Testing linear models of sea-floor topography View Full Text


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Article Info

DATE

1989-03

AUTHORS

Alberto Malinverno

ABSTRACT

Stochastic models can generate profiles that resemble topography by taking uncorrelated, zero-average noise as input, introducing some correlation in the time series of noise, and integrating the resulting correlated noise. The output profile will depict a nonstationary, randomly rough surface. Two models have been chosen for comparison: a fractal model, in which the noise is correlated even at large distances, and an autoregressive model of order 1, in which the correlation of the noise decays rapidly. Both models have as an end-member a random walk, which is the integration of uncorrelated noise. The models have been fitted to profiles of submarine topography, and the sample autocorrelation, power spectrum and variogram have been compared to the theoretical predictions. The results suggest that a linear system approach is a viable method to model and classify sea-floor topography. The comparison does not show substantial disagreement of the data with either the autoregressive or the fractal model, although a fractal model seems to give a better fit. However, the amplitudes predicted by a nonstationary fractal model for long wavelengths (of the order of 1000 km) are unreasonably large. When viewed through a large window, ocean floor topography is likely to have an expected value determined by isostasy, and to be stationary. Nonstationary models are best applied to wavelengths of the order of 100 km or less. More... »

PAGES

139-155

References to SciGraph publications

  • 1973-04. Thermal Model of Ocean Ridges in NATURE
  • 1978-06. Topography of random surfaces in NATURE
  • 1985. Random Fractal Forgeries in FUNDAMENTAL ALGORITHMS FOR COMPUTER GRAPHICS
  • 1981-11. Fractal dimensions of landscapes and other environmental data in NATURE
  • Journal

    TITLE

    Pure and Applied Geophysics

    ISSUE

    1-2

    VOLUME

    131

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bf00874484

    DOI

    http://dx.doi.org/10.1007/bf00874484

    DIMENSIONS

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