Автор: William Menke
Издательство: Academic Press, Elsevier
Год: 2024
Страниц: 344
Язык: английский
Формат: pdf (true)
Размер: 29.7 MB
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.
Includes material on probability, including Bayesian influence, probability density function, and metropolis algorithm
Offers detailed discussions of the application of inverse theory to seismological, gravitational, and tectonic studies
Provides numerous examples, color figures, and end-of-chapter problems to help readers explore and further understand the presented ideas
Includes both MATLAB and Python examples and problem sets
MATLAB and Python scripts are used throughout the book as a means of communicating how the formulas of inverse theory can be used in computer-based data processing scenarios. MATLAB is a commercial software product of The MathWorks, Inc. Python and its many add-ons, such as the NumPy linear algebra package, the SciPy scientific function package, and the Jupyter Notebook/JupyterLab environment, are freeware that offer an alternative to MATLAB and other commercial products with license fees. Both are widely used in university settings as an environment for scientific computing.
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