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

  • Robust sparse reconstruction
  • Greedy Pursuit
  • Simultaneous source separation

• 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.