In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation
for three problems related to seismic noise, namely the ’good’, corresponding
to noise generated by random sampling; the ’bad’, corresponding to coherent noise
for which (inaccurate) predictions exist and the ’ugly’ for which no predictions
exist. We will show that the compressive capabilities of curvelets on seismic data
and images can be used to tackle these three categories of noise-related problems.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:BVAU.2429/554 |
| Date | January 2007 |
| Creators | Herrmann, Felix J., Wilkinson, Dave |
| Publisher | Society of Exploration Geophysicists |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | text |
| Rights | Herrmann, Felix J. |
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