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Compressive sensing and entropy in seismic signals

Author

Listed:
  • Marinho, Eberton S.
  • Rocha, Tiago C.
  • Corso, Gilberto
  • Lucena, Liacir S.

Abstract

This work analyzes the correlation between the seismic signal entropy and the Compressive Sensing (CS) recovery index. The recovery index measures the quality of a signal reconstructed by the CS method. We analyze the performance of two CS algorithms: the ℓ1-MAGIC and the Fast Bayesian Compressive Sensing (BCS). We have observed a negative correlation between the performance of CS and seismic signal entropy. Signals with low entropy have small recovery index in their reconstruction by CS. The rationale behind our finding is: a sparse signal is easy to recover by CS and, besides, a sparse signal has low entropy. In addition, ℓ1-MAGIC shows a more significant correlation between entropy and CS performance than Fast BCS.

Suggested Citation

  • Marinho, Eberton S. & Rocha, Tiago C. & Corso, Gilberto & Lucena, Liacir S., 2017. "Compressive sensing and entropy in seismic signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 146-152.
    • Handle: RePEc:eee:phsmap:v:481:y:2017:i:c:p:146-152
    DOI: 10.1016/j.physa.2017.03.031
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    References listed on IDEAS

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    1. Kantelhardt, Jan W. & Penzel, Thomas & Rostig, Sven & Becker, Heinrich F. & Havlin, Shlomo & Bunde, Armin, 2003. "Breathing during REM and non-REM sleep: correlated versus uncorrelated behaviour," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 319(C), pages 447-457.
    2. Tavares, D.M. & Lucena, L.S., 2005. "Entropy analysis of stochastic processes at finite resolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 357(1), pages 71-78.
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