Full waveform inversion by model extension : a robust method to estimate the seismic propagation velocity in the subsurface from recorded seismograms
Abstract/Contents
- Abstract
- Seismic imaging is an effective method to produce accurate maps of the Earth's subsurface, and has been employed for decades in global seismology, hydrocarbon exploration, geothermal energy production, and more recently CO2 sequestration and monitoring. In complex geological settings, the quality of such maps highly depends on having a reliable seismic velocity model, which can be difficult to obtain. In this thesis, I develop a novel method, namely full waveform inversion by model extension (FWIME), designed to produce accurate acoustic velocity models of the subsurface from seismic recordings when conventional methods fail. I leverage the robust convergence properties of wave-equation migration velocity analysis (WEMVA) with the accuracy and high-resolution nature of acoustic full waveform inversion (FWI) by combining these techniques into a compact, mathematically consistent, and user-friendly workflow. By doing so, I mitigate the need for accurate initial models and the presence of coherent long-offset and/or low-frequency energy within the recorded data, which are difficult and costly to acquire but often necessary for conventional methods to succeed. The novelty of my method resides in the design of a custom loss function and the optimization strategy I develop to pair WEMVA with FWI, which is more efficient and powerful than applying each method separately or sequentially. I illustrate the potential of my proposed method by accurately inverting datasets generated by realistic 2D benchmark models which simulate complex and challenging geological scenarios encountered in field applications. In each scenario, the dataset lacks low-frequency energy and the initial velocity model is inaccurate, which prevents conventional methods from recovering useful solutions. In addition, I develop an efficient 3D numerical implementation of FWIME with the use of general-purpose graphics processing units (GPU) to handle 3D field datasets containing tens of terabytes of information, and to recover billions of unknown parameters. I successfully apply FWIME to a 3D ocean-bottom-node dataset acquired by Shell in the Gulf of Mexico. I show that my method outperforms conventional FWI and manages to improve the velocity model and the resulting subsurface image quality.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Barnier, Guillaume Camille Michel |
|---|---|
| Degree supervisor | Biondi, Biondo, 1959- |
| Thesis advisor | Biondi, Biondo, 1959- |
| Thesis advisor | Clapp, Robert G. (Robert Graham) |
| Thesis advisor | Dunham, Eric |
| Degree committee member | Clapp, Robert G. (Robert Graham) |
| Degree committee member | Dunham, Eric |
| Associated with | Stanford University, Department of Geophysics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Guillaume Barnier. |
|---|---|
| Note | Submitted to the Department of Geophysics. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/td173jf2299 |
Access conditions
- Copyright
- © 2022 by Guillaume Camille Michel Barnier
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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