Imaging with multiples by least-squares reverse time migration
Abstract/Contents
- Abstract
- This dissertation presents a novel technique for using surface-related multiples to improve imaging in geologically complex areas. It overcomes a challenge of the migration-based approach where crosstalk artifacts appear in the image. In the case of reverse-time migration, these imaging artifacts are the result of cross-correlation between wrong pairs of incident and scattered wavefields. Joint least-squares reverse-time migration, also known as linearized inversion, can coherently focus the reflection energy of primary and surface-related multiples into one image. By posing the imaging problem as an inversion problem, spurious reflectors or noises in the image can be attenuated. In geologically complex areas that contain salt structures, the proposed method not only improves the imaging but also added additional angular coverage in poorly illuminated areas. With respect to improving subsurface illumination, it is particularly advantageous to apply this method to ocean-bottom node acquisition. A modified modeling operator in the inversion process was introduced to model the surface-related multiples in the data. This modified modeling operator uses the data as an areal source, which removes the need to estimate the source wavelet. By using the data as a source, only the last down-going and up-going legs of the wavepath have dependency on the migration velocity model. As a result, this operator has the same sensitivity to the migration velocity model as the conventional primary modeling operator. From an imaging prospective, the robustness of the technique is improved when there are deviations between the migration velocity and the true velocity. The surface-related multiple operator can be combined with the conventional primary operator to form a joint operator that properly accounts for the physics of both primary and surface-related multiple reflections. Several methods are introduced to improve the convergence of least-squares reverse-time migration, particularly for areas with a complex salt structure. To emphasize the shadow zones in the image, a target-oriented data-reweighting scheme is incorporated in the inversion process. To extract the most information from the least-squares reverse-time migration algorithm, salt dimming data-weighting was introduced to down-weight the reflection energy coming from strong velocity contrasts in the migration velocity model. Such energy often dominates the inversion. When least-squares migration is extended to the angle domain, prestack extended-angle domain filtering can be incorporated into the modeling operator to remove unwanted noise in the image. Applications to synthetic and field ocean-bottom node datasets show that, compared to migration or single-mode inversion, joint least-squares reverse-time migration provides the best overall subsurface image and angular coverage. In particular, areas near and underneath a complex salt structure are better illuminated when surface-related multiples are used as signal.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2014 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Wong, Man Chu (Mandy) |
|---|---|
| Associated with | Ronen, Shuki |
| Associated with | Stanford University, Department of Geophysics. |
| Primary advisor | Biondi, Biondo, 1959- |
| Thesis advisor | Biondi, Biondo, 1959- |
| Thesis advisor | Claerbout, Jon F |
| Thesis advisor | Clapp, Robert G. (Robert Graham) |
| Thesis advisor | Lawrence, Jesse |
| Advisor | Claerbout, Jon F |
| Advisor | Clapp, Robert G. (Robert Graham) |
| Advisor | Lawrence, Jesse |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Man Chu (Mandy) Wong. |
|---|---|
| Note | Submitted to the Department of Geophysics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2014. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by Man Chu Wong
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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