English
资源说明：This textbook evolved from a course in geophysical inverse methods taught during the
past two decades at New Mexico Tech, first by Rick Aster and, subsequently, jointly
between Rick Aster and Brian Borchers. The audience for the course has included a
broad range of first- or second-year graduate students (and occasionally advanced under-
graduates) from geophysics, hydrology, mathematics, astrophysics, and other disciplines.
Cliff Thurber joined this collaboration during the production of the first edition and
has taught a similar course at the University of Wisconsin-Madison.
Our principal goal for this text is to promote fundamental understanding of param-
eter estimation and inverse problem philosophy and methodology, specifically regarding
such key issues as uncertainty, ill-posedness, regularization, bias, and resolution. We
emphasize theoretical points with illustrative examples, and MATLAB codes that imple-
ment these examples are provided on a companion website. Throughout the examples
and exercises, a web icon indicates that there is additional material on the website.
Exercises include a mix of applied and theoretical problems.
This book has necessarily had to distill a tremendous body of mathematics and
science going back to (at least) Newton and Gauss. We hope that it will continue to
find a broad audience of students and professionals interested in the general problem of
estimating physical models from data. Because this is an introductory text surveying a
very broad field, we have not been able to go into great depth. However, each chapter
has a “notes and further reading” section to help guide the reader to further explo-
ration of specific topics. Where appropriate, we have also directly referenced research
contributions to the field.
Some advanced topics have been deliberately left out of this book because of space
limitations and/or because we expect that many readers would not be sufficiently famil-
iar with the required mathematics. For example, readers with a strong mathematical
background may be surprised that we primarily consider inverse problems with discrete
data and discretized models. By doing this we avoid much of the technical complexity of
functional analysis. Some advanced applications and topics that we have omitted include
inverse scattering problems, seismic diffraction tomography, wavelets, data assimilation,
simulated annealing, and expectation maximization methods.
We expect that readers of this book will have prior familiarity with calculus, dif-
ferential equations, linear algebra, probability, and statistics at the undergraduate level.
In our experience, many students can benefit from at least a review of these topics, and
we commonly spend the first two to three weeks of the course reviewing material from
本源码包内暂不包含可直接显示的源代码文件，请下载源码包。
