Long-Range Persistence in Geophysical Time Series

Edited by

  • Renata Dmowska, Harvard University, Cambridge, MA, USA
  • Barry Saltzman, Yale University, New Haven, Connecticut, U.S.A.

Advances in Geophysics, Vol. 40 systematically compares many of the currently used statistical approaches to time series analysis and modeling to evaluate each method's robustness and application to geophysical datasets. This volume tackles the age-old problem of how to evaluate the relative roles of deterministic versus stochastic processes (signal vs noise) in their observations. The book introduces the fundamentals in sections titled "1.2 What is a Time Series? " and "1.3 How is a Time Series Quantified?", before diving into Spectral Analysis, Semivariograms, Rescaled-Range Analysis and Wavelet Analysis. The second half of the book applies their self-affine analysis to a number of geophysical time series (historical temperature records, drought hazard assessment, sedimentation in the context of hydrocarbon bearing strata, variability of the Earth's magnetic field).This volume explores in detail one of the main components of noise, that of long-range persistence or memory. The first chapter is a broad summary of theory and techniques of long-range persistence in time series; the second chapter is the application of long-range persistence to a variety of geophysical time series.
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Libraries as well as academics and professionals in all areas of geosciences, including geophysicists, geologists, hydrologists, climate modelers, oceanographers, petroleum explorationists, and others.


Book information

  • Published: June 1999
  • ISBN: 978-0-12-018840-6


"This series has provided workers in many fields with invaluable reference material and criticism."
Praise for the Series, --SCIENCE PROGRESS

"Should be on the bookshelf of every geophysicist."
"The entire series should be in the library of every group working in geophysics."

Table of Contents

B.D. Malamud and D.L. Turcotte, Self-Affine Time Series: Generation and Analysis. J.D. Pelletier and D.L. Turcotte, Self-Affine Time Series: Applications and Models.