This thesis concerns the study of time reversal and plane-wave decomposition
in various geophysical applications. Time reversal is a key step in seismic
interferometry, reverse time migration and full waveform inversion. The plane-wave
transform, also known as the tau-p transform or slant-stack, can separate waves based
on their ray parameters or their emergence angles at the surface.
I propose a new approach to retrieve virtual full-wave seismic responses from
crosscorrelating recorded seismic data in the plane-wave domain. Unlike a traditional
approach where the correlogram is obtained from crosscorrelating recorded data,
which contains the full range of ray parameters, this method directly chooses
common ray parameters to cancel overlapping ray paths. Thus, it can sometime avoid
spurious arrivals when the acquisition requirement of seismic interferometry is not
strictly met. I demonstrate the method with synthetic examples and an ocean bottom
seismometer data example. I show a multi-scale application of plane-wave based full
waveform inversion (FWI) with the aid of frequency domain forward modeling.
FWI uses the two-way wave-equation to produce high-resolution velocity models for
seismic imaging. This technique is implemented by an adjoint-state approach, which
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involves a time-reversal propagation of the residual wavefield at receivers, similar to
seismic interferometry. With a plane-wave transformed gather, we can decompose the
data by ray parameters and iteratively update the velocity model with selected ray
parameters. This encoding approach can significantly reduce the number of shots and
receivers required in gradient and Hessian calculations. Borrowing the idea of
minimizing different data residual norms in FWI, I study the effect of different
scaling methods to the receiver wavefield in the reverse time migration. I show that
this type of scaling is able to significantly suppress outliers compared to conventional
algorithms. I also show that scaling by its absolute norm generally produces better
results than other approaches. I propose a robust stochastic time-lapse seismic
inversion strategy with an application of monitoring Cranfield CO2 injection site. This
workflow involves two steps. The first step is the baseline inversion using a hybrid
starting model that combines a fractal prior and the low-frequency prior from well log
data. The second step is to use a double-difference inversion scheme to focus on the
local areas where time-lapse changes have occurred. Synthetic data and field data
show the effectiveness of this method. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/20685 |
| Date | 09 July 2013 |
| Creators | Tao, Yi, active 2012 |
| Source Sets | University of Texas |
| Language | en_US |
| Detected Language | English |
| Format | application/pdf |
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