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  • 1
    Publication Date: 2021-03-29
    Description: Investigation by Raman spectroscopy of samples from different geological settings shows that the occurrence of TiO2 polymorphs other than rutile can hardly be predicted, and furthermore, the occurrence of anatase is more widespread than previously thought. Metamorphic pressure and temperature, together with whole rock chemistry, control the occurrence of anatase, whereas variation of mineral assemblage characteristics and/or fluid occurrence or composition takes influence on anatase trace element characteristics and re-equilibration of relict rutiles. Evaluation of trace element contents obtained by electron microprobe in anatase, brookite, and rutile shows that these vary significantly between the three TiO2 phases. Therefore, on the one hand, an appropriation to source rock type according to Nb and Cr contents, but as well application of thermometry on the basis of Zr contents, would lead to erroneous results if no phase specification is done beforehand. For the elements Cr, V, Fe, and Nb, variation between the polymorphs is systematic and can be used for discrimination on the basis of a linear discriminant analysis. Using phase group means and coefficients of linear discriminants obtained from a compilation of analyses from samples with well-defined phase information together with prior probabilities of groupings from a natural sample compilation, one is able to calculate phase grouping probabilities of any TiO2 analysis containing at least the critical elements Cr, V, Fe, and Nb. An application of this calculation shows that for the appropriation to the phase rutile, a correct-classification rate of 99.5% is obtained. Hence, phase specification by trace elements proves to be a valuable tool besides Raman spectroscopy.
    Keywords: TiO2 polymorph discrimination; Phase classification; Anatase; Brookite; Rutile; Erzgebirge; Zr-in-rutile thermometry ; 551 ; Earth Sciences; Mineral Resources; Mineralogy; Geology
    Language: English
    Type: article , publishedVersion
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  • 2
    Publication Date: 2021-03-29
    Description: Indicator kriging (IK) is a spatial interpolation technique aimed at estimating the conditional cumulative distribution function (ccdf) of a variable at an unsampled location. Obtained results form a discrete approximation to this ccdf, and its corresponding discrete probability density function (cpdf) should be a vector, where each component gives the probability of an occurrence of a class. Therefore, this vector must have positive components summing up to one, like in a composition in the simplex. This suggests a simplicial approach to IK, based on the algebraic-geometric structure of this sample space: simplicial IK actually works with log-odds. Interpolated log-odds can afterwards be easily re-expressed as the desired cpdf or ccdf. An alternative but equivalent approach may also be based on log-likelihoods. Both versions of the method avoid by construction all conventional IK standard drawbacks: estimates are always within the (0,1) interval and present no order-relation problems (either with kriging or co-kriging). Even the modeling of indicator structural functions is clarified.
    Keywords: Aitchison geometry; Ilr coordinates; Indicator variogram; Logistic regression ; 551 ; Geosciences; Hydrogeology ; Geotechnical Engineering; Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences; Math. Applications in Geosciences
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  • 3
    Publication Date: 2021-03-29
    Description: It is well-known that sediment composition strongly depends on grain size. A number of studies have tried to quantify this relationship focusing on the sand fraction, but only very limited data exists covering wider grain size ranges. Geologists have a clear conceptual model of the relation between grain size and sediment petrograpic composition, typically displayed in evolution diagrams. We chose a classical model covering grain sizes from fine gravel to clay, and distinguishing five types of grains (rock fragments, poly- and mono crystalline quartz, feldspar and mica/clay). A compositional linear process is fitted here to a digitized version of this model, by (i) applying classical regression to the set of all pairwise log-ratios of the 5-part composition against grain size, and (ii) looking for the compositions that best approximate the set of estimated parameters, one acting as slope and one as intercept. The method is useful even in the presence of several missing values. The linear fit suggests that the relative influence of the processes controlling the relationship between grain size and sediment composition is constant along most of the grain size spectrum.
    Keywords: Censored data; Compositional Data Analysis; Moore–Penrose generalized inverse; Sedimentary petrography ; 551 ; Earth Sciences; Hydrogeology ; Geotechnical Engineering; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Mathematical Applications in Earth Sciences
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