Course Syllabus

Class Meets Tue-Thu, 9:00-10:15 pm in Distance Ed 209

Resources:

Matlab scripts from Menke

Assignments:

Due 3 September at 9:00am: Assignment 1 & the data

PowerPoint Lectures:

29 August: Introduction/Motivation for Inverse Theory 31 August: Introduction to Linear Inversion; Minimization of the L2 Norm 5 September: Ordinary Least Squares; the Pseudoinverse 7 September: Gaussian Statistics and Model Parameter Error (OLS) 12 September: Parameter Uncertainty: the Chi-Square Parameter; Solution Appraisal 14 September: Solution Appraisal; Weighted Least Squares 19 September: Overparameterized Inversion: SVD & the Generalized Inverse 21 September: SVD & The Generalized Inverse 26 September: The Generalized Inverse and Resolution/Variance Tradeoffs 28 September: Damped Least Squares 3 October: Maximum Likelihood 5 October: Nonlinear Inversion 10 October: Nonlinear Inversion: Gradient (Gauss-Newton) Approach; Stabilization 12 October: Solution Appraisal for Nonlinear Inversion 17 October: Simulated Annealing 19 October: Linear Programming 24 October: Review of Assignment 1; SVD for (L2) Constrained Problems 26 October: Quadratic Programming; Inequality Constraints For Nonlinear Problems 31 October: Stochastic Inversion 2 November: Stochastic Inversion & Gravity Example 7 November: Stochastic Inversion (Special Cases); Joint Inversion 9 November: Likelihood Filtering Example 14 November: Bayesian Inversion 16 November: Optimal Interpolation (Kriging) 21 November: Tikhonov Regularization 28 November: Example applications using cross-correlation observations 30 November: Example applications using cross-correlation observations; Assignment 2