Ground roll attenuation with least-squares and robust inversion

• Author / Creator

  Papathanasaki, Iliana

• This thesis proposes a framework for ground roll removal based on regularized inversion.
  Both ground roll and reflections are represented in the frequency-space (f - x) domain as
  a linear function of unknown complex amplitudes. An inversion algorithm is developed to estimate the coefficients that model the ground roll and reflections separately. The latter
  allows for an independent synthesis of ground roll and reflections. The synthesized ground
  roll is subtracted from the seismic records to yield a seismic record with enhanced reflections.
  In the first part of this thesis, a regularized least-squares inversion algorithm is proposed.
  I further propose to adopt two types of regularization to guarantee the stability of the
  inversion. To be more specific, I compare classical quadratic regulation with sparsity promoting
  regularization. Numerical experiments show that sparsity promoting regularization
  yields better results than quadratically regularized inversion. A robust regularized inversion
  algorithm is also proposed to cope with erratic noise often present in onshore data.
  Through synthetic and field data examples, we prove the effectiveness and the limitations
  of these algorithms.

• Subjects / Keywords

  • IRLS
  • Robust Inversion
  • Least-Squares
  • ground roll
  • Inversion
  • ADMM

• Graduation date

  Fall 2020

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-pr6v-x918

• License

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