doi: 10.3934/ipi.2021056
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Small defects reconstruction in waveguides from multifrequency one-side scattering data

1. 

Institut Fourier, Université Grenoble Alpes, France

2. 

Institut Camille Jordan, École Centrale Lyon, France

* Corresponding author: Angèle Niclas

Received  January 2021 Revised  June 2021 Early access September 2021

Localization and reconstruction of small defects in acoustic or electromagnetic waveguides is of crucial interest in nondestructive evaluation of structures. The aim of this work is to present a new multi-frequency inversion method to reconstruct small defects in a 2D waveguide. Given one-side multi-frequency wave field measurements of propagating modes, we use a Born approximation to provide a $ \text{L}^2 $-stable reconstruction of three types of defects: a local perturbation inside the waveguide, a bending of the waveguide, and a localized defect in the geometry of the waveguide. This method is based on a mode-by-mode spacial Fourier inversion from the available partial data in the Fourier domain. Indeed, in the available data, some high and low spatial frequency information on the defect are missing. We overcome this issue using both a compact support hypothesis and a minimal smoothness hypothesis on the defects. We also provide a suitable numerical method for efficient reconstruction of such defects and we discuss its applications and limits.

Citation: Éric Bonnetier, Angèle Niclas, Laurent Seppecher, Grégory Vial. Small defects reconstruction in waveguides from multifrequency one-side scattering data. Inverse Problems & Imaging, doi: 10.3934/ipi.2021056
References:
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[2]

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G. Bao and P. Li, Inverse medium scattering problems for electromagnetic waves, SIAM J. Appl. Math., 65 (2005), 2049-2066.  doi: 10.1137/040607435.  Google Scholar

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G. Bao and F. Triki, Reconstruction of a defect in an open waveguide, Sci. China Math., 56 (2013), 2539-2548.  doi: 10.1007/s11425-013-4696-8.  Google Scholar

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G. Bao and F. Triki, Stability for the multifrequency inverse medium problem, J. Differential Equations, 269 (2020), 7106-7128.  doi: 10.1016/j.jde.2020.05.021.  Google Scholar

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L. Bourgeois and S. Fliss, On the identification of defects in a periodic waveguide from far field data, Inverse Problems, 30 (2014), 095004. doi: 10.1088/0266-5611/30/9/095004.  Google Scholar

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L. Bourgeois and E. Lunéville, The linear sampling method in a waveguide: A modal formulation, Inverse Problems, 24 (2008), 015018. doi: 10.1088/0266-5611/24/1/015018.  Google Scholar

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S. Dediu and J. R. McLaughlin, Recovering inhomogeneities in a waveguide using eigensystem decomposition, Inverse Problems, 22 (2006), 1227-1246.  doi: 10.1088/0266-5611/22/4/007.  Google Scholar

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A. S. B.-B. DhiaL. Chesnel and S. A. Nazarov, Perfect transmission invisibility for waveguides with sound hard walls, J. Math. Pures Appl., 111 (2018), 79-105.  doi: 10.1016/j.matpur.2017.07.020.  Google Scholar

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M. Isaev and R. G. Novikov, Hölder-logarithmic stability in Fourier synthesis, Inverse Problems, 36 (2020), 125003. doi: 10.1088/1361-6420/abb5df.  Google Scholar

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V. Isakov and S. Lu, Increasing stability in the inverse source problem with attenuation and many frequencies, SIAM J. Appl. Math., 78 (2018), 1-18.  doi: 10.1137/17M1112704.  Google Scholar

[19]

M. KharratO. BareilleW. Zhou and M. Ichchou, Nondestructive assessment of plastic elbows using torsional waves: Numerical and experimental investigations, Journal of Nondestructive Evaluation, 35 (2016), 1-14.  doi: 10.1007/s10921-015-0324-6.  Google Scholar

[20]

M. Kharrat, M. N. Ichchou, O. Bareille and W. Zhou, Pipeline inspection using a torsional guided-waves inspection system. part 1: Defect identification, International Journal of Applied Mechanics, 6 (2014). doi: 10.1142/S1758825114500343.  Google Scholar

[21]

Y. Y. Lu, Exact one-way methods for acoustic waveguides, Math. Comput. Simulation, 50 (1999), 377-391.  doi: 10.1016/S0378-4754(99)00111-1.  Google Scholar

[22] W. McLean, Strongly Elliptic Systems and Boundary Integral Equations, Cambridge University Press, 2000.   Google Scholar
[23]

M. Sini and N. T. Thanh, Inverse acoustic obstacle scattering problems using multifrequency measurements, Inverse Probl. Imaging, 6 (2012), 749-773.  doi: 10.3934/ipi.2012.6.749.  Google Scholar

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J. Todd, The condition of the finite segment of the Hilbert matrix, National Bureau of Standarts, Applied Mathematics Series, (1954), 109–119.  Google Scholar

show all references

References:
[1]

L. Abrahamsson, Orthogonal grid generation for two-dimensional ducts, J. Comput. Appl. Math., 34 (1991), 305-314.  doi: 10.1016/0377-0427(91)90091-W.  Google Scholar

[2]

L. Abrahamsson and H. O. Kreiss, Numerical solution of the coupled mode equations in duct acoustics, J. Comput. Phy., 111 (1994), 1-14.  doi: 10.1006/jcph.1994.1038.  Google Scholar

[3]

S. Acosta, S. Chow, J. Taylor and V. Villamizar, On the multi-frequency inverse source problem in heterogeneous media, Inverse Problems, 28 (2012), 075013. doi: 10.1088/0266-5611/28/7/075013.  Google Scholar

[4]

H. AmmariE. Iakovleva and H. Kang, Reconstruction of a small inclusion in a two-dimensional open waveguide, SIAM J. Appl. Math., 65 (2005), 2107-2127.  doi: 10.1137/040615389.  Google Scholar

[5]

G. Bao and P. Li, Inverse medium scattering problems for electromagnetic waves, SIAM J. Appl. Math., 65 (2005), 2049-2066.  doi: 10.1137/040607435.  Google Scholar

[6]

G. Bao and F. Triki, Reconstruction of a defect in an open waveguide, Sci. China Math., 56 (2013), 2539-2548.  doi: 10.1007/s11425-013-4696-8.  Google Scholar

[7]

G. Bao and F. Triki, Stability for the multifrequency inverse medium problem, J. Differential Equations, 269 (2020), 7106-7128.  doi: 10.1016/j.jde.2020.05.021.  Google Scholar

[8]

J. P. Berenger, A perfectly matched layer for the absorption of electromagnetic waves, J. Comput. Phys., 114 (1994), 185-200.  doi: 10.1006/jcph.1994.1159.  Google Scholar

[9]

L. Bourgeois and S. Fliss, On the identification of defects in a periodic waveguide from far field data, Inverse Problems, 30 (2014), 095004. doi: 10.1088/0266-5611/30/9/095004.  Google Scholar

[10]

L. Bourgeois and E. Lunéville, The linear sampling method in a waveguide: A modal formulation, Inverse Problems, 24 (2008), 015018. doi: 10.1088/0266-5611/24/1/015018.  Google Scholar

[11]

D. Colton and A. Kirsch, A simple method for solving inverse scattering problems in the resonance region, Inverse Problems, 12 (1996), 383-393.  doi: 10.1088/0266-5611/12/4/003.  Google Scholar

[12]

D. Colton and R. Kress, Inverse Acoustic and Electromagnetic Scattering Theory, Applied Mathematical Sciences, Springer-Verlag, Berlin, 1992. doi: 10.1007/978-3-662-02835-3.  Google Scholar

[13]

S. Dediu and J. R. McLaughlin, Recovering inhomogeneities in a waveguide using eigensystem decomposition, Inverse Problems, 22 (2006), 1227-1246.  doi: 10.1088/0266-5611/22/4/007.  Google Scholar

[14]

A. S. B.-B. DhiaL. Chesnel and S. A. Nazarov, Perfect transmission invisibility for waveguides with sound hard walls, J. Math. Pures Appl., 111 (2018), 79-105.  doi: 10.1016/j.matpur.2017.07.020.  Google Scholar

[15]

H. Dym and H. P. McKean, Fourier Series and Integrals, Academic Press New York, 1972.  Google Scholar

[16]

P. Grisvard, Elliptic Problems in Nonsmooth Domains, Society for Industrial and Applied Mathematics, 2011. doi: 10.1137/1.9781611972030.ch1.  Google Scholar

[17]

M. Isaev and R. G. Novikov, Hölder-logarithmic stability in Fourier synthesis, Inverse Problems, 36 (2020), 125003. doi: 10.1088/1361-6420/abb5df.  Google Scholar

[18]

V. Isakov and S. Lu, Increasing stability in the inverse source problem with attenuation and many frequencies, SIAM J. Appl. Math., 78 (2018), 1-18.  doi: 10.1137/17M1112704.  Google Scholar

[19]

M. KharratO. BareilleW. Zhou and M. Ichchou, Nondestructive assessment of plastic elbows using torsional waves: Numerical and experimental investigations, Journal of Nondestructive Evaluation, 35 (2016), 1-14.  doi: 10.1007/s10921-015-0324-6.  Google Scholar

[20]

M. Kharrat, M. N. Ichchou, O. Bareille and W. Zhou, Pipeline inspection using a torsional guided-waves inspection system. part 1: Defect identification, International Journal of Applied Mechanics, 6 (2014). doi: 10.1142/S1758825114500343.  Google Scholar

[21]

Y. Y. Lu, Exact one-way methods for acoustic waveguides, Math. Comput. Simulation, 50 (1999), 377-391.  doi: 10.1016/S0378-4754(99)00111-1.  Google Scholar

[22] W. McLean, Strongly Elliptic Systems and Boundary Integral Equations, Cambridge University Press, 2000.   Google Scholar
[23]

M. Sini and N. T. Thanh, Inverse acoustic obstacle scattering problems using multifrequency measurements, Inverse Probl. Imaging, 6 (2012), 749-773.  doi: 10.3934/ipi.2012.6.749.  Google Scholar

[24]

J. Todd, The condition of the finite segment of the Hilbert matrix, National Bureau of Standarts, Applied Mathematics Series, (1954), 109–119.  Google Scholar

Figure 1.  Representation of the three types of defects: in $ (1) $ a local perturbation $ q $, in $ (2) $ a bending of the waveguide, in $ (3) $ a localized defect in the geometry of $ \Omega $. A controlled source $ s $ generates a wave field $ u^\text{inc}_k $. When it crosses the defect, it generates a scattered wave field $ u^s_k $. Both $ u^\text{inc}_k $ and $ u^s_k $ are measured on the section $ \Sigma $
Figure 2.  Condition number of $ M_t^TM_t $ for different sizes of support and values of $ \omega_0 $. Here, $ X $ is the discretization of $ [1-r, 1+r] $ with $ 500r+1 $ points. The $ x $-axis represents the evolution of $ r $, and the $ y $-axis $ \text{cond}_2(M_t^TM_t) $. Each curve corresponds to value of $ \omega_0 $ as indicated in the left rectangle
Figure 3.  Representation of a bend in a waveguide
Figure 4.  Representation of a shape defect in a waveguide
Figure 5.  Reconstruction of $ f(x) = (x-0.8)(1.2-x)\textbf{1}_{0.8\leq x\leq 1.2} $ for different values of $ \omega_1 $ using the discrete operator $ \gamma $ and the algorithm (91) with $ \lambda = 0.001 $. Here, $ X $ is the discretization of $ [0.5, 1.5] $ with $ 10\omega_1 $ points, and $ K $ is the discretization of $ [0.01, \omega_1] $ with $ 1000 $ points
Figure 6.  $ \text{L}^2 $-error between $ f(x) = (x-0.8)(1.2-x)\textbf{1}_{0.8\leq x\leq 1.2} $ and its reconstruction $ f_{app} $ for different values of $ \omega_1 $ using the discrete operator $ \gamma $ and the algorithm (91) with $ \lambda = 0.001 $. Here, $ X $ is the discretization of $ [0.5, 1.5] $ with $ 10\omega_1 $ points, and $ K $ is the discretization of $ [0.01, \omega_1] $ with $ 1000 $ points
Figure 7.  Reconstruction of $ f(x) = (x-0.8)(1.2-x)\textbf{1}_{0.8\leq x\leq 1.2} $ for different values of $ \omega_0 $ and $ r = 0.5 $ using the discrete operator $ \gamma $ and the algorithm (91) with $ \lambda = 0.001 $. Here, $ X $ is the discretization of $ [0.5, 1.5] $ with $ 251 $ points, and $ K $ is the discretization of $ [\omega_0, 50] $ with $ 1000 $ points
Figure 8.  Reconstruction of $ f(x) = (x-0.8)(1.2-x)\textbf{1}_{0.8\leq x\leq 1.2} $ for different sizes of support $ r $ and $ \omega_0 = 3\pi $ using the discrete operator $ \gamma $ and the algorithm (91) with $ \lambda = 0.001 $. Here, $ X $ is the discretization of $ [1-r, 1+r] $ with $ 500r+1 $ points, and $ K $ is the discretization of $ [3\pi, 50] $ with $ 1000 $ points
Figure 9.  Reconstruction of two different bends. The black lines represent the initial shape of $ \Omega $, and the red the reconstruction of $ \Omega $. In both cases, $ K $ is the discretization of $ [0.01, 40] $ with $ 100 $ points, and the reconstruction is obtain by (94). On the left, the initial parameters of the bend are $ (x_c, r, \theta) = (4, 10, \pi/12) $ and on the right, $ (x_c, r, \theta) = (2, 5, \pi/6) $
Figure 10.  Reconstruction of a waveguide with two successive bends. The black lines represent the initial shape of $ \Omega $, and the red the reconstruction of $ \Omega $, slightly shifted for comparison purposes. In both cases, $ K $ is the discretization of $ [0.01, 40] $ with $ 100 $ points. The parameters of the two bends are $ (x_c^{(1)}, r^{(1)}, \theta^{(1)}) = (2, 10, \pi/30)) $ and $ (x_c^{(2)}, r^{(2)}, \theta^{(2)}) = (3.8, 8, -\pi/20)) $
Figure 11.  Reconstruction of two shape defects. In black, the initial shape of $ \Omega $, and in red the reconstruction, slightly shifted for comparison purposes. In both cases, $ K $ is the discretization of $ [0.01, 70]\setminus \{[n\pi-0.2, n\pi+0.2], n\in \mathbb{N}\} $ with $ 300 $ points, $ X $ is the discretization of $ [3, 4.5] $ with $ 151 $ points and we use the algorithm (91) with $ \lambda = 0.08 $ to reconstruct $ s_0 $ and $ s_1 $. On the left, $ h(x) = \frac{5}{16}\textbf{1}_{3.2\leq x\leq 4.2}(x-3.2)^2(4.2-x)^2 $ and $ g(x) = -\frac{35}{16}\textbf{1}_{3.4\leq x\leq 4}(x-3.4)^2(4-x)^2 $. On the right, $ h(x) = \frac{125}{16}\textbf{1}_{3.7\leq x\leq 4.2}(x-3.7)^2(4.2-x)^2 $ and $ g(x) = \frac{125}{16}\textbf{1}_{3.4\leq x\leq 4}(x-3.4)^2(4-x)^2 $
Figure 12.  Recontruction of $ h_n $ for $ 0\leq n\leq 9 $, where $ h(x) = 0.05\textbf{1}_{\left|\left(\frac{x-4}{0.05}, \frac{y-0.6}{0.15}\right)\right|\leq 1}\left|\left(\frac{x-4}{0.05}, \frac{y-0.6}{0.15}\right)\right|^2 $. In blue, we represent $ h_n $ and in red the reconstruction of $ h_{n_{\text{app}}} $. In every reconstruction, $ K $ is the discretization of $ [0.01, 150] $ with $ 200 $ points, $ X $ is the discretization of $ [3.8, 4.2] $ with $ 101 $ points and we use the algorithm (91) with $ \lambda = 0.002 $ to reconstruct every $ h_n $
Figure 13.  Recontruction of an inhomogeneity $ h $, where $ h(x) = 0.05\textbf{1}_{\left|\left(\frac{x-4}{0.05}, \frac{y-0.6}{0.15}\right)\right|\leq 1}\left|\left(\frac{x-4}{0.05}, \frac{y-0.6}{0.15}\right)\right|^2 $. On the left, we represent the initial shape of $ h $, and on the right the reconstruction $ h_{\text{app}} $. Here, $ K $ is the discretization of $ [0.01, 150] $ with $ 200 $ points, $ X $ is the discretization of $ [3.8, 4.2] $ with $ 101 $ points and we use the algorithm (91) with $ \lambda = 0.002 $ to reconstruct every $ h_n $. We used $ N = 20 $ modes to reconstruct $ h $
Figure 14.  Recontruction of an inhomogeneity $ h $. From top to bottom, the initial representation of $ h $, the reconstruction $ h_{\text{app}} $ and the reconstruction $ h_{\text{app}} $ with the knowledge of the positivity of $ h $. Here, $ K $ is the discretization of $ [0.01, 150] $ with $ 200 $ points, $ X $ is the discretization of $ [3, 6] $ with $ 3001 $ points and we use the algorithm (91) with $ \lambda = 0.01 $ to reconstruct every $ h_n $. We choose used $ N = 20 $ modes to reconstruct $ h $
Table 1.  Relative errors on the reconstruction of $ (x_c, r, \theta) $ for different bends. In each case, $ K $ is the discretization of $ [0.01, 40] $ with $ 100 $ points, and the reconstruction is obtain by (94)
$ (x_c, r, \theta) $ $ (2.5, 40, \pi/80) $ $ (4, 10, \pi/12) $ $ (2, 5, \pi/6) $
relative error on $ x_c $ $ 1.8\% $ $ 0\% $ $ 7.6\% $
relative error on $ r $ $ 3.0\% $ $ 7.5\% $ $ 23.8\% $
relative error on $ \theta $ $ 1.6\% $ $ 10.7\% $ $ 16.9\% $
$ (x_c, r, \theta) $ $ (2.5, 40, \pi/80) $ $ (4, 10, \pi/12) $ $ (2, 5, \pi/6) $
relative error on $ x_c $ $ 1.8\% $ $ 0\% $ $ 7.6\% $
relative error on $ r $ $ 3.0\% $ $ 7.5\% $ $ 23.8\% $
relative error on $ \theta $ $ 1.6\% $ $ 10.7\% $ $ 16.9\% $
Table 2.  Relative errors on the reconstruction of $ h $ for different amplitudes $ A $. We choose $ h(x) = A\textbf{1}_{3\leq x\leq 5}(x-3)^2(5-x)^2 $ and $ g(x) = 0 $. In every reconstruction, $ K $ is the discretization of $ [0.01, 40]\setminus \{[n\pi-0.2, n\pi+0.2], n\in \mathbb{N}\} $ with $ 100 $ points, $ X $ is the discretization of $ [1, 7] $ with $ 601 $ points and we use the algorithm (91) with $ \lambda = 0.08 $ to reconstruct $ h' $
$ A $ $ 0.1 $ $ 0.2 $ $ 0.3 $ $ 0.5 $
$ \Vert h-h_{\text{app}}\Vert_{\text{L}^2( \mathbb{R})}/\Vert h\Vert_{\text{L}^2( \mathbb{R})} $ $ 8.82\% $ $ 10.41\% $ $ 15.12\% $ $ 54.99\% $
$ A $ $ 0.1 $ $ 0.2 $ $ 0.3 $ $ 0.5 $
$ \Vert h-h_{\text{app}}\Vert_{\text{L}^2( \mathbb{R})}/\Vert h\Vert_{\text{L}^2( \mathbb{R})} $ $ 8.82\% $ $ 10.41\% $ $ 15.12\% $ $ 54.99\% $
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