Small defects reconstruction in waveguides from multifrequency one-side scattering data

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 $