Characterizing grain size distributions: evaluation of a new approach using a multivariate extension of entropy analysis

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ABSTRACT

In the search for environment-diagnostic descriptors of grain size distributions, the use of both least squares statistical procedures and single index or summary measures to characterize sediment distributions have come under increasing criticism. Tests of these approaches highlight the validity of the criticisms. Analysis of sieved samples using the entropy concept, noted in the sedimentological literature as a potentially powerful tool for granulometric analysis, gives a much sharper result. Used previously only in its univariate form, the multivariate extension to the entropy procedure proposed here overcomes the problem of numbers of intervals and interval width inherent in the univariate application. It groups samples in terms of the whole shape of their grain size distributions, allows for variation in the optimal number of intervals for particular samples, and permits the use of more than one set of descriptor variables to be incorporated.

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