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Random variation of fabric eigenvalues: implications for the use of a‐axis fabric data to differentiate till facies †
Author(s) -
Benn Douglas I.,
Ringrose Trevor J.
Publication year - 2001
Publication title -
earth surface processes and landforms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.294
H-Index - 127
eISSN - 1096-9837
pISSN - 0197-9337
DOI - 10.1002/1096-9837(200103)26:3<295::aid-esp159>3.0.co;2-1
Subject(s) - facies , bootstrapping (finance) , eigenvalues and eigenvectors , geology , resampling , mathematics , sampling (signal processing) , geometry , range (aeronautics) , sample (material) , statistics , computer science , geomorphology , materials science , econometrics , physics , composite material , structural basin , quantum mechanics , filter (signal processing) , computer vision , thermodynamics
Fabric ‘shape’, based on the relative values of three eigenvalues calculated from fabric data, has been used to differentiate sediment facies and infer deformation histories. The eigenvalues are based on samples drawn from parent populations, and as such are subject to statistical variance due to sampling effects. In this paper, the degree of statistical variance in fabric data for two types of subglacial till from Breidamerkurjøkull, Iceland, is investigated using ‘bootstrapping’ techniques, in which empirical ‘confidence regions’ are built up by repeated resampling of the original data. The experiments show that, for each till type, the observed between‐sample variability in the fabrics lies within the boundaries associated with random variations, indicating that the observed range of fabric shapes within each till type is likely to be entirely the product of sampling effects. Differences in fabric shape between till types are generally greater than that associated with random variations, indicating that their fabric shape characteristics, as measured by eigenvalues, are significantly different. Nevertheless, the results suggest that great care should be exercised when using a‐axis fabric data to differentiate sedimentary facies, or to infer subtle differences in physical processes or conditions. Copyright © 2001 John Wiley & Sons, Ltd.