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Including frequency-dependent attenuation for the deconvolution of ultrasonic signals
Author(s) -
Ewen Carcreff,
Sébastien Bourguig,
Jéro me Idier,
Laurent Simon,
Aroune Duclos
Publication year - 2013
Publication title -
proceedings of meetings on acoustics
Language(s) - English
Resource type - Conference proceedings
ISSN - 1939-800X
DOI - 10.1121/1.4800850
Subject(s) - deconvolution , attenuation , acoustics , ultrasonic sensor , nondestructive testing , classification of discontinuities , transducer , blind deconvolution , convolution (computer science) , acoustic impedance , computer science , optics , physics , algorithm , mathematics , artificial intelligence , mathematical analysis , artificial neural network , quantum mechanics
International audienceUltrasonic non-destructive testing (NDT) is a standard process for detecting flaws or discontinuities in industrial parts. A pulse is emitted by an ultrasonic transducer through a material, and a reflected wave is produced at each impedance change. In many cases, echoes can overlap in the received signal and deconvolution can be applied to perform echo separation and to enhance the resolution. Common deconvolution techniques assume that the shape of the echoes is invariant to the propagation distance. This can cause poor performances with materials such as plastics or composites, in particular because acoustic propagation suffers from frequency-dependent attenuation. In geophysics, biomedical imaging or NDT, various frequency-dependent attenuation models have been proposed under different formulations. This communication compares the related possible constructions in order to account for attenuation in deconvolution methods. Especially, we introduce a discrete model for the data, that includes an attenuation matrix in the standard convolution model. Experimental data acquired from Plexiglas plates show that, for this material, attenuation varies roughly linearly with frequency, leading to the identification of a unique parameter. Finally, we show that such an advanced model manages a better fitting of the data, and promises improvement for the deconvolution of complex ultrasonic data

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