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Geophysical Data Analysis: Discrete Inverse Theory, Vol 45. Edition No. 3. International Geophysics

• ID: 2088888
• Book
• August 2016
• 330 Pages
• Elsevier Science and Technology
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Since 1984, Geophysical Data Analysis has filled the need for a short, concise reference on inverse theory for individuals who have an intermediate background in science and mathematics. The new edition maintains the accessible and succinct manner for which it is known, with the addition of:

- MATLAB examples and problem sets
- Coverage of new topics, including Adjoint Methods; Inversion by Steepest Descent, Monte Carlo and Simulated Annealing methods; and Bootstrap algorithm for determining empirical confidence intervals

- Additional material on probability, including Bayesian influence, probability density function, and metropolis algorithm
- Detailed discussion of application of inverse theory to tectonic, gravitational and geomagnetic studies
- Numerous examples and end-of-chapter homework problems help you explore and further understand the ideas presented
- Use as classroom text facilitated by a complete set of exemplary lectures in Microsoft PowerPoint format and homework problem solutions for instructors
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Preface Introduction Chapter 1: Describing Inverse Problems Chapter 2: Some Comments on Probability Theory Chapter 3: Solution of the Linear, Gaussian Inverse Problem, Viewpoint 1: The Length Method Chapter 4: Solution of the Linear, Gaussian Inverse Problem, Viewpoint 2: Generalized Inverses Chapter 5: Solution of the Linear, Gaussian Inverse Problem, Viewpoint 3: Maximum Likelihood Methods Chapter 6: Nonuniqueness and Localized Averages Chapter 7: Applications of Vector Spaces Chapter 8: Linear Inverse Problems and Non-Gaussian Statistics Chapter 9: Nonlinear Inverse Problems Chapter 10: Factor Analysis Chapter 11: Continuous Inverse Theory and Tomography Chapter 12: Sample Inverse Problems Chapter 13: Applications of Inverse Theory to Solid Earth Geophysics Appendices

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Menke, William
William Menke is a Professor of Earth and Environmental Sciences at Columbia University, USA. 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.
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