Unsupervised and Self-Supervised Deep Learning for Seismic Data Processing: Methods and Applications
Seismic data processing is vital for subsurface imaging but is hindered by incoherent, coherent, and partially coherent noise as well as incomplete or irregular sampling. Classical model-based approaches (e.g., f-x filtering, Radon transform, curvelet thresholding) depend on strong priors and often...
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| Format: | Thesis |
| Language: | English |
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Science
2026
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| Online Access: | https://hdl.handle.net/1880/123558 |
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