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Nonlinear
Waveform Tomography:
Theory
and Application to Crosshole Seismic Data
Delaine Rebecca Thompson
Submitted
to the Department of Earth, Atmospheric, and Planetary Sciences on May 4,
1993 in partial fulfillment of the requirements for the degree of Doctor of
Philosophy
Abstract
The acoustic inverse problem of crosshole seismology is nonlinear in the medium
velocities and ill-posed because of the lack of complete data coverage surrounding
the area of interest. In light of these facts, this thesis develops a new
nonlinear waveform technique which inverts crosshole seismic data for acoustic
velocities in the shallow subsurface.
We first describe the methods we use to treat the nonlinearity and nonuniquesness
of the inverse problem. Tikhonov regularization methods are used to define
a minimization condition which balances the normal misfit between observed
and modeled data with a normed measure of the spatial roughness of the model.
This type of regularization produces minimum structure velocity models which
can vary in their degree of smoothness versus fit to the data. The measure
of spatial roughness we use is a two-dimensional second derivative operator
applied to the area of velocity reconstruction; the degree of smoothness in
the solution is controlled by a regularization parameter set prior to the
inversion. We numerically solve the Tikhonov minimization condition using
a conjugate algorithm; this algorithm require a gradient direction which we
derive directly from the acoustic wave equation.
To accurately calculate the components of the minimization condition and its
gradient, we use frequency-domain moment method with sinc basis functions.
The transformation of the acoustic wave equation into the frequency domain
reduces the necessary forward modeling computations; his reduction is possible
because of the spatial wavenumber redundancy in crosshole data. The moment
method does not use the Born approximation or high frequency ray asymptotics
to simplify the forward modeling. A two-dimensional area between the source
and receiver boreholes is sampled with a grid of basis functions and a point-matching
procedure discretizes the integral form of the acoustic wave equation. This
discretization produces a two part matrix problem which we solve for the Green’s
functions and total fields in the medium using general matrix decomposition
techniques. The combination of the Tikhonov regularization technique and the
frequency-domain moment method results in a inversion technique we call nonlinear
waveform tomography.
We test the validity of nonlinear waveform tomography on two synthetic data
sets. We show the superiority of nonlinear waveform tomography over conventional
linear diffraction tomography by inverting the analytical data from a disk
for a variety of experimental and model parameters. Then we invert acoustic
finite difference data produced through a laterally varying medium to demonstrate
the flexibility of nonlinear waveform tomography in a more geophysical realistic
setting. These inversions produce guidelines for the usage of the method in
more complex and potentially noisy situations.
Finally, we successfully apply nonlinear waveform tomography to two scale
model data sets obtained from an ultrasonic modeling tank. The first data
set comes from a mostly plane-layered epoxy-resin model. The data exhibit
elastic effects and other complicated wave phenomena. We invert this data
set for the lateral variations in the model using a smoothed one-dimensional
starting model. Then we perturb some inversion parameters to determine the
robustness of nonlinear waveform tomography. The second scale model data set
is from a rubber half cylinder model submerged in water. The limited coverage
of the experiment and the strong velocity contrast between rubber and water
tax the ability of nonlinear waveform tomography to reconstruct the correct
velocity model. In this case, a good initial model is required for accurate
velocity reconstruction, but we are able to locate and describe the shape
of the rubber half cylinder using only a zero-perturbation initial model.