Geophysical Data Analysis and Inverse Theory with MATLAB� and Python

William Menke is a Professor of Earth and Environmental Sciences at Columbia University. His research focuses on the development of data analysis algorithms for time series analysis and imaging in the earth and environmental sciences and the application of these methods to volcanoes, earthquakes, and other natural hazards. He has thirty years of experience teaching data analysis methods to both undergraduates and graduate students. Relevant courses that he has taught include, at the undergraduate level, Environmental Data Analysis and The Earth System, and at the graduate level, Geophysical Inverse Theory, Quantitative Methods of Data Analysis, Geophysical Theory and Practical Seismology.

• 1. Getting started with Matlab® or python2. Describing inverse problems3. Using probabilty to describe random variation4. Solution of the linear, normal inverse problem, viewpoint 1: the length method5. Solution of the linear, normal inverse problem, viewpoint 2: generalized inverses6. Solution of the linear, normal inverse problem, viewpoint 3: maximum likelihood methods7. Data assimilation methods including gaussian process regression and kalman filtering8. Nonuniqueness and localized averages9. Applications of vector spaces10. Linear inverse problems with non-normal statistics11. Nonlinear inverse problems12. Monte carlo methods13. Factor analysis14. Continuous inverse theory and tomography15. Sample inverse problems16. Applications of inverse theory to solid earth geophysics17. Important algorithms and method summaries