EOS550: Course Outline
In the physical sciences we are often confronted with the task of determining
the distribution of a physical property (e.g. density, velocity, conductivity,
susceptibility etc.) in a volume without sampling the volume directly.
An experiment is preformed in which observations are collected. The goal
is to "invert" these observations to obtain information about the model
the distribution of the physical parameter of interest).
Linear Inverse Theory: This course presents the fundamentals of formulating and solving linear inverse
problems when the model to be recovered is either a function or a vector of parameters. There is an emphasis on
understanding the basic difficulties involved in solving any inverse problem, and on practical methodologies for
obtaining answers. Topics include: constructing models which acceptably fit the data, incorporating various forms
of a priori information, singular value decomposition solutions, and assessing nonuniqueness. Additionally there
will be an introduction to basic elements of solving nonlinear inverse problems and parameter estimation problems.
A majority of the assigned class problems will be solved with MATLAB, and a tomographic inverse problem is used
as a theme throughout the course. Practical examples will be drawn from various areas of geophysics. Students
are encouraged to bring their own inverse problems to add diversification and relevance.
Prerequisites: Solid background in linear algebra. Students are assumed to have completed a B.Sc in
a scientific discipline. Please contact Dr. Doug Oldenburg (Dept of Earth and Ocean Sciences, 822–5406, doug@eos.ubc.ca).
Outline of topics:
 Overview to linear inverse theory
 Review of essential mathematics
 Minimum norm construction using accurate
 Discretization and Singular Value Decomposition
 Linear inverse problems with inaccurate
 Choosing a regularization parameter: (GCV,
 Basics for solving nonlinear inverse problems
 Tuning the inversion algorithm: applications
 Calculation of sensitivities and Frechet derivatives
 Inverse problems with partial differential
 General measures of misfit and model norm
 Parameter estimation and uncertainty anlaysis
 Resolution analysis and uncertainty in inverse
See also the expanded syllabus (PDF format).
Grading:
 Problem Assignments: 50%
 Project: 50%
Deadlines:

Feb 21: A 1–page description about proposed project.

April 11: final project due.
References
Inverse Theory
 R.L.Parker, 1994, Geophysical Inverse Theory, Princeton Academic Press
 P.C. Hansen, 1998, RankDeficient and Discrete Illposed Problems, SIAM
 J.Scales and M.Smith, Geophysical Inverse Theory. (//samizdat.mines.edu/
 C.L.Lawson and R.J.Hanson, 1974, Solving Least Squares Problems. Prentice_hall, Englewood Cliffs, New
Jersey
 A. Tarantola, 1987, Inverse Problem Theory: Methods for Data Fitting and Model Parameter Estimation,
Elsevier
 A.N.Tikhonov and V.Y.Arsenin. Solutions of IllPosed Problems. John Wiley, New York, 1977.
 C.Vogel, 2002, Computational Methods for Inverse Problems, SIAM
 W. Menke, Geophysical Data Analysis: Revised Edition, volume 45 of International Geophysics Series.
Academic Press, San Diego, 1989.
 R. Aster, B. Borchers, and C. Thurber, Parameter Estimation and Inverse Problems, in press (I have a copy)
 D.W.Oldenburg, Y. Li, F.Jones, Inversion for Applied Geophysics, UBCGeophysical Inversion Facility, (betatest
version available on CDROM)
 E. Haber, 1997, Numerical Strategies for the Solution of Inverse Problems, PhD thesis, Institute for Applied
Mathematics, UBC. (www.iam.ubc.ca/theses/index.html)
Optimization and Numerical Solutions
 J.E. Dennis and R.B.Schnabel, 1996, Numerical Methods for Unconstrained Optimization and Nonlinear
Equations. SIAM
 C.T.Kelley, 1999, Iterative Methods for Optimization, SIAM
 D. Luenberger, 1969, Optimization by Vector Space Methods. John Wiley and Sons.
 M. Hestenes., 1980, Conjugate Direction Methods in Optimization. SpringerVerlag.
 J. Nocedal, and S.J.Wright, 1999, Numerical Optimization, Springer
 P.Gill, W.Murray and M.Wright., 1981, Practical Optimization: Academic Press.
 Y.Saad, 1996, Iterative Methods for Sparse Linear Systems, PWS Publishing Co.
Websites
 UBCGeophysical Inversion Facility: (www.eos.ubc.ca/ubcgif)
 Neos Guide: (www.mcs.anl.gov/otc/Guide/)
 Per Christian Hansen (www.imm.dtu.dk/~pch) (theoretical background and downloadable matlab files)
 C.T.Kelley (www4.ncsu.edu/eos/users/c/ctkelley/www/tim.html)
 Curt Vogel (http://www.math.montana.edu/~vogel)
 H.Mittelmann and P.Spellucci (http://plato.la.asu.edu/guide.html)
 Matlab Optimization Toolbox (http://www.mathworks.com) (Contains codes and documentation)