Abstract: The approximate determination of average Ra/U disequilibria in orebodies is one of the most common causes of errors in U reserve estimations. In roll-front deposits, the disequilibria are however frequently distributed following complex geometries, which must be fully understood to prevent major U reserve overestimates and costly unproductive extractive operations. The processes responsible for disruption of the radioactive equilibria and the U and Ra carriers in such complex natural systems remain poorly constrained. In this contribution, we propose an innovative approach, mixing orebody to sub-grain scale studies to unravel the distribution of U and Ra and the processes responsible for their concentration and uncoupling. Using mineral separations, gamma spectrometry and mineral-chemical analyses, we identified the Fe-Ti clusters (altered ilmenite + pyrite/marcasite) as the microsites for coffinite precipitation and Ra concentration. To understand the influence of such clusters on the distribution of U and Ra at the deposit scale, whole-rock Ra/U disequilibria were measured and mapped at a series of ten drill holes along a profile crosscutting the studied roll-front. The main Ra/U disequilibria are encountered around the mineralization in low U content zones. They are controlled by two main processes. (1) In the oxidized zones, the immobility of 230Th with respect to the U produces patches of Ra disequilibria (carried by the altered U minerals). (2) In the immediate vicinity of the roll-front, the dissolution of the mineralization produces an Ra flux trapped by the alteration products of ilmenites, as definitely confirmed by direct SIMS measurements. Such a process is responsible for the Ra disequilibria envelope located downstream of the richest ores, also known as Ra halo. The highest Ra/U ratios correspond to oxidized upstream samples, but most other high Ra/U ratios are from reduced downstream samples close to the mineralization. Such a low to medium U content envelope with high Ra/U ratios constitutes the main cause of U reserve overestimations.

Abstract: Coffinite (USiO4) is a common uranium-bearing mineral of roll-front uranium deposits. This mineral can be identified by the visible near infrared (Vis-NIR) portable field spectrometers used in mining exploration. However, due to the low detection limits and associated errors, the quantification of coffinite abundance in the mineralized sandstones or sandy sediments of roll-front uranium deposits using Vis-NIR spectrometry requires a specific methodological development. In this study, the 1135 nm absorption band area is used to quantify the abundance of coffinite. This absorption feature does not interfere with NIR absorption bands of any other minerals present in natural sands or sandstones of uranium roll-front deposits. The correlation between the 1135 nm band area and coffinite content was determined from a series of spectra measured from prepared mineral mixtures. The samples were prepared with a range of weighted amounts of arenitic sands and synthetic coffinite simulating the range of uranium concentration encountered in roll-front uranium deposits. The methodology presented in this study provides the quantification of the coffinite content present in sands between 0.03 wt% to 1 wt% coffinite with a detection limit as low as 0.005 wt%. The integrated area of the 1135 nm band is positively correlated with the coffinite content of the sand in this range, showing that the method is efficient to quantify coffinite concentrations typical of roll-front uranium deposits. The regression equation defined in this study was then used as a reference to predict the amount of natural coffinite in a set of mineralized samples from the Tortkuduk uranium roll-front deposit (South Kazakhstan).

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.

Abstract: Concentrations of uranium (U) \textgreater30 µg/L in groundwater are relatively uncommon in drinking water in the United States but can be of concern in those areas where complex interactions of aquifer materials and anthropogenic alterations of the natural flow regime mobilize U. High concentrations (\textgreater30 µg/L) of U in the southeastern San Joaquin Valley, California, USA, have been detected in 24 percent of 257 domestic, irrigation, and public-supply wells sampled across an approximately 110,000 km2 area. In this study we evaluated mechanisms for mobilization of U in the San Joaquin Valley proposed in previous studies, confirming mobilization by HCO3 and refuting mobilization by NO3 and we refined our understanding of the geologic sources of U to the scale of individual alluvial fans. The location of high concentrations depends on the interactions of geological U sources from fluvial fans that originate in the Sierra Nevada to the east and seepage of irrigation water that contains high concentrations of HCO3 that leaches U from the sediments. In addition, interactions with PO4 from fertilized irrigated fields may sequester U in the aquifer. Principal component analysis of the data demonstrates that HCO3 and ions associated with high total dissolved solids in the aquifer and the percentage of agriculture near the well sampled are associated with high U concentrations. Nitrate concentrations do not appear to control release of U to the aquifer. Age dating of the groundwater and generally increasing U concentrations of the past 25 years in resampled wells where irrigation is prevalent suggests that high U concentrations are associated with younger water, indicating that irrigation of fields over the past 100 years has significantly contributed to increasing concentrations and mobilizing U. In some places, the groundwater is supersaturated with uranyl-containing minerals, as would be expected in roll front deposits. In general, the interaction of natural geological sources high in U, the anthropogenically driven addition of HCO3 and possibly phosphate fertilizer, control the location and concentration of U in each individual fluvial fan, but the addition of nitrate in fertilizer does not appear control the location of high U. These geochemical interactions are complex but can be used to determine controls on anomalously high U in alluvial aquifers.