Compressive sensing and entropy in seismic signals

Eberton S. Marinho, Tiago C. Rocha, Gilberto Corso and Liacir S. Lucena

Physica A: Statistical Mechanics and its Applications, 2017, vol. 481, issue C, 146-152

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.

Keywords: Seismic data reconstruction; ℓ1-MAGIC; Fast Bayesian compressive sensing; Wavelets; Sparsity (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:481:y:2017:i:c:p:146-152

DOI: 10.1016/j.physa.2017.03.031

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