Abstract: 4He concentrations in excess of the solubility equilibrium with the atmosphere by up to two to three orders of magnitude are observed in the Carrizo Aquifer in Texas, the Ojo Alamo and Nacimiento aquifers in the San Juan Basin, New Mexico, and the Auob Sandstone Aquifer in Namibia. A simple 4He accumulation model is applied to explain these excess 4He concentrations in terms of both in situ production and a crustal flux across the bottom layer of the aquifer. Results from the model simulations suggest variability in the 4He fluxes, ranging from 6×10−6 cm3 STP cm−2 yr−1 for the Auob Sandstone Aquifer to 3.6×10−7 cm3 STP cm−2 yr−1 for the Carrizo aquifer. For the Ojo Alamo and Nacimiento aquifers an intermediate value of 3×10−6 cm3 STP cm−2 yr−1 was estimated. The contribution of in-situ produced 4He to the measured concentrations was also estimated. This contribution is negligible for the Auob Sandstone Aquifer as compared with both the concentrations measured at the top and bottom of the aquifer for most of the pathway. In the Carrizo aquifer, in-situ produced 4He contributes 27.5% and 15.4%, to the total 4He observed at the top and bottom of the aquifer, respectively. For both aquifers of the San Juan Basin in-situ production almost entirely dominates the 4He concentrations at the top of the aquifer for most of the pathway. In contrast, the internal production is negligible as compared with the measured concentrations at the bottom of these aquifers, reaching, at most, 1.1%. The model simulations require an exponential decrease in the horizontal velocity of the water with increasing recharge distance to reproduce the distribution of 4He in these aquifers. For the Auob Sandstone Aquifer the highest range in the velocity values is obtained (25 to 0.4 m yr−1). The simulations for the Carrizo aquifer and both aquifers located in the San Juan Basin require velocities varying from 4 to 0.1 m yr−1, and from 2 to 0.3 m yr−1, respectively. For each aquifer, average permeability values were also estimated. They are generally in agreement with results obtained from pumping tests, hydrodynamic modeling and previous 14C measurements. On the basis of the results obtained by calibrating the model with the measured 4He concentrations, the mean water residence times were estimated. They agree reasonably well with 14C ages. When applied as chronologies for noble gas temperatures in the same aquifers, the calculated 4He ages allow the identification of three different climate periods similar to those previously identified using 14C ages: (1) the Holocene period (0–10 Ka BP), (2) the Last Glacial Maximum (≈18 Ka BP), and (3) the preceeding period (30–150 Ka BP).

Abstract: More than 4,000 sandstone-hosted uranium occurrences host over 1.2 billion pounds of mined and in situ U3O8 throughout the Colorado Plateau. Most of the resources are in two distinct mineral systems with deposits hosted in the Triassic Chinle and Jurassic Morrison Formations. In the Chinle mineral system, base metal sulfides typically accompany mineralization. The Morrison mineral system is characterized by V/U ratios up to 20. The uranium source was likely volcanic ash preserved as bentonitic mudstones in the Brushy Basin Member of the Morrison Formation, and lithic volcanic clasts, ash shards, and bentonitic clay in the lower part of the Chinle Formation. Vanadium originated from two possible sources: iron–titanium oxides that are extensively altered in bleached rock near deposits or from similar minerals in variably bleached red beds interbedded with and beneath the Morrison. In Chinle-hosted deposits, in addition to volcanic ash, a contributing source of both vanadium and uranium is proposed here for the first time to be underlying red beds in the Moenkopi and Cutler Formations that have undergone a cycle of reddening-bleaching-reoxidation. Transport in both systems was likely in groundwater through the more permeable sandstones and conglomerate units. The association of uranium minerals with carbonate and more rarely apatite, suggests that transport of uranium was as a carbonate or phosphate complex. The first comprehensive examination of paleoclimate, paleotopography, and subsurface structure of aquifers coupled with analysis of the geochronology of deposits suggests that that there were distinct pulses of uranium mineralization/redistribution during the period from about 259 Ma to 12 Ma when oxidized mineralizing fluids were intermittently rejuvenated in the Plateau in response to changes in tectonic regime and climate. Multiple lines of evidence indicate that deposits formed at ambient temperatures of about 25 °C to no greater than about 140 °C. In both systems, deposits formed where groundwater flow slowed and was subject to evaporative concentration. Stagnant conditions allowed for prolonged interaction of U- and V-enriched groundwater with ferrous iron-bearing reductants, such as illite and iron–titanium oxides, and more rarely organic material such as plant debris. Paragenetically late in the sequence, reducing fluids introduced additional organic matter to some deposits. Reducing fluids and introduced organic matter (now amorphous and altered by radiolysis) may originate from regional petroleum systems where peak oil and gas generation was from ∼ 82 to ∼ 5 Ma. Our novel analysis indicates that these reducing fluids bleached rock and protected affected deposits from remobilization during exposure and weathering that followed uplift of the Plateau (∼80 to 40 Ma).

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.