Attenuating random and coherent noise is an important part of seismic data processing. Successful removal results in an enhanced image of the subsurface geology, which facilitate economical decisions in hydrocarbon exploration. This motivates the search for new and more efficient techniques for noise removal. The main goal of this thesis is to present an overview of the Singular Spectrum Analysis (SSA) technique, studying its potential application to seismic data processing.
An overview of the application of SSA for time series analysis is presented. Subsequently, its applications for random and coherent noise attenuation, expansion to multiple dimensions, and for the recovery of unrecorded seismograms are described. To improve the performance of SSA, a faster implementation via a randomized singular value decomposition is proposed.
Results obtained in this work show that SSA is a versatile method for both random and coherent noise attenuation, as well as for the recovery of missing traces. / Geophysics
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/1429 |
| Date | 11 1900 |
| Creators | Oropeza, Vicente |
| Contributors | Sacchi, Mauricio (Physics), Kravchinsky, Vadim (Physics), Van Der Baan, Mirko (Physics), Vorobyov, Sergiy (Electrical and Computer Engineering) |
| 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 | Thesis |
| Format | 7151327 bytes, application/pdf |
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