Seismic velocity model building with deep convolutional neural networks
Abstract/Contents
- Abstract
- This thesis focuses on recovering wave velocity estimates from seismic data with limited recording offsets and band-limited frequency content using Deep Learning (DL), specifically using deep convolutional neural networks (CNN). A combined strategy combines a supervised learning framework to recover the low-wavenumber components of velocity models with the traditional Full Waveform Inversion (FWI) to recover high-wavenumbers. We demonstrate the methodology on synthetic industry benchmark problems and a field data application with an open-source Gulf of Mexico seismic dataset.
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 | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Farris, Stuart |
|---|---|
| Degree supervisor | Biondi, Biondo |
| Thesis advisor | Biondi, Biondo |
| Thesis advisor | Araya,Mauricio |
| Thesis advisor | Beroza, Greg |
| Thesis advisor | Schroeder, Dustin |
| Degree committee member | Araya,Mauricio |
| Degree committee member | Beroza, Greg |
| Degree committee member | Schroeder, Dustin |
| Associated with | Stanford Doerr School of Sustainability |
| Associated with | Stanford University, Department of Geophysics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Stuart Farris. |
|---|---|
| Note | Submitted to the Department of Geophysics. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/hv925cz6378 |
Access conditions
- Copyright
- © 2023 by Stuart Farris
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