Global Optimization Methods in Geophysical Inversion
eBook - PDF

Global Optimization Methods in Geophysical Inversion

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Global Optimization Methods in Geophysical Inversion

About this book

Providing an up-to-date overview of the most popular global optimization methods used in interpreting geophysical observations, this new edition includes a detailed description of the theoretical development underlying each method and a thorough explanation of the design, implementation and limitations of algorithms. New and expanded chapters provide details of recently developed methods, such as the neighborhood algorithm, particle swarm optimization, hybrid Monte Carlo and multi-chain MCMC methods. Other chapters include new examples of applications, from uncertainty in climate modeling to whole Earth studies. Several different examples of geophysical inversion, including joint inversion of disparate geophysical datasets, are provided to help readers design algorithms for their own applications. This is an authoritative and valuable text for researchers and graduate students in geophysics, inverse theory and exploration geoscience, and an important resource for professionals working in engineering and petroleum exploration.

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Yes, you can access Global Optimization Methods in Geophysical Inversion by Mrinal K. Sen,Paul L. Stoffa in PDF and/or ePUB format, as well as other popular books in Physical Sciences & Geophysics. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover
  2. Global Optimization Methods in Geophysical Inversion
  3. Title
  4. Copyright
  5. Contents
  6. Preface to the first edition (1995)
  7. Preface to the second edition (2013)
  8. 1 Preliminary statistics
  9. 2 Direct, linear, and iterative-linear inverse methods
  10. 3 Monte Carlo methods
  11. 4 Simulated annealing methods
  12. 5 Genetic algorithms
  13. 6 Other stochastic optimization methods
  14. 7 Geophysical applications of simulated annealing and genetic algorithms
  15. 8 Uncertainty estimation
  16. Bibliography
  17. Index