Unsupervised Processing of Geophysical Signals: A Review of Some Key Aspects of Blind Deconvolution and Blind Source Separation

Takahata, André K.; Nadalin, Everton Z.; Ferrari, Rafael; Duarte, Leonardo Tomazeli; Suyama, Ricardo; Lopes, Renato da Rocha; Romano, João Marcos Travassos; Tygel, Martin

doi:10.1109/msp.2012.2189999

Cited by 44 publications

(32 citation statements)

References 21 publications

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“…The deconvolution problem becomes blind, even more ill-posed, when the blur kernel h is unknown, and needs to be estimated as well as the target signal. Applications include communications (equalization or channel estimation) [2], nondestructive testing [3], geophysics [4][5][6], image processing [7][8][9][10], medical imaging and remote sensing [11]. Blind deconvolution, being an underdetermined problem, often requires additional hypotheses.…”

Section: Introductionmentioning

confidence: 99%

Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed <formula formulatype="inline"><tex Notation="TeX">${\ell _1}/{\ell _2}$</tex></formula> Regularization

Repetti

Pham

Duval

et al. 2015

IEEE Signal Process. Lett.

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The ℓ 1 /ℓ 2 ratio regularization function has shown good performance for retrieving sparse signals in a number of recent works, in the context of blind deconvolution. Indeed, it benefits from a scale invariance property much desirable in the blind context. However, the ℓ 1 /ℓ 2 function raises some difficulties when solving the nonconvex and nonsmooth minimization problems resulting from the use of such a penalty term in current restoration methods. In this paper, we propose a new penalty based on a smooth approximation to the ℓ 1 /ℓ 2 function. In addition, we develop a proximal-based algorithm to solve variational problems involving this function and we derive theoretical convergence results. We demonstrate the effectiveness of our method through a comparison with a recent alternating optimization strategy dealing with the exact ℓ 1 /ℓ 2 term, on an application to seismic data blind deconvolution.

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Section: Introductionmentioning

confidence: 99%

Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed <formula formulatype="inline"><tex Notation="TeX">${\ell _1}/{\ell _2}$</tex></formula> Regularization

Repetti

Pham

Duval

et al. 2015

IEEE Signal Process. Lett.

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show abstract

“…In these methods, the system is assumed to be multi input multi output [4], which means that N signals are produced by different sources, these signals are then captured by M sensors after travelling through the medium. Based on the statistical differences, blind signal separation methods can separate the signals from all sources having only signals captured by the sensors.…”

Section: Methodsmentioning

confidence: 99%

Limitation of the Lateral Angled Broadband Low Frequency Impact Excitation on the Non-Destructive Condition Assessment of the Timber Utility Poles

Jozi¹,

Braun²,

Samali³

et al. 2017

Int J Adv Tech

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“…Recently, with the rapid advance in signal processing technologies, various approaches have been applied in the field of non-stationary signals analysis such as short-time Fourier transform (STFT), wavelet transform (WT) and blind source separation (BSS), and so on [3,4,5]. Empirical mode decomposition (EMD), which is based on the local characteristic time-scale of the data, can be used in nonlinear and non-stationary processes and has been widely applied in many fields successfully [6].…”

Section: Introductionmentioning

confidence: 99%

Analysis of hydro-turbine non-stationary vibration signals based on CEEMDAN and Wigner-Ville Distribution

Jiang¹,

Zhou²

2017

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017

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Abstract. This paper proposes a time-frequency method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Winger-Ville distribution (WVD) for hydro-turbine non-stationary vibration signals analysis. Unlike the traditional EMD and EEMD, CEEMDAN can efficiently reduce the mode mixing and has a better convergence. Base on a series of intrinsic mode functions (IMFs) obtained by using CEEMDAN, WVD can effectively suppress the interference of cross-terms and accurately depict the time-frequency characteristics of original vibration signals. The proposed method can be employed to analyze the hydro-turbine non-stationary vibration signals successfully and shows excellent performance.

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Unsupervised Processing of Geophysical Signals: A Review of Some Key Aspects of Blind Deconvolution and Blind Source Separation

Cited by 44 publications

References 21 publications

Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed <formula formulatype="inline"><tex Notation="TeX">${\ell _1}/{\ell _2}$</tex></formula> Regularization

Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed <formula formulatype="inline"><tex Notation="TeX">${\ell _1}/{\ell _2}$</tex></formula> Regularization

Limitation of the Lateral Angled Broadband Low Frequency Impact Excitation on the Non-Destructive Condition Assessment of the Timber Utility Poles

Analysis of hydro-turbine non-stationary vibration signals based on CEEMDAN and Wigner-Ville Distribution

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