Geophysical Data Analysis and Inverse Theory with MATLAB or Python, Fifth Edition is a revised and expanded introduction to inverse theory and tomography as it is practiced by geophysicists. The book demonstrates the methods needed to analyze a broad spectrum of geophysical datasets, with special attention given to those methods that generate images of the earth. Data analysis can be a mathematically complex activity, but the treatment in this volume is carefully designed to emphasize those mathematical techniques that readers will find the most familiar and to systematically introduce less-familiar ones. A series of "crib sheets" offer step-by-step summaries of methods presented. Utilizing problems and case studies, along with MATLAB and Python computer code and summaries of methods, the book provides professional geophysicists, students, data scientists and engineers in geophysics with the tools necessary to understand and apply mathematical techniques and inverse theory.
1. Geophysical Data Analysis and Inverse Theory with MATLAB® and Python - p. I 2. Cover - p. I 3. Title Page - p. iii 4. Copyright Page - p. v 5. Table of Contents - p. v 6. Dedication - p. vi 7. Preface - p. vii 8. Chapter 1 Getting started with MATLAB® or Python - p. 1 9. Chapter 2 Describing inverse problems - p. 33 10. Chapter 3 Using probability to describe random variation - p. 49 11. Chapter 4 Solution of the linear, Normal inverse problem, viewpoint 1: The length method - p. 67 12. Chapter 5 Solution of the linear, Normal inverse problem, viewpoint 2: Generalized inverses - p. 95 13. Chapter 6 Solution of the linear, Normal inverse problem, viewpoint 3: Maximum likelihood methods - p. 113 14. Chapter 7 Data assimilation methods including Gaussian process regression and Kalman filtering - p. 135 15. Chapter 8 Nonuniqueness and localized averages - p. 151 16. Chapter 9 Applications of vector spaces - p. 159 17. Chapter 10 Linear inverse problems with non-Normal statistics - p. 179 18. Chapter 11 Nonlinear inverse problems - p. 197 19. Chapter 12 Monte Carlo methods - p. 223 20. Chapter 13 Factor analysis - p. 235 21. Chapter 14 Continuous inverse theory and tomography - p. 249 22. Chapter 15 Sample inverse problems - p. 273 23. Chapter 16 Applications of inverse theory to solid earth geophysics - p. 297 24. Chapter 17 Important algorithms and method summaries - p. 317 25. Index - p. 327
Geophysical Data Analysis and Inverse Theory with MATLAB or Python, Fifth Edition is a revised and expanded introduction to inverse theory and tomography as it is practiced by geophysicists. The book demonstrates the methods needed to analyze a broad spectrum of geophysical datasets, with special attention given to those methods that generate images of the earth. Data analysis can be a mathematically complex activity, but the treatment in this volume is carefully designed to emphasize those mathematical techniques that readers will find the most familiar and to systematically introduce less-familiar ones. A series of "crib sheets" offer step-by-step summaries of methods presented. Utilizing problems and case studies, along with MATLAB and Python computer code and summaries of methods, the book provides professional geophysicists, students, data scientists and engineers in geophysics with the tools necessary to understand and apply mathematical techniques and inverse theory.
1. Geophysical Data Analysis and Inverse Theory with MATLAB® and Python - p. I 2. Cover - p. I 3. Title Page - p. iii 4. Copyright Page - p. v 5. Table of Contents - p. v 6. Dedication - p. vi 7. Preface - p. vii 8. Chapter 1 Getting started with MATLAB® or Python - p. 1 9. Chapter 2 Describing inverse problems - p. 33 10. Chapter 3 Using probability to describe random variation - p. 49 11. Chapter 4 Solution of the linear, Normal inverse problem, viewpoint 1: The length method - p. 67 12. Chapter 5 Solution of the linear, Normal inverse problem, viewpoint 2: Generalized inverses - p. 95 13. Chapter 6 Solution of the linear, Normal inverse problem, viewpoint 3: Maximum likelihood methods - p. 113 14. Chapter 7 Data assimilation methods including Gaussian process regression and Kalman filtering - p. 135 15. Chapter 8 Nonuniqueness and localized averages - p. 151 16. Chapter 9 Applications of vector spaces - p. 159 17. Chapter 10 Linear inverse problems with non-Normal statistics - p. 179 18. Chapter 11 Nonlinear inverse problems - p. 197 19. Chapter 12 Monte Carlo methods - p. 223 20. Chapter 13 Factor analysis - p. 235 21. Chapter 14 Continuous inverse theory and tomography - p. 249 22. Chapter 15 Sample inverse problems - p. 273 23. Chapter 16 Applications of inverse theory to solid earth geophysics - p. 297 24. Chapter 17 Important algorithms and method summaries - p. 317 25. Index - p. 327
