Computational Time Reversal for NDT Applications Using Experimental Data

Lopatin, Craig; Rabinovich, Daniel; Givoli, Dan; Turkel, Eli

doi:10.1007/s10921-017-0424-6

Cited by 9 publications

(3 citation statements)

References 39 publications

(57 reference statements)

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“…In Ref. 25, which deals with a nongeophysical application, only the total signal was used. However, we have found that the use of both the total and scattered signals enhances the identification.…”

Section: Augmented Trmentioning

confidence: 99%

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Scatterer identification in a 2D geophysical medium using an augmented computational time reversal method

Rabinovich

Givoli

Bielak³

et al. 2021

Num Anal Meth Geomechanics

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The problem of identifying an obstacle (scatterer) in the form of a cavity in a 2D geophysical medium is considered. This is posed as an inverse wave problem, where the location of the cavity is sought based on measurements of the elastic waves recorded by sensors located at certain points in the domain. The sensor measurements are noisy, and are generated synthetically as a first step. The inverse problem is solved by seeking the minimum of a specially designed cost functional, based on a computational time reversal (TR) procedure, where waves are radiated back in time from the sensors. The cost functional is defined to measure, for each scatterer candidate in the search space, the quality of the refocusing of the backward‐propagating waves on the given wave source at the end of the TR process. While the basic idea has appeared in previous publications, here it is applied to a 2D heterogeneous geophysical model (albeit a relatively simple one), and is enhanced by using several auxiliary techniques. These include (a) an “augmentation” technique, which is used to strengthen the coherent information (and thus to weaken the noncoherent information) by solving an elliptic problem at each time step; (b) experimentation with both instantaneous (impact) sources and time‐harmonic sources of finite duration; (c) combining the identification results of several sources and several source wavelengths to enhance identification; (d) reducing the size of the search space by a special “zooming in” technique; and (e) defining a performance index to assess the method's success and to provide a measure of confidence in the identification result in each specific case. Several numerical experiments are presented that demonstrate the performance of the proposed schemes. The sensitivity of the identification process to various parameters of the scatterer, the measurements, and the medium is investigated.

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Section: Augmented Trmentioning

confidence: 99%

“…In Ref. 25, TR was used to identify a hole in a small elastic plate. In this case, the measurements were obtained both from a lab experiment, and synthetically, the results of the two procedures were compared.…”

Section: Introductionmentioning

confidence: 99%

Scatterer identification in a 2D geophysical medium using an augmented computational time reversal method

Rabinovich

Givoli

Bielak³

et al. 2021

Num Anal Meth Geomechanics

Self Cite

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“…A full scan of the parameter space that defined the crack and its position was performed for optimization. [Lop+17] considers an experimental validation of this approach.…”

Section: Introductionmentioning

confidence: 99%

Defect reconstruction in a 2D semi-analytical waveguide model via derivative-based optimization

Bulling,

Jurgelucks,

Prager

et al. 2021

Preprint

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This paper considers the reconstruction of a defect in a two-dimensional waveguide using a derivativebased optimization approach. The propagation of the waves is simulated by the Scaled Boundary Finite Element Method (SBFEM) that builds on a semi-analytical approach. The simulated data is then fitted to a given set of data describing the reflection of a defect to be reconstructed. For this purpose, we apply an iteratively regularized Gauss-Newton method in combination with algorithmic differentiation to provide the required derivative information accurately and efficiently. We present numerical results for three different kinds of defects, namely a crack, a delamination, and a corrosion. These examples show that the parameterization of the defect can be reconstructed efficiently and robustly in the presence of noise.

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