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Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data

  • Author / Creator
    Bonar, Christopher David
  • Local time-frequency analysis, also known as spectral decomposition, allows for a more detailed interpretation of time-series by providing the evolution of the frequency spectrum through time and has proven to be a useful seismic attribute for exercises such as reservoir characterization. This thesis explores posing the spectral decomposition problem through sparsity promoting inversion techniques to obtain a high resolution local time-frequency representation for a seismic trace. By requiring a high resolution local time-frequency representation for each individual seismic trace, increased noise variability is obtained between the local time-frequency representations of neighbouring seismic traces. To help attenuate this noise, information from nearby seismic traces can be incorporated during the inversion process for the spectral decomposition of an individual seismic trace. A similar strategy, called group sparsity, can also be incorporated for the simultaneous denoising of multicomponent seismic traces. A new method for the noise attenuation of seismic data is presented as well.

  • Subjects / Keywords
  • Graduation date
    2012-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R38H8J
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Physics
  • Specialization
    • Geophysics
  • Supervisor / co-supervisor and their department(s)
    • Sacchi, Mauricio (Physics)
  • Examining committee members and their departments
    • Schmitt, Doug (Physics)
    • Szepesvari, Csaba (Computing Science)
    • Gu, Jeff (Physics)