Essentials of Geophysical Data Processing

A concise introduction to geophysical data processing - many of the techniques associated with the general field of time series analysis - for advanced students, researchers, and professionals. The textbook begins with calculus before transitioning to discrete time series via the sampling theorem, aliasing, use of complex sinusoids, development of the discrete Fourier transform from the Fourier series, and an overview of linear digital filter types and descriptions. Aimed at senior undergraduate and graduate students in geophysics, environmental science, and engineering with no previous background in linear algebra, probability, or statistics, this textbook draws scenarios and datasets from across the world of geophysics, and shows how data processing techniques can be applied to real-world problems using detailed examples, illustrations, and exercises (using MATLAB or similar computing environment). Online supplementary resources include datasets for students, and a solutions manual and all the figures from the book as PowerPoints for course instructors.

• Covers the key contents for a signal processing course emphasizing exploration geophysics, where reflection seismology is a main application
• Doesn't assume a background in linear algebra, probability, statistics and Fourier transforms - the three appendices at the end of the book cover these basics
• Draws scenarios and datasets from across the world of geophysics and shows how data processing techniques can be applied to real-world problems using detailed examples and illustrations
• Homework exercises are related to reflection seismology, climate time series, and other sciences to illustrate the diversity of applications. Most are computational and can be completed in MATLAB or a similar computing environment.