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Robust Sparse Recovery via Matching Pursuit Algorithms and Applications to Simultaneous-Source Seismic Data Processing

  • Author / Creator
    Li, Ji
  • Compressive sensing and sparse reconstruction techniques are adopted to solve many seismic data processing problems, including the design of high-resolution transforms for coherent noise removal, signal separation, and seismic wave eld reconstruction. Traditional sparse reconstruction algorithms optimally work with noise-free data or data contaminated with random noise of Gaussian distribution. Their performance degrades in the presence of erratic noise. In this thesis, erratic noise refers to noise characterized by large and isolated amplitudes. The thesis proposes robust sparse reconstruction algorithms that are resistant to erratic noise. These algorithms are adopted to solve the simultaneous-source separation problem via robust sparse Radon transforms.

  • Subjects / Keywords
  • Graduation date
    Spring 2021
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-83j1-rd14
  • 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.