Mixtures of multiplicative cascade models in geochemistry

Multifractal modeling of geochemical map data can help to explain the nature of frequency distributions of element concentration values for small rock samples and their spatial covariance structure. Useful frequency distribution models are the lognormal and Pareto distributions which plot as straigh...

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Main Author: F. P. Agterberg
Format: Article
Language:English
Published: Copernicus Publications 2007-05-01
Series:Nonlinear Processes in Geophysics
Online Access:http://www.nonlin-processes-geophys.net/14/201/2007/npg-14-201-2007.pdf
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spelling doaj-b8fe80c1562d41809f19c92ea04140bf2020-11-24T23:02:39ZengCopernicus PublicationsNonlinear Processes in Geophysics1023-58091607-79462007-05-01143201209Mixtures of multiplicative cascade models in geochemistryF. P. AgterbergMultifractal modeling of geochemical map data can help to explain the nature of frequency distributions of element concentration values for small rock samples and their spatial covariance structure. Useful frequency distribution models are the lognormal and Pareto distributions which plot as straight lines on logarithmic probability and log-log paper, respectively. The model of de Wijs is a simple multiplicative cascade resulting in discrete logbinomial distribution that closely approximates the lognormal. In this model, smaller blocks resulting from dividing larger blocks into parts have concentration values with constant ratios that are scale-independent. The approach can be modified by adopting random variables for these ratios. Other modifications include a single cascade model with ratio parameters that depend on magnitude of concentration value. The Turcotte model, which is another variant of the model of de Wijs, results in a Pareto distribution. Often a single straight line on logarithmic probability or log-log paper does not provide a good fit to observed data and two or more distributions should be fitted. For example, geochemical background and anomalies (extremely high values) have separate frequency distributions for concentration values and for local singularity coefficients. Mixtures of distributions can be simulated by adding the results of separate cascade models. Regardless of properties of background, an unbiased estimate can be obtained of the parameter of the Pareto distribution characterizing anomalies in the upper tail of the element concentration frequency distribution or lower tail of the local singularity distribution. Computer simulation experiments and practical examples are used to illustrate the approach. http://www.nonlin-processes-geophys.net/14/201/2007/npg-14-201-2007.pdf
collection DOAJ
language English
format Article
sources DOAJ
author F. P. Agterberg
spellingShingle F. P. Agterberg
Mixtures of multiplicative cascade models in geochemistry
Nonlinear Processes in Geophysics
author_facet F. P. Agterberg
author_sort F. P. Agterberg
title Mixtures of multiplicative cascade models in geochemistry
title_short Mixtures of multiplicative cascade models in geochemistry
title_full Mixtures of multiplicative cascade models in geochemistry
title_fullStr Mixtures of multiplicative cascade models in geochemistry
title_full_unstemmed Mixtures of multiplicative cascade models in geochemistry
title_sort mixtures of multiplicative cascade models in geochemistry
publisher Copernicus Publications
series Nonlinear Processes in Geophysics
issn 1023-5809
1607-7946
publishDate 2007-05-01
description Multifractal modeling of geochemical map data can help to explain the nature of frequency distributions of element concentration values for small rock samples and their spatial covariance structure. Useful frequency distribution models are the lognormal and Pareto distributions which plot as straight lines on logarithmic probability and log-log paper, respectively. The model of de Wijs is a simple multiplicative cascade resulting in discrete logbinomial distribution that closely approximates the lognormal. In this model, smaller blocks resulting from dividing larger blocks into parts have concentration values with constant ratios that are scale-independent. The approach can be modified by adopting random variables for these ratios. Other modifications include a single cascade model with ratio parameters that depend on magnitude of concentration value. The Turcotte model, which is another variant of the model of de Wijs, results in a Pareto distribution. Often a single straight line on logarithmic probability or log-log paper does not provide a good fit to observed data and two or more distributions should be fitted. For example, geochemical background and anomalies (extremely high values) have separate frequency distributions for concentration values and for local singularity coefficients. Mixtures of distributions can be simulated by adding the results of separate cascade models. Regardless of properties of background, an unbiased estimate can be obtained of the parameter of the Pareto distribution characterizing anomalies in the upper tail of the element concentration frequency distribution or lower tail of the local singularity distribution. Computer simulation experiments and practical examples are used to illustrate the approach.
url http://www.nonlin-processes-geophys.net/14/201/2007/npg-14-201-2007.pdf
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