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Milena-Pérez, A., Piñero-García, F., Benavente, J., Expósito-Suárez, V. M., Vacas-Arquero, P., & Ferro-García, M. A. (2021). Uranium content and uranium isotopic disequilibria as a tool to identify hydrogeochemical processes. Journal of Environmental Radioactivity, 227, 106503.
Abstract: This paper studies the uranium content and uranium isotopic disequilibria as a tool to identify hydrogeochemical processes from 52 groundwater samples in the province of Granada (Betic Cordillera, southeastern Spain). According to the geological complexity of the zone, three groups of samples have been considered. In Group 1 (thermal waters; longest residence time), the average uranium content was 2.63 ± 0.16 μg/L, and 234U/238U activity ratios (AR) were the highest of all samples, averaging 1.92 ± 0.30. In Group 2 (mainly springs from carbonate aquifers; intermediate residence time), dissolved uranium presented an average value of 1.34 ± 0.13 μg/L, while AR average value was 1.38 ± 0.25. Group 3 comes from pumping wells in a highly anthropized alluvial aquifer. In this group, where the residence time of the groundwater is the shortest of the three, average uranium content was 5.28 ± 0.26 μg/L, and average AR is the lowest (1.17 ± 0.12). In addition, the high dissolved uranium value and the low AR brought to light the contribution of fertilizers (Group 3). In the three groups, 235U/238U activity ratios were similar to the natural value of 0.046. Therefore, 235U detected in the samples comes from natural sources. This study is completed with the determination of major ions and physicochemical parameters in the groundwater samples and the statistical analysis of the data by using the Principal Component Analysis. This calculation indicates the correlation between uranium isotopes and bicarbonate and nitrate anions.
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Merembayev, T., Yunussov, R., & Yedilkhan, A. (2019). Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits. Procedia Computer Science, 150, 46–52.
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.
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Dutova, E. M., Nikitenkov, A. N., Pokrovskiy, V. D., Banks, D., Frengstad, B. S., & Parnachev, V. P. (2017). Modelling of the dissolution and reprecipitation of uranium under oxidising conditions in the zone of shallow groundwater circulation. Journal of Environmental Radioactivity, 178-179, 63–76.
Abstract: Generic hydrochemical modelling of a grantoid-groundwater system, using the Russian software “HydroGeo”, has been carried out with an emphasis on simulating the accumulation of uranium in the aqueous phase. The baseline model run simulates shallow granitoid aquifers (U content 5 ppm) under conditions broadly representative of southern Norway and southwestern Siberia: i.e. temperature 10 °C, equilibrated with a soil gas partial CO2 pressure (PCO2, open system) of 10−2.5 atm. and a mildly oxidising redox environment (Eh = +50 mV). Modelling indicates that aqueous uranium accumulates in parallel with total dissolved solids (or groundwater mineralisation M – regarded as an indicator of degree of hydrochemical evolution), accumulating most rapidly when M = 550–1000 mg L−1. Accumulation slows at the onset of saturation and precipitation of secondary uranium minerals at M = c. 1000 mg L−1 (which, under baseline modelling conditions, also corresponds approximately to calcite saturation and transition to Na-HCO3 hydrofacies). The secondary minerals are typically “black” uranium oxides of mixed oxidation state (e.g. U3O7 and U4O9). For rock U content of 5–50 ppm, it is possible to generate a wide variety of aqueous uranium concentrations, up to a maximum of just over 1 mg L−1, but with typical concentrations of up to 10 μg L−1 for modest degrees of hydrochemical maturity (as indicated by M). These observations correspond extremely well with real groundwater analyses from the Altai-Sayan region of Russia and Norwegian crystalline bedrock aquifers. The timing (with respect to M) and degree of aqueous uranium accumulation are also sensitive to Eh (greater mobilisation at higher Eh), uranium content of rocks (aqueous concentration increases as rock content increases) and PCO2 (low PCO2 favours higher pH, rapid accumulation of aqueous U and earlier saturation with respect to uranium minerals).
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Priestley, S. C., Payne, T. E., Harrison, J. J., Post, V. E. A., Shand, P., Love, A. J., et al. (2018). Use of U-isotopes in exploring groundwater flow and inter-aquifer leakage in the south-western margin of the Great Artesian Basin and Arckaringa Basin, central Australia. Applied Geochemistry, 98, 331–344.
Abstract: The distribution of uranium isotopes (238U and 234U) in groundwaters of the south-western margin of the Great Artesian Basin (GAB), Australia, and underlying Arckaringa Basin were examined using groundwater samples and a sequential extraction of aquifer sediments. Rock weathering, the geochemical environment and α-recoil of daughter products control the 238U and 234U isotope distributions giving rise to large spatial variations. Generally, the shallowest aquifer (J aquifer) contains groundwater with higher 238U activity concentrations and 234U/238U activity ratios close to secular equilibrium. However, the source input of uranium is spatially variable as intermittent recharge from ephemeral rivers passes through rocks that have already undergone extensive weathering and contain low 238U activity concentrations. Other locations in the J aquifer that receive little or no recharge contain higher 238U activity concentrations because uranium from localised uranium-rich rocks have been leached into solution and the geochemical environment allows the uranium to be kept in solution. The geochemical conditions of the deeper aquifers generally result in lower 238U activity concentrations in the groundwater accompanied by higher 234U/238U activity ratios. The sequential extraction of aquifer sediments showed that α-recoil of 234U from the solid mineral phases into the groundwater, rather than dissolution of, or exchange with the groundwater accessible minerals in the aquifer, caused enrichment of groundwater 234U/238U activity ratios in the Boorthanna Formation. Decay of 238U in uranium-rich coatings on J aquifer sediments caused resistant phase 234U/238U activity ratio enrichment. The groundwater 234U/238U activity ratio is dependent on groundwater residence time or flow rate, depending on the flow path trajectory. Thus, uranium isotope variations confirmed earlier groundwater flow interpretations based on other tracers; however, spatial heterogeneity, and the lack of clear regional correlations, made it difficult to identify recharge and inter-aquifer leakage.
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Strandmann, P. A. E. P. von, Reynolds, B. C., Porcelli, D., James, R. H., Calsteren, P. van, Baskaran, M., et al. (2006). Assessing continental weathering rates and actinide transport in the Great Artesian Basin. Geochimica et Cosmochimica Acta, 70(18, Supplement), 497.
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