This thesis proposes a nonlinear differential semblance approach to full waveform inversion as an alternative to standard least squares inversion,
which cannot guarantee a reliable solution, because of the existence of many spurious local minima of the objective function for typical data that
lacks low-frequency energy. Nonlinear differential semblance optimization combines the ability of full waveform inversion to account for nonlinear
physical effects, such as multiple reflections, with the tendency of differential semblance migration velocity analysis to avoid local minima.
It borrows the gather-flattening concept from migration velocity analysis, and updates the velocity by flattening primaries-only gathers obtained
via nonlinear inversion. I describe a general formulation of this algorithm, its main components and implementation. Numerical experiments show for
simple layered models, standard least squares inversion fails, whereas nonlinear differential semblance succeeds in constructing a kinematically
correct model and fitting the data rather precisely.
| Identifer | oai:union.ndltd.org:RICE/oai:scholarship.rice.edu:1911/71694 |
| Date | 24 July 2013 |
| Creators | Sun, Dong |
| Contributors | Symes, William W. |
| Source Sets | Rice University |
| Language | English |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
Page generated in 0.0223 seconds