Automatic interpretative seismic image-focusing analysis
Abstract/Contents
- Abstract
- The focusing of a seismic image is directly linked to the accuracy of the migration velocity model. Therefore, a critical step in a seismic imaging workflow is to perform a focusing analysis on a seismic image to determine errors within the migration velocity model. While the residual curvature information provided within the offset/angle axis is commonly used to assess velocity errors, the focusing within the physical (i.e., midpoint) axes of a seismic image is often neglected. This is due to the highly interpretative nature of the focusing of geological features within seismic images, therefore making focusing analysis within the physical space difficult to automate. In this dissertation, I present an automatic data-driven approach that makes use of a convolutional neural network (CNN) equipped with high-dimensional convolutional operators to automate seismic image-focusing analysis. Training the CNN on focused and unfocused geological faults within synthetic and real prestack seismic images, I demonstrate this approach can make use of both spatial and offset/angle focusing information to robustly estimate velocity errors within seismic images. To test effectiveness of the approach, I apply it to an unfocused 2D limited-aperture image from the Gulf of Mexico and an unfocused 3D image from the Dutch Sector of the North Sea and show that it can robustly estimate velocity errors in the presence of limited illumination and coherent noise. For both applications, I show that performing a refocusing correction using the estimated velocity errors leads to an improved focusing in the unfocused images and consequently, improves the automatic detection of the faults within the images.
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 | Jennings, Joseph Stanley |
|---|---|
| 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 | Joseph Stanley Jennings. |
|---|---|
| Note | Submitted to the Department of Geophysics. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/bm714qb2760 |
Access conditions
- Copyright
- © 2022 by Joseph Stanley Jennings
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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