Geochemistry versus grain-size relations of sediments in the light of comminution, chemical alteration, and contrasting source rocks

Around 170 sediment samples from glacial and proximal glacio-fluvial deposits have been analysed for their geochemical composition. Samples derive from two strongly contrasting source areas (granitoids vs. amphibolite) and cover a broad grain-size range from coarse sand to clay. Following descriptive data evaluation, the relation of sediment geochemical composition versus grain size is modelled using linear regression techniques in the Aitchison geometry of the simplex in order to (i) describe the effects of comminution on the composition of individual grain size fractions, (ii) describe the influence of inherited mineral-specific grain-size distributions for contrasting source rocks, and (iii) to test for any potential influence of chemical weathering. Results indicate strong overall grain-size control on sediment composition that is largely reflecting the greater grain-size control on mineralogy. Comminution leads to overall strong enrichment of sheet silicates in the fine-grained fraction at the expense of quartz and, less pronounced, feldspars. Specific elements such as Ca, P, and Ti related to certain minerals do not follow this general trend and clearly indicate source-rock dependent enrichment of certain minerals (e.g. apatite, Ti-minerals) in medium grain-size fractions. Estimates of mineral compositions obtained from a geometric endmember approach support these conclusions. Chemical weathering is shown to be negligible

Life-cycle analysis of coesite-bearing garnet

Detrital coesite-bearing garnet is the final product of a complex geological cycle including coesite entrapment at ultrahigh-pressure conditions, exhumation to Earth’s surface, erosion, and sedimentary transport. In contrast to the usual enrichment of high-grade metamorphic garnet in 14 medium- to coarse-sand fractions, coesite-bearing grains are often enriched in the very fine-sand fraction. To understand this imbalance, we analyze the role of source rock lithology, inclusion size, inclusion frequency, and fluid infiltration on the grain-size heterogeneity of coesite-bearing garnet based on a dataset of 2100 inclusion-bearing grains, of which 93 contain coesite, from the Saxonian Erzgebirge, Germany. By combining inclusion assemblages and garnet chemistry, we show that mafic garnet contains a low number of coesite inclusions per grain and is enriched in the coarse fraction, and felsic garnet contains variable amounts of coesite inclusions per grain, whereby coesite-poor grains are enriched in the coarse fraction and coesite-rich grains extensively disintegrated into smaller fragments resulting in an enrichment in the fine fraction. Raman images reveal that small coesite inclusions <9 µm are primarily monomineralic, whereas larger inclusions partially transformed to quartz, and garnet fracturing, fluid infiltration, and the coesite-to-quartz transformation is a late process during exhumation taking place at ~330°C. A model for the disintegration of coesite-bearing garnet enables explaining the heterogeneous grain27 size distribution by inclusion frequency. High abundances of coesite inclusions cause a high degree of fracturing and fracture connections to smaller inclusions, allowing fluid infiltration and the transformation to quartz, which in turn further promotes garnet disintegration

Garnet major‑element composition as an indicator of host‑rock type: a machine learning approach using the random forest classifier

The major-element chemical composition of garnet provides valuable petrogenetic information, particularly in metamorphic rocks. When facing detrital garnet, information about the bulk-rock composition and mineral paragenesis of the initial garnet-bearing host-rock is absent. This prevents the application of chemical thermo-barometric techniques and calls for quantitative empirical approaches. Here we present a garnet host-rock discrimination scheme that is based on a random forest machine-learning algorithm trained on a large dataset of 13,615 chemical analyses of garnet that covers a wide variety of garnet-bearing lithologies. Considering the out-of-bag error, the scheme correctly predicts the original garnet host-rock in (i) > 95% concerning the setting, that is either mantle, metamorphic, igneous, or metasomatic; (ii) > 84% concerning the metamorphic facies, that is either blueschist/greenschist, amphibolite, granulite, or eclogite/ultrahigh-pressure; and (iii) > 93% concerning the host-rock bulk composition, that is either intermediate–felsic/metasedimentary, mafic, ultramafic, alkaline, or calc–silicate. The wide coverage of potential host rocks, the detailed prediction classes, the high discrimination rates, and the successfully tested real-case applications demonstrate that the introduced scheme overcomes many issues related to previous schemes. This highlights the potential of transferring the applied discrimination strategy to the broad range of detrital minerals beyond garnet. For easy and quick usage, a freely accessible web app is provided that guides the user in five steps from garnet composition to prediction results including data visualization