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E-raamat: Inverse Problems: Basics, Theory and Applications in Geophysics

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The overall goal of the book is to provide access to the regularized solution of inverse problems relevant in geophysics without requiring more mathematical knowledge than is taught in undergraduate math courses for scientists and engineers. From abstract analysis only the concept of functions as vectors is needed. Function spaces are introduced informally in the course of the text, when needed. Additionally, a more detailed, but still condensed introduction is given in Appendix B.A second goal is to elaborate the single steps to be taken when solving an inverse problem: discretization, regularization and practical solution of the regularized optimization problem. These steps are shown in detail for model problems from the fields of inverse gravimetry and seismic tomography.The intended audience is mathematicians, physicists and engineers having a good working knowledge of linear algebra and analysis at the upper undergraduate level.

1.Characterization of Inverse Problems.- 2.Discretization of Inverse Problems.- 3.Regularization of Linear Inverse Problems.- 4.Regularization of Nonlinear Inverse Problems.- Appendix: A.Results from Linear Algebra.- B.Function Spaces.- C.The Fourier Transform.- D.Proofs of Theorems from Chapter 3.
1 Characterization of Inverse Problems
1(28)
1.1 Examples of Inverse Problems
1(10)
1.2 Ill-Posed Problems
11(6)
1.3 Model Problems for Inverse Gravimetry
17(4)
1.4 Model Problems for Seismic Tomography
21(8)
2 Discretization of Inverse Problems
29(48)
2.1 Approximation of Functions
30(7)
2.2 Discretization of Linear Problems by Least Squares Methods
37(9)
2.3 Discretization of Fredholm Equations by Collocation Methods
46(5)
2.4 The Backus-Gilbert Method and the Approximative Inverse
51(8)
2.5 Discrete Fourier Inversion of Convolutional Equations
59(6)
2.6 Discretization of Nonlinear Model Problems
65(12)
3 Regularization of Linear Inverse Problems
77(80)
3.1 Linear Least Squares Problems
77(3)
3.2 Sensitivity Analysis of Linear Least Squares Problems
80(11)
3.3 The Concept of Regularization
91(8)
3.4 Tikhonov Regularization
99(12)
3.5 Discrepancy Principle
111(9)
3.6 Reduction of Least Squares Regularization to Standard Form
120(6)
3.7 Regularization of the Backus-Gilbert Method
126(3)
3.8 Regularization of Fourier Inversion
129(4)
3.9 Landweber Iteration and the Curve of Steepest Descent
133(13)
3.10 The Conjugate Gradient Method
146(11)
4 Regularization of Nonlinear Inverse Problems
157(38)
4.1 Tikhonov Regularization of Nonlinear Problems
157(6)
4.2 Tikhonov Regularization for Nonlinear Inverse Gravimetry
163(6)
4.3 Nonlinear Least Squares Problems
169(4)
4.4 Computation of Derivatives by the Adjoint Method
173(6)
4.5 Tikhonov Regularization for Nonlinear Seismic Tomography
179(7)
4.6 Iterative Regularization
186(9)
A Results from Linear Algebra 195(8)
B Function Spaces 203(12)
C The Fourier Transform 215(16)
D Regularization Property of CGNE 231(6)
References 237(2)
Index 239