Two numerical methods for an inverse problem for the 2-D Helmholtz equation

Two solution methods for the inverse problem for the 2-D Helmholtz equation are developed, tested, and compared. The proposed approaches are based on a marching finite-difference scheme which requires the solution of an overdetermined system at each step. The preconditioned conjugate gradient method is used for rapid solutions of these systems and an efficient preconditioner has been developed for this class of problems. Underlying target applications include the imaging of land mines, unexploded ordinance, and pollutant plumes in environmental cleanup sites, each formulated as an inverse problem for a 2-D Helmholtz equation. The images represent the electromagnetic properties of the respective underground regions. Extensive numerical results are presented.

[1]  Yuriy A. Gryazin,et al.  Numerical Solution of a Subsurface Imaging Inverse Problem , 2001, SIAM J. Appl. Math..

[2]  Jack Dongarra,et al.  1. High-Performance Computing , 1998 .

[3]  E. Miller,et al.  A shape reconstruction method for electromagnetic tomography using adjoint fields and level sets , 2000 .

[4]  L. Delves,et al.  Computational methods for integral equations: Frontmatter , 1985 .

[5]  I. Duff,et al.  The state of the art in numerical analysis , 1997 .

[6]  David Isaacson,et al.  Inverse problems for a perturbed dissipative half-space , 1995 .

[7]  F. Natterer,et al.  A propagation-backpropagation method for ultrasound tomography , 1995 .

[8]  J. A. Buck,et al.  Engineering Electromagnetics , 1967 .

[9]  Yuriy A. Gryazin,et al.  Imaging the diffusion coefficient in a parabolic inverse problem in optical tomography , 1999 .

[10]  Yuriy A. Gryazin,et al.  GMRES Computation of High Frequency Electrical Field Propagation in Land Mine Detection , 2000 .

[11]  J. Pasciak,et al.  Computer solution of large sparse positive definite systems , 1982 .

[12]  Margaret Cheney,et al.  Three-dimensional inverse scattering for the wave equation: weak scattering approximations with error estimates , 1988 .

[13]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[14]  T. R. Lucas,et al.  A fast and accurate imaging algorithm in optical/diffusion tomography , 1997 .

[15]  P. L. T. Brian,et al.  A finite‐difference method of high‐order accuracy for the solution of three‐dimensional transient heat conduction problems , 1961 .

[16]  Yousef Saad,et al.  Iterative methods for sparse linear systems , 2003 .

[17]  Y. Saad,et al.  GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems , 1986 .

[18]  S. Arridge Optical tomography in medical imaging , 1999 .

[19]  Alan George,et al.  Computer Solution of Large Sparse Positive Definite , 1981 .

[20]  Gene H. Golub,et al.  Matrix computations , 1983 .

[21]  Jack Dongarra,et al.  Numerical Linear Algebra for High-Performance Computers , 1998 .

[22]  MICHAEL V. KLIBANOV,et al.  Numerical Solution of a Parabolic Inverse Problem in Optical Tomography Using Experimental Data , 1999, SIAM J. Appl. Math..