Geophysical Data Analysis and Inverse Theory with MATLAB® and Python

Geophysical Data Analysis and Inverse Theory with MATLAB® and Python
Author: William Menke
Publisher: Elsevier
Total Pages: 344
Release: 2024-02-22
Genre: Science
ISBN: 0443137951

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

Geophysical Data Analysis: Discrete Inverse Theory

Geophysical Data Analysis: Discrete Inverse Theory
Author: William Menke
Publisher: Academic Press
Total Pages: 273
Release: 2012-12-02
Genre: Science
ISBN: 0323141285

Geophysical Data Analysis: Discrete Inverse Theory is an introductory text focusing on discrete inverse theory that is concerned with parameters that either are truly discrete or can be adequately approximated as discrete. Organized into 12 chapters, the book's opening chapters provide a general background of inverse problems and their corresponding solution, as well as some of the basic concepts from probability theory that are applied throughout the text. Chapters 3-7 discuss the solution of the canonical inverse problem, that is, the linear problem with Gaussian statistics, and discussions on problems that are non-Gaussian and nonlinear are covered in Chapters 8 and 9. Chapters 10-12 present examples of the use of inverse theory and a discussion on the numerical algorithms that must be employed to solve inverse problems on a computer. This book is of value to graduate students and many college seniors in the applied sciences.

Environmental Data Analysis with MatLab or Python

Environmental Data Analysis with MatLab or Python
Author: William Menke
Publisher: Academic Press
Total Pages: 466
Release: 2022-08-14
Genre: Science
ISBN: 0323955770

Environmental Data Analysis with MATLAB, Third Edition, is a new edition that expands fundamentally on the original with an expanded tutorial approach, more clear organization, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. The work teaches the basics of the underlying theory of data analysis and then reinforces that knowledge with carefully chosen, realistic scenarios, including case studies in each chapter. The new edition is expanded to include applications to Python, an open source software environment. Significant content in Environmental Data Analysis with MATLAB, Third Edition is devoted to teaching how the programs can be effectively used in an environmental data analysis setting. This new edition offers chapters that can both be used as self-contained resources or as a step-by-step guide for students, and is supplemented with data and scripts to demonstrate relevant use cases. - Provides a clear learning path for researchers and students using data analysis techniques which build upon one another, choosing the right order of presentation to substantially aid the reader in learning material - Includes crib sheets to summarize the most important data analysis techniques, results, procedures, and formulas and worked examples to demonstrate techniques - Uses real-world environmental examples and case studies formulated using the readily-available software environment in both MATLAB® and Python - Completely updated and expanded to include coverage of Python and reorganized for better navigability - Includes access to both an instructor site with exemplary lectures and solutions to problems and a supplementary site with MATLAB LiveScripts and Python Notebooks

Environmental Data Analysis with MatLab

Environmental Data Analysis with MatLab
Author: William Menke
Publisher: Elsevier
Total Pages: 282
Release: 2011-09-02
Genre: Computers
ISBN: 0123918863

"Environmental Data Analysis with MatLab" is for students and researchers working to analyze real data sets in the environmental sciences. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often-noisy data drawn from a broad range of sources. This book teaches the basics of the underlying theory of data analysis, and then reinforces that knowledge with carefully chosen, realistic scenarios. MatLab, a commercial data processing environment, is used in these scenarios; significant content is devoted to teaching how it can be effectively used in an environmental data analysis setting. The book, though written in a self-contained way, is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial. It is well written and outlines a clear learning path for researchers and students. It uses real world environmental examples and case studies. It has MatLab software for application in a readily-available software environment. Homework problems help user follow up upon case studies with homework that expands them.

Parameter Estimation and Inverse Problems

Parameter Estimation and Inverse Problems
Author: Richard C. Aster
Publisher: Elsevier
Total Pages: 406
Release: 2018-10-16
Genre: Science
ISBN: 0128134232

Parameter Estimation and Inverse Problems, Third Edition, is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who do not have an extensive mathematical background. The book is complemented by a companion website that includes MATLAB codes that correspond to examples that are illustrated with simple, easy to follow problems that illuminate the details of particular numerical methods. Updates to the new edition include more discussions of Laplacian smoothing, an expansion of basis function exercises, the addition of stochastic descent, an improved presentation of Fourier methods and exercises, and more. - Features examples that are illustrated with simple, easy to follow problems that illuminate the details of a particular numerical method - Includes an online instructor's guide that helps professors teach and customize exercises and select homework problems - Covers updated information on adjoint methods that are presented in an accessible manner

Geophysical Inversion

Geophysical Inversion
Author: J. Bee Bednar
Publisher: SIAM
Total Pages: 472
Release: 1992-01-01
Genre: Science
ISBN: 9780898712735

This collection of papers on geophysical inversion contains research and survey articles on where the field has been and where it's going, and what is practical and what is not. Topics covered include seismic tomography, migration and inverse scattering.

Inverse Theory and Applications in Geophysics

Inverse Theory and Applications in Geophysics
Author: Michael S. Zhdanov
Publisher: Elsevier
Total Pages: 731
Release: 2015-07-15
Genre: Science
ISBN: 044462712X

Geophysical Inverse Theory and Applications, Second Edition, brings together fundamental results developed by the Russian mathematical school in regularization theory and combines them with the related research in geophysical inversion carried out in the West. It presents a detailed exposition of the methods of regularized solution of inverse problems based on the ideas of Tikhonov regularization, and shows the different forms of their applications in both linear and nonlinear methods of geophysical inversion. It's the first book of its kind to treat many kinds of inversion and imaging techniques in a unified mathematical manner.The book is divided in five parts covering the foundations of the inversion theory and its applications to the solution of different geophysical inverse problems, including potential field, electromagnetic, and seismic methods. Unique in its focus on providing a link between the methods used in gravity, electromagnetic, and seismic imaging and inversion, it represents an exhaustive treatise on inversion theory.Written by one of the world's foremost experts, this work is widely recognized as the ultimate researcher's reference on geophysical inverse theory and its practical scientific applications. - Presents state-of-the-art geophysical inverse theory developed in modern mathematical terminology—the first to treat many kinds of inversion and imaging techniques in a unified mathematical way - Provides a critical link between the methods used in gravity, electromagnetic, and seismic imaging and inversion, and represents an exhaustive treatise on geophysical inversion theory - Features more than 300 illustrations, figures, charts and graphs to underscore key concepts - Reflects the latest developments in inversion theory and applications and captures the most significant changes in the field over the past decade

Introduction to Python in Earth Science Data Analysis

Introduction to Python in Earth Science Data Analysis
Author: Maurizio Petrelli
Publisher: Springer Nature
Total Pages: 229
Release: 2021-09-16
Genre: Science
ISBN: 3030780554

This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.

Discrete Inverse Problems

Discrete Inverse Problems
Author: Per Christian Hansen
Publisher: SIAM
Total Pages: 220
Release: 2010-01-01
Genre: Mathematics
ISBN: 089871883X

This book gives an introduction to the practical treatment of inverse problems by means of numerical methods, with a focus on basic mathematical and computational aspects. To solve inverse problems, we demonstrate that insight about them goes hand in hand with algorithms.

Advances in Network Electrophysiology

Advances in Network Electrophysiology
Author: Makoto Taketani
Publisher: Springer Science & Business Media
Total Pages: 488
Release: 2006-11-22
Genre: Medical
ISBN: 0387258582

Advances in Network Electrophysiology: Using Multi Electrode Arrays explores methods for using electrophysiological techniques for monitoring the concurrent activity of ensembles of single neurons. It reviews the recent progress in both electronics and computational tools developed to analyze the functional operations of large ensembles of neurons using multi-electrode arrays and in vitro preparations. In addition, it gives readers a sense of the applications made possible by these technological tools. This volume is the reference for researchers, industry, graduate students, and postdoctoral fellows in all areas of neuroscience, cognitive neuroscience, pharmaceutical science, and bioengineering.