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: 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.

Abstract: 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.

Abstract: Apart from some notable exceptions, the sound governance of transboundary aquifers (coupled or uncoupled to rivers) is seriously lacking in most regions of the world, despite a highly successful 20-year ISARM initiative. The distinction between regions of water abundance (as in the Haute Savoie–Geneva aquifers) and those of water scarcity (\textless1000 m3/an/capita), as in the Rum-Saq aquifer, ought to be a driver for the urgency in adopting sound governance. In the latter regions, however, such an urgent response faces too many hurdles (institutional, financial, and weak capacity). Climate change, one of the global megatrends (among demography, economic shift, resources stress, urbanization, and novel viruses such as COVID-19), will exacerbate the problem in the coming decade and beyond. This chapter provides an critical perspective on the status of this subsurface asset in 570 or so, domestic and transboundary aquifers of the world (self-identified by country experts), while taking full account of their interconnections, or not, with surface waters. This critical perspective will be grounded in two important factors, first the hiatus in adoption by countries of the evolving international water law and guidance on transboundary aquifers (the Draft Articles, which provide legal pathways for collaboration or eventually dispute resolution), and second the framework of the sustainable development goals (SDG) 6 (clean water and sanitation), which countries have committed themselves to with reference to transboundary waters. The critical perspective finds that despite the lack of momentum in adopting formal global norms, sporadic cooperation and collaboration is continuing and is well received, when delivered methodically through the support of international agencies. The findings of the critical perspective are that even if water-related SDGs will have been achieved across the world, it would contribute precious little to meaningful enhancement of governance of transboundary aquifers, unless they have been explicitly addressed in terms that are tangible to decision makers, such as the impact of disregarding them on the current or future national GDP. The onset of a “new socioeconomic normal” in the aftermath of COVID-19 could further defer meaningful progress, taking the example of Latin America, where a 5% decline has been forecast for 2020. With such declines in the finances of governments, attention to shared aquifer resources may well decline even further. Urgent wise reaction to this possibility must be a priority for the professional science-policy community.

Abstract: Argon-37 (37Ar) and Argon-39 (39Ar) are used for groundwater dating on timescales from weeks to centuries. For both isotopes, the quantification of underground sources is essential to accurately infer water residence times from sampled dissolved activities. Subsurface production resulting from interactions with neutrons from the natural radioactivity in rocks and with primary cosmogenic neutrons has been known for a long time. More recently, the capture of slow negative muons and reactions with muon-induced neutrons were documented for 39Ar subsurface production in the context of underground particle detectors (e.g. for Dark Matter research). However, the contribution from these particles was never considered for groundwater dating applications. Here, we reevaluate the importance of all potential depth-related production channels at depth ranges relevant for 39Ar groundwater dating [0 − 200 meters below the surface (m.b.s)]. The production of radioargon by muon-induced processes is considered in this depth range for the first time. The uncertainty on the total depth-dependent production rate is estimated with Monte Carlo simulations assuming a uniform distribution of the parameter uncertainties. This work aims to provide a comprehensive framework for interpreting 39Ar activities in terms of groundwater residence times and for exposure age dating of rocks. The production of 37Ar is also addressed since this isotope is relevant as a proxy for 39Ar production, for the timing of river-groundwater exchanges, and in the context of on-site inspections (OSI) within the verification framework of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). In this perspective, we provide an interactive web-based application for the calculation of 37Ar and 39Ar production rates in rocks.