Combined arrival-time imaging and time reversal for scatterer identification

Amitt, Eyal; Givoli, Dan; Turkel, Eli

doi:10.1016/j.cma.2016.08.031

Cited by 17 publications

(13 citation statements)

References 44 publications

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“…The main advantage of TR over the direct solution of the optimization problem, discussed earlier, is that the formulated inverse problem is quite wellbehaved [14]. In addition to that, TR is robust to noise in the measurements.…”

Section: Introductionmentioning

confidence: 99%

“…The presence of boundaries results to multiple reflections of the initially emitted pulse, a process that significantly increases the information content of the received signal. This extra information can clearly be beneficial in source localization problems [14,24], since it is equivalent to increasing the aperture size. In scatterer localization however, where the scatterer acts as a secondary source that emits pulses every time a wavefront impinges on it (see Sec.…”

Section: Introductionmentioning

confidence: 99%

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Signal-to-Noise Ratio analysis for time-reversal based imaging techniques in bounded domains

2018

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We consider the problem of localizing small material defects in rectangular bounded domains. The scalar acoustic equation is used to model wave propagation in this context. Our data is the scattered field collected at one or more receivers and due to impulsive excitations at one or more source positions. To localize the defect we use an imaging method that consists in back-propagating the recorded field in the domain of interest. The back-propagation is performed numerically using a model for the Green's function in the bounded medium. For the source localization problem this imaging technique is equivalent to computational Time Reversal (TR). We study in this paper the quality of imaging in terms of the Signal to Noise Ratio (SNR) both for the source and the defect localization problems. SNR here is defined as the value of the image at the true source (defect) location, divided by the maximal value of the image outside a small region around the true source (defect) location. Our theoretical analysis carried out for the simpler one-dimensional case allows us to correctly predict the performance of the method. Our results indicate that for the source localization problem the SNR increases linearly with the number of receivers while for the defect localization its maximal value is 2 and can only be attained by

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Signal-to-Noise Ratio analysis for time-reversal based imaging techniques in bounded domains

2018

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“…Recently, Amitt et al [40] proposed to combine Arrival Time Imaging and TR in order to greatly accelerate the identification process, by significantly reducing the search space for the TR. This and additional ideas, related to model reduction, may prove useful in making the technique an efficient and practical NDT procedure to be used by the industry.…”

Section: Discussionmentioning

confidence: 99%

Computational Time Reversal for NDT Applications Using Experimental Data

et al. 2017

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A model-based non destructive testing (NDT) method is proposed for damage identification in elastic structures, incorporating computational time reversal (TR) analysis. Identification is performed by advancing elastic wave signals, measured at discrete sensor locations, backward in time. In contrast to a previous study, which was purely numerical and employed only synthesized data, here an experimental system with displacement sensors is used to provide physical measurements at the sensor locations. The performance of the system is demonstrated by considering two problems of a thin metal plate in a plane stress state. The first problem, which represents passive damage identification, consists in finding the location of a small impact region from remote measurements. The second problem is the identification of the location of a square hole in the plate. The difficulties one encounters in applying this identification method and ways to overcome them are described. It is concluded that while this is a viable model-based identifica-

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“…Then, the finer TR method searches the precise position of the obstacle within the crude location. Indeed, in a recent paper by Amitt et al, the TR computational method has been combined with the arrival‐time method …”

Section: Introductionmentioning

confidence: 99%

“…Then, the finer TR method [10][11][12] searches the precise position of the obstacle within the crude location. Indeed, in a recent paper by Amitt et al, 13 the TR computational method [10][11][12] has been combined with the arrival-time method. 9 In this study, however, we offer a different computational methodology for obstacle identification, which is based on the time-reversed absorbing condition (TRAC) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Obstacle identification using the TRAC algorithm with a second‐order ABC

Levin

Turkel

Givoli

2019

Numerical Meth Engineering

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Summary We consider obstacle identification using wave propagation. In such problems, one wants to find the location, shape, and size of an unknown obstacle from given measurements. We propose an algorithm for the identification task based on a time‐reversed absorbing condition (TRAC) technique. Here, we apply the TRAC method to time‐dependent linear acoustics, although our methodology can be applied to other wave‐related problems as well, such as elastodynamics. There are two main contributions of our identification algorithm. The first contribution is the development of a robust and effective method for obstacle identification. While the original paper presented criteria for accepting or rejecting regions that enclose the obstacle, we use these criteria to develop an algorithm that automatically identifies the location of the obstacle. The second contribution is the utilization of an improved absorbing boundary condition (ABC) for the identification. We use the second‐order Engquist‐Majda ABC, and we implement it with a finite element scheme. To our knowledge, this is the first time that the second‐order Engquist‐Majda ABC is employed with the finite element method, as this boundary condition does not naturally fit in finite element schemes in its original form. Numerical experiments for the algorithms are presented.

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Combined arrival-time imaging and time reversal for scatterer identification

Cited by 17 publications

References 44 publications

Signal-to-Noise Ratio analysis for time-reversal based imaging techniques in bounded domains

Signal-to-Noise Ratio analysis for time-reversal based imaging techniques in bounded domains

Computational Time Reversal for NDT Applications Using Experimental Data

Obstacle identification using the TRAC algorithm with a second‐order ABC

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