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: Mitigation of localized pluvial flooding is one of the major resiliency goals in urban environments, and Green Stormwater Infrastructure (GSI) has the potential to deliver such an outcome. However, there is a lack of systematic approaches to prioritize investment in different candidate areas. This study provides a framework to identify vulnerable stormwater drainage inlets and their contributing areas, prioritize them, identify dominant factors in their selection, assess the potential of GSI in mitigating their overflows, and compare the impact and its cost to gray infrastructure upgrade alternatives. Using SWMM 5.1.013, decision trees, and a volumetric-based assessment of GSI overflow capture, we applied the framework to the City of Dallas, Texas, for three design storms with three GSI practices— bioretention cells, raingardens, and rainwater harvesting tanks. Results showed that there was a significant increase in the number of overflowing stormwater drainage inlets, referred to as hotspots, and their contributing subwatersheds, referred to as opportunity areas, with more intense storms especially in problematic watersheds. Also, prioritization results provided a series of maps to rank the opportunity areas based on overflow severity, recurrence of the overflows, and GSI availability. Moreover, classification results showed that inlet features, especially the inlet depth, were the dominant factors in the identification of the non-problematic inlets. Finally, the GSI impact assessment showed substantial overflow mitigation even at the “very high” severity levels when GSI is comprehensively deployed across opportunity areas. Despite gray infrastructure upgrades yielding higher reduction levels, their cost per cubic meter was higher than GSI. Therefore, a combination of GSI and gray results in maximum overflow reduction at a lower cost compared to common practices.

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).