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Merembayev, T.; Yunussov, R.; Yedilkhan, A. |
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Title |
Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits |
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Journal Article |
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Year |
2019 |
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Procedia Computer Science |
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150 |
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46-52 |
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Keywords |
classification, geophysics logging data, machine learning, stratigraphy, uranium deposit |
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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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1877-0509 |
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THL @ christoph.kuells @ merembayev_machine_2019 |
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113 |
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Milena-Pérez, A.; Piñero-García, F.; Benavente, J.; Expósito-Suárez, V.M.; Vacas-Arquero, P.; Ferro-García, M.A. |
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Title |
Uranium content and uranium isotopic disequilibria as a tool to identify hydrogeochemical processes |
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Journal Article |
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2021 |
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Journal of Environmental Radioactivity |
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227 |
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106503 |
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Keywords |
234U/238U, Betic cordillera, Groundwater, Hydrogeochemistry, Uranium natural isotopes |
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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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0265-931x |
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THL @ christoph.kuells @ milena-perez_uranium_2021 |
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112 |
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Min, M.; Xu, H.; Chen, J.; Fayek, M. |
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Evidence of uranium biomineralization in sandstone-hosted roll-front uranium deposits, northwestern China |
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2005 |
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Ore Geology Reviews |
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26 |
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3 |
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198-206 |
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Biomineralization, China, Roll-front uranium deposit, Sandstone |
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We show evidence that the primary uranium minerals, uraninite and coffinite, from high-grade ore samples (U3O8\textgreater0.3%) in the Wuyiyi, Wuyier, and Wuyisan sandstone-hosted roll-front uranium deposits, Xinjiang, northwestern China were biogenically precipitated and psuedomorphically replace fungi and bacteria. Uranium (VI), which was the sole electron acceptor, was likely to have been enzymically reduced. Post-mortem accumulation of uranium may have also occurred through physio-chemical interaction between uranium and negatively-charged cellular sites, and inorganic adsorption or precipitation reactions. These results suggest that microorganisms may have played a key role in formation of the sandstone- or roll-type uranium deposits, which are among the most economically significant uranium deposits in the world. |
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0169-1368 |
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THL @ christoph.kuells @ min_evidence_2005 |
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186 |
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Min, M.; Chen, J.; Wang, J.; Wei, G.; Fayek, M. |
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Title |
Mineral paragenesis and textures associated with sandstone-hosted roll-front uranium deposits, NW China |
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2005 |
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Ore Geology Reviews |
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26 |
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1 |
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51-69 |
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China, Mineralogy, Paragenesis, Sandstone-hosted roll-type uranium deposit |
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We present a first paragenetic study of the Wuyier, Wuyisan, Wuyiyi and Shihongtan sandstone-hosted roll-front uranium deposits, northwest China. The mineralization is hosted by Lower–Middle Jurassic coarse- to medium-grained sandstones, which are dark-gray to black due to a mixture of ore minerals and carbonaceous debris. The sandstone is alluvial fan-braided river facies. Minerals associated with these deposits can be broadly categorized as detrital, authigenic, and ore-stage mineralization. Ore minerals consist of uraninite and coffinite. This is the first noted occurrence of coffinite in this type of deposit in China. Sulfide minerals associated with the uranium minerals are pyrite, marcasite, and less commonly, sphalerite and galena. The sulfide minerals are largely in textural equilibrium with the uranium minerals. However, these sulfide minerals occasionally appear to predate, as well as postdate, the uranium minerals. This implies that there are multiple generations of sulfides associated with these deposits. The ore minerals occur interstitially between fossilized wood cells in the sandstones as well as replace fossilized wood and biotite. The deposits are generally low-grade. Primary uranium minerals associated with the low-grade deposits are generally too small, ranging from 0.2 to 0.3 μm in diameter, to be observed by optical microscopy and are only observed by electron microscopy. Mineral paragenesis and textures indicate that these deposits formed under low temperature (30–50 °C) conditions. |
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0169-1368 |
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THL @ christoph.kuells @ min_mineral_2005 |
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175 |
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Moreau, M.; Daughney, C. |
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Defining natural baselines for rates of change in New Zealand’s groundwater quality: Dealing with incomplete or disparate datasets, accounting for impacted sites, and merging into state of the-environment reporting |
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Journal Article |
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2021 |
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Science of The Total Environment |
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755 |
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143292 |
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Baseline, Groundwater quality, Machine-learning, Monitoring, New Zealand, Trends |
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To effectively manage sustainably groundwater bodies, it is essential to establish what the naturally occurring ranges of chemical concentrations in groundwaters are and how they change over time. We defined baseline trends for New Zealand groundwaters using: 1) pattern recognition techniques to deal with inconsistent monitoring suites between the national (110 sites) and the denser regional network (\textgreater1000 sites), and 2) multivariate statistics to identify and remove impacted sites from the enhanced dataset. Rates of changes were calculated for 13 parameters between January 2005 and December 2014 at more than 1000 groundwater quality monitoring sites. The resulting dataset included 262 complete cases (CC), which was enhanced using Machine-Learning (ML) techniques to a total of 607 sites. Hierarchical cluster analysis was used to identify trend clusters that were consistent between the CC, ML-enhanced datasets and a 2006 study based on solely on the national network. The largest cluster (WR) consisted of low magnitude changes across all parameters and was attributed to water-rock interaction processes. The second largest cluster (I) exhibited fast changes particularly for parameters linked to human-induced impact. The third largest cluster (D) comprised decreases of all parameters and was associated with dilution processes. Trend clusters were further refined using groundwater quality state information, enabling the identification of impacted sites outside of Cluster I in the ML-enhanced and CC datasets. Corresponding trend baselines were subsequently derived at unimpacted sites using univariate quantile distribution (5th and 95th percentile thresholds). Finally, we developed classifications combining baselines (state and trend) and natural variability to enhance state of the environment reporting. This allowed the new identification of deteriorating trends at sites where groundwater quality state is not yet affected in addition to trend reversals. These classifications can be adapted to incorporate new knowledge or align with surface water quality reporting. |
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0048-9697 |
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THL @ christoph.kuells @ moreau_defining_2021 |
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164 |
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