Multiscale denoising of self-similar processes

Published

Journal Article

A practical limitation to investigating self-similarity in geophysical phenomena from their measured state variables is that measured signals are typically convolved with instrumentation noise at multiple scales. This study develops and tests a multiscale Bayesian model (BEFE) for separating a 1/f-like signal from inherent instrumentation noise and contrasts its performance to the Wiener-type (WAS) and Fourier amplitude (FAS) shrinkage methods. The novel feature in BEFE is that the separation is performed in the wavelet domain and involves the use of a Bayesian inference approach guided by existing theoretical power laws in the filtered signal energy spectrum. We contrast the performance of all three methods for synthetic fractional Brownian motion (fBm) signals and turbulent velocity time series collected in the atmospheric boundary layer. Differences between BEFE and WAS were minor except for the spectral properties at low signal-to-noise ratios and at the finest levels of details in which the filtered signal spectra by BEFE is more consistent with the spectra of the uncontaminated velocity signal. Copyright 2000 by the American Geophysical Union.

Full Text

Duke Authors

Cited Authors

  • Vidakovic, BD; Katul, GG; Albertson, JD

Published Date

  • November 27, 2000

Published In

Volume / Issue

  • 105 / D22

Start / End Page

  • 27049 - 27058

International Standard Serial Number (ISSN)

  • 0148-0227

Digital Object Identifier (DOI)

  • 10.1029/2000JD900479

Citation Source

  • Scopus