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...

Full description

Bibliographic Details

Main Author:	Li, Ji
Format:	Thesis
Language:	English
Published:	Science 2026
Subjects:	machine learning unsupervised learning self-supervised learning signal processing Geophysics
Online Access:	https://hdl.handle.net/1880/123558
Tags:	Add Tag No Tags, Be the first to tag this record!