Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements - Softcover

Twomey, S.

Softcover

ISBN 10: 0486832988 ISBN 13: 9780486832982

Publisher: Dover Publications, 2019

Synopsis

In this graduate-level monograph, S. Twomey, a professor of atmospheric sciences, develops the background and fundamental theory of inversion processes used in remote sensing — e.g., atmospheric temperature structure measurements from satellites—starting at an elementary level.
The text opens with examples of inversion problems from a variety of disciplines, showing that the same problem—solution of a Fredholm linear integral equation of the first kind — is involved in every instance. A discussion of the reduction of such integral equations to a system of linear algebraic equations follows. Subsequent chapters examine methods for obtaining stable solutions at the expense of introducing constraints in the solution, the derivation of other inversion procedures, and the detailed analysis of the information content of indirect measurements. Each chapter begins with a discussion that outlines problems and questions to be covered, and a helpful Appendix includes suggestions for further reading.

About this title

About the Author

Sean Twomey (1927–2012) was born in Cork County, Ireland in 1927 and received his academic degrees, including his Ph.D. from the National University of Ireland in Galway. He held research appointments with several institutions in Australia and the United States including the U.S. Weather Bureau and the Naval Research Laboratory, and was a Professor at The University of Arizona's Department of Atmospheric Sciences from 1976 until his retirement in 1990. He was the author of many scientific papers in areas such as atmospheric radiation, aerosol and cloud physics, and inversion mathematics and of another influential textbook, Atmospheric Aerosols, 1979. 

Excerpt. © Reprinted by permission. All rights reserved.

Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements

By S. Twomey

Dover Publications, Inc.

Copyright © 1977 Elsevier Scientific Publishing Company, Amsterdam
All rights reserved.

ISBN: 978-0-486-83298-2

Contents

List of frequently used symbols and their meanings, VII, 
Preface, IX, 
CHAPTER 1. INTRODUCTION, 1, 
CHAPTER 2. SIMPLE PROBLEMS INVOLVING INVERSION, 27, 
CHAPTER 3. THEORY OF LARGE LINEAR SYSTEMS, 37, 
CHAPTER 4. PHYSICAL AND GEOMETRIC ASPECTS OF VECTORS AND MATRICES, 53, 
CHAPTER 5. ALGEBRAIC AND GEOMETRIC ASPECTS OF FUNCTIONS AND FUNCTION SPACE, 83, 
CHAPTER 6. LINEAR INVERSION METHODS, 115, 
CHAPTER 7. FURTHER INVERSION TECHNIQUES, 151, 
CHAPTER 8. INFORMATION CONTENT OF INDIRECT SENSING MEASUREMENTS, 185, 
CHAPTER 9. FURTHER TOPICS, 213, 
APPENDIX, 231, 
1. Determinants, 231, 
2. Matrix properties involving determinants, 232, 
3. Solution by determinants of a linear system of equations, 234, 
Suggestions for further reading, 235, 
Name Index, 239, 
Subject index, 241, 

PREFACE

Inversion problems have existed in various branches of physics and engineering for a long time, but in the past ten or fifteen years they have received far more attention than ever before. The reason, of course, was the arrival on the scene of large computers (which enabled hitherto abstract algebraic concepts such as the solution of linear systems of equations in many unknowns to be achieved arithmetically with real numbers) and the launching of earth-orbiting satellites which viewed the atmosphere from outside and which could only measure atmospheric parameters remotely and indirectly by measuring electromagnetic radiation of some sort and relating this to the atmospheric parameter being sought, very often implying a mathematical inversion problem of some sort.

It was soon found that the dogmas of algebraic theory did not necessarily carry over into the realm of numerical inversion and some of the first numerical inversion s gave results so bad — for example, in one early instance negative absolute temperatures — that the prospects for the remote sensing of atmospheric temperature profiles, water vapor concentration profiles, ozone profiles, and so on, for a time looked very bleak, attractive as they might have appeared when first conceived.

The crux of the difficulty was that numerical inversions were producing results which were physically unacceptable but were mathematically acceptable (in the sense that had they existed they would have given measured values identical or almost identical with what was measured). There were in fact ambiguities — the computer was being "told" to find an f(x) from a set of values for g(y) at prescribed values of y, it was "told" what the mathematical process was which related f(x) and g(y), but it was not "told" that there were many sorts of f(x) — highly oscillatory, negative in some places, or whatever — which, either because of the physical nature of f(x) or because of the way in which direct measurements showed that f(x) usually behaved , would be rejected as impossible or ridiculous by the recipient of the computer's "answer". And yet the computer was often blamed, even though it had done all that had been asked of it and produced an f(x) which via the specified mathematical process led to values of g(y) which were exceedingly close to the initial data for g(y) supplied to the computer. Were it possible for computers to have ulcers or neuroses there is little doubt that most of those on which early numerical inversion attempts were made would have acquired both afflictions.

For a time it was thought that prec1s10n and accuracy in the computer were the core of the problem and more accurate numerical procedures were sought and applied without success. Soon it was realized that the problem was not inaccuracy in calculations, but a fundamental ambiguity in the presence of inevitable experimental inaccuracy — sets of measured quantities differing only minutely from each other could correspond to unknown functions differing very greatly from each other. Thus in the presence of measurement errors (or even in more extreme cases computer roundoff error) there were many, indeed an infinity of possible "solutions". Ambiguity could only be removed if some grounds could be provided, from outside the inversion problem, for selecting one of the possible solutions and rejecting the others. These grounds might be provided by the physical nature of the unknown function, by the likelihood that the unknown function be smooth or that it lie close to some known climatological average. It is important, however, to realize that most physical inversion problems are ambiguous — they do not possess a unique solution and the selection of a preferred unique solution from the infinity of possible solutions is an imposed additional condition. The reasonableness of the imposed condition will dictate whether or not the unique solution is also reasonable. There have been many advances in mathematical procedures for solving inversion problems but these do not remove the fundamental ambiguity (although in some instances they very effectively hide it). Our measurements reduce the number of possible solutions from infinity to a lesser but still infinite selection of possibilities. We rely on some other knowledge (or hope) to select from those a most likely or most acceptable candidate.

S. TWOMEY

(Continues...)

Excerpted from Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements by S. Twomey. Copyright © 1977 Elsevier Scientific Publishing Company, Amsterdam. Excerpted by permission of Dover Publications, Inc..
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Edition details

• Publisher: Dover Publications
• Publication date: 2019
• Language: English
• ISBN 10: 0486832988
• ISBN 13: 9780486832982
• Binding: Paperback
• Number of pages: 256