Interpolation and denoising of seismic signals using orthogonal matching pursuit algorithm: An aplication in VSP and refraction data

  • Rómulo Sandoval-Flórez
  • José Luis Paredes- Quintero
  • Flor Alba Vivas
  • Francisco Cabrera Zambrano
Keywords: Sparse coding, Dictionary learning, Denoising, Interpolation, VSP

Abstract

An implementation of the Orthogonal Matching Pursuit (OMP) algorithm was used and the results obtained therefrom are presented for simultaneous interpolation and denoising from seismic signals in the framework of sparse signal representation. OMP is an algorithm for sparse signal representation based on orthogonal projections underlying the signal over an over-complete dictionary. This over-complete dictionary was designed using K-times Singular Values Decomposition (K-SVD). In each iteration, OMP calculates a new signal approximation and the approximation error is used in the next iteration to determine the new element. The new element corresponds to the largest magnitude of the inner products between the current residual and the original elements in the dictionary. The implemented algorithm was applied to VSP seismic data and refraction seismic data; results for the application in restored missing traces and denoise signals are presented.

How to Cite
Sandoval-Flórez, R., Paredes- Quintero, J. L., Vivas, F. A., & Zambrano, F. C. (2018). Interpolation and denoising of seismic signals using orthogonal matching pursuit algorithm: An aplication in VSP and refraction data. CT&F - Ciencia, Tecnología Y Futuro, 8(2). https://doi.org/10.29047/01225383.81

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Published
2018-12-19
Section
Scientific and Technological Research Articles
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