Carbonate rocks are among the sedimentary systems which preserve information on the formation and diagenetic history expressed in its composition (distribution of its major rock-forming components (RFC). For estimating RFC proportions at the micro-scale, a simple counting of visible RFCs in thin sections using overlaid grids is a long-used, well-established technique. However, computer tomography (CT) analysis provides us with quantitative data in 3D at both the scale of the entire sample and a resolution defined by dimensions of the voxels at the micro-scale. The quantitative data expressed in Hounsfield units (HU) correlates with the density of RFCs. In this work statistical properties of CT-based data for selected freshwater carbonate samples from the Danube-Tisza Interfluve have been assessed using histograms and boxplots. Univariate statistical parameters characterize each sample. The maximum-likelihood method of mixture analysis has been adapted to recover and estimate the parameters of these subpopulations. Subpopulations have been defined in the form of overlapping intervals using statistical parameters gained (mean±2STD). Five major components have been defined: empty and partially or entirely filled pores by calcite, limestone micrite, dolomite micrite matrix and limonite saturated matrix.

Grain size distribution is one of the paleoenvironmental proxies that provide insight statistical distribution of size fractions within the sediments. Multivariate statistics have been used to investigate the depositional process from the grain size distribution. Still, the direct application of the standard multivariate methods is not straightforward and can yield misleading interpretations due to the compositional nature of the raw grain size data. This paper is a methodological framework for grain size data characterization through the centered log ratio transformation and euclidean data, coupled with principal component analysis, cluster analysis, and linear discriminant analysis to examine Quaternary sediments from Tövises bed in the southeast Great Hungarian Plain. These approaches provide statistically significant and sedimentologically interpretable results for both datasets. However, the details by which they supplemented the conceptual model were significantly different, and this discrepancy resulted in a different temporal model of the depositional history.