Geophysical Data Analysis: Volume 45

Geophysical Data Analysis: Volume 45 : Discrete Inverse Theory

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Description

Please use extracts from reviews of first edition
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Product details

  • Hardback | 289 pages
  • 154.94 x 231.14 x 25.4mm | 657.71g
  • Academic Press Inc
  • San Diego, United States
  • English
  • Revised
  • 2nd Revised edition
  • 0124909213
  • 9780124909212
  • 1,763,460

Review quote

"The author has produced a meaningful guide to the subject; one which a student (or professional unfamiliar with the field) can follow without great difficulty and one in which many motivational guideposts are provided....I think that the value of the book is outstanding....It deserves a prominent place on the shelf of every scientist or engineer who has data to interpret."
--GEOPHYSICS
"As a meteorologist, I have used least squares, maximum likelihood, maximum entropy, and empirical orthogonal functions during the course of my work, but this book brought together these somewhat disparate techniques into a coherent, unified package....I recommend it to meteorologists involved with data analysis and parameterization."
--Roland B. Stull, THE BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
"This book provides an excellent introductory account of inverse theory with geophysical applications....My experience in using this book, along with supplementary material in a course for the first year graduate students, has been very positive. I unhesitatingly recommend it to any student or researcher in the geophysical sciences."
--PACEOPH
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Table of contents

Preface.
Introduction.
DESCRIBING INVERSE PROBLEMS
Formulating Inverse Problems.
The Linear Inverse Problem.
Examples of Formulating Inverse Problems.
Solutions to Inverse Problems.
SOME COMMENTS ON PROBABILITY THEORY
Noise and Random Variables.
Correlated Data.
Functions of Random Variables.
Gaussian Distributions.
Testing the Assumption of Gaussian Statistics
Confidence Intervals.
SOLUTION OF THE LINEAR, GAUSSIAN INVERSE PROBLEM, VIEWPOINT 1:THE LENGTH METHOD
The Lengths of Estimates.
Measures of Length.
Least Squares for a Straight Line.
The Least Squares Solution of the Linear Inverse Problem.
Some Examples.
The Existence of the Least Squares Solution.
The Purely Underdetermined Problem.
Mixed*b1Determined Problems.
Weighted Measures of Length as a Type of A Priori Information.
Other Types of A Priori Information.
The Variance of the Model Parameter Estimates.
Variance and Prediction Error of the Least Squares Solution.
SOLUTION OF THE LINEAR, GAUSSIAN INVERSE PROBLEM, VIEWPOINT 2: GENERALIZED INVERSES
Solutions versus Operators.
The Data Resolution Matrix.
The Model Resolution Matrix.
The Unit Covariance Matrix.
Resolution and Covariance of Some Generalized Inverses.
Measures of Goodness of Resolution and Covariance.
Generalized Inverses with Good Resolution and Covariance.
Sidelobes and the Backus-Gilbert Spread Function.
The Backus-Gilbert Generalized Inverse for the Underdetermined Problem.
Including the Covariance Size.
The Trade-off of Resolution and Variance.
SOLUTION OF THE LINEAR, GAUSSIAN INVERSE PROBLEM, VIEWPOINT 3: MAXIMUM LIKELIHOOD METHODS
The Mean of a Group of Measurements.
Maximum Likelihood Solution of the Linear Inverse Problem.
A Priori Distributions.
Maximum Likelihood for an Exact Theory.
Inexact Theories.
The Simple Gaussian Case with a Linear Theory.
The General Linear, Gaussian Case.
Equivalence of the Three Viewpoints.
The F Test of Error Improvement Significance.
Derivation of the Formulas of Section 5.7.
NONUNIQUENESS AND LOCALIZED AVERAGES
Null Vectors and Nonuniqueness.
Null Vectors of a Simple Inverse Problem.
Localized Averages of Model Parameters.
Relationship to the Resolution Matrix.
Averages versus Estimates.
Nonunique Averaging Vectors and A Priori Information.
APPLICATIONS OF VECTOR SPACES
Model and Data Spaces.
Householder Transformations.
Designing Householder Transformations.
Transformations That Do Not Preserve Length.
The Solution of the Mixed-Determined Problem.
Singular-Value Decomposition and the Natural Generalized Inverse.
Derivation of the Singular-Value Decomposition.
Simplifying Linear Equality and Inequality Constraints.
Inequality Constraints.
LINEAR INVERSE PROBLEMS AND NON-GAUSSIAN DISTRIBUTIONS
L1 Norms and Exponential Distributions.
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