Abstract: The Complexe Terminal (CT) multi-layer aquifer is formed by Neogene/Paleogene sand deposits, Upper Senonian (Campanian-Maastrichtian limestones) and Turonian carbonates. The chemical composition and isotopes of carbon and uranium were investigated in groundwater sampled from the main hydrogeological units of the (CT) aquifer in southern Tunisia. We paid special attention to the variability of uranium contents and isotopes ratio (234U/238U) to provide a better understanding of the evolution of the groundwater system. Uranium concentrations range from 1.5 to 19.5 ppb, typical of oxic or mildly reducing conditions in groundwaters. The lowest concentrations are found southeast of the study area, where active recharge is supposed to take place. When looking at the isotope composition, it appears that all the samples, including those from carbonate levels, are in radioactive disequilibrium with significant 234U excess. A clear-cut distinction is observed between Turonian and Senonian carbonate aquifers on the one hand, with 234U/238U activity ratios between 1.1 and 1.8, and the sandy aquifer on the other hand, showing higher ratios from 1.8 to 3.2. The distribution of uranium in this complex aquifer system seems to be in agreement with the lithological variability and are ultimately a function of a number of physical and chemical factors including the uranium content of the hosting geological formation, water-rock interaction and mixing between waters having different isotopic signatures. Significant relationships also appear when comparing the uranium distribution with the major ions composition. It is noticeable that uranium is better correlated with sulfate, calcium and magnesium than with other major ions as chloride or bicarbonate. The 14C activities and δ13C values of DIC cover a wide range of values, from 1.1 pmc to 30.2 pmc and from −3.6‰ to −10.7‰, respectively. 14C model ages estimated by the Fontes and Garnier model are all younger than 22 Ka and indicate that the recharge of CT groundwater occurred mainly during the end of the last Glacial and throughout the Holocene.

Abstract: Pyrite is common in roll-front type uranium deposit in Chu-sarysu basin, Kazakhstan. Combined in-situ microstructural, isotopic and chemical analysis of pyrite indicates variation in precipitation conditions and in fluid composition. Broad-scale δ34S heterogeneity indicates a complex multi-facet evolution. First generation authigenic framboïdal aggregates are biogenic as demonstrated by the lowest δ34S values of -48‰ to -28‰. The latest generation pyrites are probably hydrothermal with greater δ34S variation (-30‰ to +12‰). This hydrothermal pyrite commonly displays variable enrichment of several trace elements especially As, Co and Ni. Strong variation in δ34S values and variable trace element enrichment is interpreted in terms of continuous variations in fluid composition.

Abstract: Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words – it is a great tool to describe non-linear phenomena. We tried to use this technique to improve existing process of stratigraphy, and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for stratigraphy boundaries classification based on geophysics logging data for uranium deposit in Kazakhstan. Correct marking of stratigraphy from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting, k nearest neighbour and XGBoost.