured traveltimes. The presence of noise introduces high frequency variations in the data. Unfortunately, these type of variations are not confined to the null space of the data but are present in singular vectors corresponding to larger singular values. Therefore, in some applications it might be necessary to damp the effect of singular values larger than those contained in the null space in order to attenuate the effect of certain components of the noise.

Figure 4

Figure shows the SVD of the matrix that result after discretizing the model and computing the data using natural pixels (equation 4). We notice immediately that the matrix for the natural pixels is much better conditioned than the one obtained with square pixels (Figure ). The singular vectors in data space that correspond to the largest singular value are almost identical for both discretizations. However, the ways those data are distributed in the model are different for the different discretizations. For all the eigenvectors in Figure there is a clear correspondency between the structures represented in data and model space, unlike the eigenvectors in Figure . With the natural pixels all the parameters and data can be resolved because the discretization does not introduce another null space in the problem (different to the null space of the measurements), whereas the discretization of the model in square pixels does.

Figure 5

The SVD for a 1-D anisotropic parametrization (equation 5) is shown
in Figure .
The
upper half of each eigenvector in model space
corresponds to *S*_{x} and the lower half corresponds to *S*_{z}.
We observe that the largest singular values correspond to
*S*_{x} in model space
and in data space the behavior is similar to the
isotropic 1-D case. The null space of the model is spanned
by vectors that only contain information about *S*_{z}.

Figure 6

Figure 7

Figure 8

Figure shows the SVD
when the model is 2-D anisotropic. The result is nearly
a combination of the previous ones in model space,
namely, vertical variations in *S*_{x} correspond to the
largest singular values and horizontal variations in *S*_{z}
to the smallest ones. The null space of the model (Figure )
is formed
almost entirely by ``unit'' vectors that describe *S*_{z}.
For this reason,
in the synthetic and real data
applications shown in Michelena and Muir (1991), the resolution
of the vertical component of the slowness is poor when compared with
the resolution
of the horizontal component. As expected, *S*_{z} is poorly defined by the
cross-well recording
geometry.
Note that by increasing the number of model parameters the condition number
of the matrix diminishes when compared with the one of
the matrix for the isotropic case
(Figure 4).

12/18/1997