Abstract
The monotonicity method for the inverse acoustic scattering problem is to understand the inclusion relation between an unknown object and artificial one by comparing the far field operator with the artificial operator. This paper introduces the development of this method to the inverse crack scattering problem. Our aim is to give the following two indicators: One (Theorem 1.1) is to determine whether an artificial small arc is contained in the unknown arc. The other one (Theorem 1.2) is whether an artificial large domain contains the unknown one. Finally, numerical examples based on Theorem 1.1 are given.
2010 AMS Subject Classification:
1. Introduction
Let be a smooth non-intersecting open arc, and we assume that Γ can be extended to an arbitrary smooth, simply connected, closed curve enclosing a bounded domain Ω in . Let k>0 be the wave number, and let be incident direction, where denotes the unit sphere in . We consider the following direct scattering problem: For determine such that (1) (1) (2) (2) (3) (3) where , and (Equation3(3) (3) ) is the Sommerfeld radiation condition. Precisely, this problem is understood in the variational form, that is, determine satisfying , the Sommerfeld radiation condition (Equation3(3) (3) ), and (4) (4) for all , , with compact support. Here, denotes the local Sobolev space of one order.
It is well known that there exists a unique solution and it has the following asymptotic behaviour (see, e.g.[Citation1]): (5) (5) The function is called the far field pattern of . With the far field pattern , we define the far field operator by (6) (6) The inverse scattering problem we consider in this paper is to reconstruct the unknown arc Γ from the far field pattern for all , all with one k>0. In other words, given the far field operator F, reconstruct Γ.
In order to solve such an inverse problem, we use the idea of the monotonicity method. The feature of this method is to understand the inclusion relation of an unknown object and artificial one by comparing the data operator with some operator corresponding to an artificial object. For electrical impedance tomography (EIT) we refer to [Citation2], for the inverse boundary value problem for the Helmholtz equation we refer to [Citation3 5], and for the inverse medium scattering problem we refer to [Citation6,Citation7].
Our aim in this paper is to provide the following two theorems.
Theorem 1.1
Let be a smooth non-intersecting open arc. Then, (7) (7) where the Herglotz operator is given by (8) (8) and the inequality on the right-hand side in (Equation7(7) (7) ) denotes that has only finitely many negative eigenvalues, and the real part of an operator A is self-adjoint operators given by .
Theorem 1.2
Let be a bounded open set. Then, (9) (9) where is given by (10) (10)
Theorem 1.1 determines whether an artificial open arc σ is contained in Γ or not. While, Theorem 1.2 determines an artificial domain B contains Γ. In two theorems we can understand Γ from the inside and outside.
This paper is organized as follows. In Section 2, we give a rigorous definition of the above inequality. Furthermore, we recall the properties of the far field operator and technical lemmas which are useful to prove main results. In Sections 3 and 4, we prove Theorems 1.1 and 1.2, respectively. In Section 5, we give numerical examples based on Theorem 1.1.
2. Preliminary
First, we give a rigorous definition of the inequality in Theorems 1.1 and 1.2.
Definition 2.1
Let be self-adjoint compact linear operators on a Hilbert space X. We write (11) (11) if B−A has only finitely many negative eigenvalues.
The following lemma was shown in Corollary 3.3 of [Citation4].
Lemma 2.2
Let be self-adjoint compact linear operators on a Hilbert space X with an inner product . Then, the following statements are equivalent:
There exists a finite dimensional subspace V in X such that (12) (12) for all .
Secondly, we define several operators in order to mention properties of the far field operator F. The data-to-pattern operator is defined by (13) (13) where is the far field pattern of a radiating solution v (that is, v satisfies the Sommerfeld radiation condition) such that (14) (14) (15) (15) The following lemma was given by the same argument in Lemma 1.13 of [Citation8].
Lemma 2.3
The data-to-pattern operator G is compact and injective.
We define the single layer boundary operator by (16) (16) where denotes the fundamental solution to Helmholtz equation in , i.e. (17) (17) Here, we denote by (18) (18) (19) (19) and and the dual spaces of and , respectively. Then, we have the following inclusion relation: (20) (20) For these details, we refer to [Citation9]. The following two Lemmas was shown in Section 3 of [Citation10].
Lemma 2.4
S is an isomorphism from onto .
Let be the boundary integral operator (Equation16(16) (16) ) corresponding to the wave number k = i. The operator is self-adjoint and coercive, i.e, there exists such that (21) (21) where denotes the duality pairing in .
is compact.
There exists a self-adjoint and positive square root of which can be extended such that is an isomorphism and
Lemma 2.5
The far field operator F has the following factorization: (22) (22) where and are the adjoints of G and S, respectively.
Thirdly, we recall the following technical lemmas which will be useful to prove Theorems 1.1 and 1.2. We refer to Lemma 4.6 and 4.7 in [Citation4].
Lemma 2.6
Let X, Y, and Z be Hilbert spaces, and let and be bounded linear operators. Then, (23) (23)
Lemma 2.7
Let X, Y, be subspaces of a vector space Z. If (24) (24) then .
3. Proof of Theorem 1.1
In Section 3, we will show Theorem 1.1. Let . We denote by the restriction operator, the compact embedding, and , the Herglotz operators, respectively. Since is a far field pattern of , we have by definitions of G and S (25) (25) The right-hand side is identical with (see the proof of Lemma 3.4 in [Citation10]). Then, we have . By this equality we have (26) (26) Using (Equation26(26) (26) ) and Lemmas 2.4 and 2.5, has the following factorization: (27) (27) where is an extension of the square root of , is self-adjoint compact, and is the identity operator on . Let V be the sum of eigenspaces of K associated to eigenvalues less than . Then, V is a finite dimensional and (28) (28) for all . Since for (29) (29) and , we have by (Equation28(28) (28) ) and Lemma 2.2 that .
Let now and assume on the contrary , that is, by Lemma 2.2 there exists a finite dimensional subspace V in such that (30) (30) for all . Since , we can take a small open arc such that , which implies that for all (31) (31) Before showing a contradiction with (Equation31(31) (31) ), we will show the following lemma.
Lemma 3.1
.
Proof of Lemma 3.1.
By the same argument in (Equation26(26) (26) ) we have (32) (32) where , , and are the data-to-pattern operator, the single layer boundary operator, and the compact embedding, respectively, corresponding to . Since , is dense, and is injective (see Lemma 2.3 and (a) of Lemma 2.4.), we have .
By (Equation32(32) (32) ), we have . Let , i.e. where and are far field patterns of the scattered field and associated to scatterers Γ and , respectively. Then by Rellich lemma and unique continuation we have . Hence, we can define by (33) (33) and v is a radiating solution to (34) (34) Thus , which implies that h = 0.
By the above lemma we have and . By a contraposition of Lemma 2.7, we have (35) (35) where is the orthognal projection on V. Lemma 2.6 implies that for any C>0 there exists a such that (36) (36) Hence, there exists a sequence such that and as . Setting we have as , (37) (37) (38) (38) This contradicts (Equation31(31) (31) ). Therefore, we have . Theorem 1.1 has been shown.
4. Proof of Theorem 1.2
In Section 4, we will show Theorem 1.2. Let . We denote by and are the data-to-pattern operator and the single layer boundary operator, respectively corresponding to closed curve . They have the same properties like Lemmas 2.3 and 2.4 and we have . (See, e.g. Lemma 1.14, Theorem 1.15 in [Citation8].) We define by (39) (39) where v is a radiating solution such that (40) (40) (41) (41) T is compact since its mapping is from to . Furthermore, by the definition of T we have that . Thus, we have (42) (42) where K and are some self-adjoint compact operators, and is an extension of the square root of where is the single layer boundary operator corresponding to and the wave number k = i. Let V be the sum of eigenspaces of associated to eigenvalues less than . Then V is a finite dimensional, and for all we have (43) (43) Therefore, .
Let now and assume on the contrary , i.e. by Lemma 2.2 there exists a finite dimensional subspace V in such that (44) (44) for all . Since , we can take a small open arc such that . We define by (45) (45) where v is a radiating solution such that (46) (46) (47) (47) By the definition of L, we have where is the data-to-pattern operator corresponding to . We denote by the single layer boundary operator corresponding to , and , the Herglotz operators corresponding to , respectively. By the same argument in (Equation25(25) (25) ) we have . Then, we have (48) (48) for . Since is of the form , by the similar argument in (Equation27(27) (27) )–(Equation28(28) (28) ) and (Equation42(42) (42) )–(Equation43(43) (43) ), there exists a finite dimensional subspace W in such that for (49) (49) Collecting (Equation48(48) (48) ), (Equation49(49) (49) ), and , we have (50) (50) for .
Lemma 4.1
.
Proof of Lemma 4.1.
is given by the same argument in Lemma 3.1.
Since (Equation32(32) (32) ) replacing by holds, by taking a conjugation in (Equation32(32) (32) ) we have . Let , i.e. where and are far field patterns of the scattered field and associated to scatterers B and , respectively. Then by Rellich lemma and unique continuation we have . Hence, we can define by (51) (51) and v is a radiating solution to (52) (52) Thus , which implies that h = 0.
By the above lemma we have and . By a contraposition of Lemma 2.7, we have (53) (53) where is the orthognal projection on . Lemma 2.6 implies that for any C>0 there exists a such that (54) (54) Hence, there exists a sequence such that and as . Setting we have as , (55) (55) (56) (56) This contradicts (Equation50(50) (50) ). Therefore, we have . Theorem 1.2 has been shown.
5. Numerical examples
In Section 5, we discuss the numerical examples based on Theorem 1.1. The following three open arcs (j = 1, 2, 3) are considered. (see Figure )
Based on Theorem 1.1, the indicator function in our examples is given by (57) (57) The idea to reconstruct is to plot the value of for many of small σ in the sampling region. Then, we expect from Theorem 1.1 that the value of the function is low if σ is close to .
Here, σ is chosen in two ways; One is the vertical line segment where () denote the centre of , and is the length of , and R>0 is length of sampling square region , and is large to take a small segment. The other is horizontal one .
The far field operator F is approximated by the matrix (58) (58) where and . The far field pattern of the problem (Equation1(1) (1) )–(Equation3(3) (3) ) is computed by the Nyström method in [Citation11]. The operator is approximated by (59) (59) When σ is given by the vertical and horizontal line segment, we can compute the integrals (60) (60) (61) (61)
In our examples we fix R = 1.5, M = 100, N = 60, and wavenumber k = 1. Figure is given by plotting the values of the vertical indicator function (62) (62) for each . Figure is given by plotting the values of the horizontal indicator function (63) (63) for each . We obverse that seems to be reconstructed independently of the direction of linear segment.
Acknowledgments
Authors thank to Professor Bastian von Harrach, who gave us helpful comments in our study.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Takashi Furuya http://orcid.org/0000-0001-6132-6846
References
- Colton D, Kress R. Inverse acoustic and electromagnetic scattering theory. Third edition. Vol. 93. Applied Mathematical Sciences. New York: Springer; 2013.
- Harrach B, Ullrich M. Monotonicity based shape reconstruction in electrical impedance tomography. SIAM J Math Anal. 2013;45(6):3382–3403. doi: 10.1137/120886984
- Harrach B, Pohjola V, Salo M. Dimension bounds in monotonicity methods for the Helmholtz equation. SIAM J Appl Math. 2019;51(4):2995–3019. doi: 10.1137/19M1240708
- Harrach B, Pohjola V, Salo M. Monotonicity and local uniqueness for the Helmholtz equation. Anal PDE. 2019;12(7):1741–1771. doi: 10.2140/apde.2019.12.1741
- Harrach B, Ullrich M. Local uniqueness for an inverse boundary value problem with partial data. Proc Am Math Soc. 2017;145(3):1087–1095. doi: 10.1090/proc/12991
- Griesmaier R, Harrach B. Monotonicity in inverse medium scattering on unbounded domains. SIAM J Appl Math. 2018;78(5):2533–2557. doi: 10.1137/18M1171679
- Lakshtanov E, Lechleiter A. Difference factorizations and monotonicity in inverse medium scattering for contrasts with fixed sign on the boundary. SIAM J Math Anal. 2016;48(6):3688–3707. doi: 10.1137/16M1060819
- Kirsch A, Grinberg N. The factorization method for inverse problems. Karlsruhe, Germany: Oxford University Press; 2008.
- McLean W. Strongly elliptic systems and boundary integral equations. Cambridge: Cambridge University Press; 2000.
- Kirsch A, Ritter S. A linear sampling method for inverse scattering from an open arc. Inverse Probl. 2000;16(1):89–105. doi: 10.1088/0266-5611/16/1/308
- Kress R. Inverse scattering from an open arc. Math Methods Appl Sci. 1995;18(4):267–293. doi: 10.1002/mma.1670180403