Abstract

Abstract. Paleopiezometry and paleowattometry studies are essential to validate models of lithospheric deformation and therefore increasingly common in structural geology. These studies require a single measure of dynamically recrystallized grain size in natural mylonites to estimate the magnitude of differential paleostress (or the rate of mechanical work). This contribution tests the various measures of grain size used in the literature and proposes the frequency peak of a grain size distribution as the most robust estimator for paleopiezometry or paleowattometry studies. The novelty of the approach resides in the use of the Gaussian kernel density estimator as an alternative to the classical histograms, which improves reproducibility. A free, open-source, easy-to-handle script named GrainSizeTools ( http://www.TEOS-10.org) was developed with the aim of facilitating the adoption of this measure of grain size in paleopiezometry or paleowattometry studies. The major advantage of the script over other programs is that by using the Gaussian kernel density estimator and by avoiding manual steps in the estimation of the frequency peak, the reproducibility of results is improved.

Highlights

  • Dynamic recrystallization was originally defined by Poirier and Guillopé (1979) as “a deformation-induced reworking of grain sizes, shapes and/or orientations with little or no chemical change”

  • Because this definition does not make a clear distinction of how to discriminate between dynamic recrystallization and metamorphic reaction in some cases, it was later re-defined by Stunitz (1998) as “the reconstruction of crystalline material without a change in chemical composition driven by strain energy in the form of dislocations”

  • The results show that the Gaussian kernel density estimator (KDE) peak, the mean, the median and the area-weighted grain size have comparable quality as grain size estimators

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Summary

Introduction

Dynamic recrystallization was originally defined by Poirier and Guillopé (1979) as “a deformation-induced reworking of grain sizes, shapes and/or orientations with little or no chemical change”. Mercier et al, 1977; Ross et al, 1980; Tungatt and Humphreys, 1984; De Bresser et al, 1998; Ter Heege, 2002; Ter Heege et al, 2005; Shimizu, 2008) or that it is determined by the rate of mechanical work (i.e. the product of stress and strain rate) (Ter Heege, 2002; Austin and Evans, 2007) The latter implies that the estimation of the mean grain size is not a paleopiezometer but a paleowattometer (Austin and Evans, 2007). Produces the numerical results required and ready-to-publish figures

Deriving grain size from thin sections: a brief review
Why a 2-D approach?
Defining the size of individual grains
Measuring the grain size in monodisperse populations: the cut-section effect
Polydisperse populations: the intersection probability effect
The script
Brief description of the script
Discussion: evaluation of different measures of grain size
Sources of error
How many grains are needed to achieve reproducibility?
Testing different measures of grain size for reproducibility
Testing the script against other software available
Findings
Concluding remarks and future development
Full Text
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