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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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Author |
Vushe, A.; Amutenya, M. |
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Title |
Investigating nitrate retention capacity, elementary and mineral composition of Kalahari sandy soils at Mashare farm in Namibia, Okavango river basin |
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Journal Article |
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Year |
2019 |
Publication |
Scientific African |
Abbreviated Journal |
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6 |
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00193 |
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Keywords |
Irrigated field, Cultivated Kalahari sandy soil, Leaching, Nitrate retention capacity, Quartz mineral, Water saturated |
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Abstract |
Kalahari sands which cover a large part of Southern Africa and extend into Central Africa are infertile and marginal soils for intensive agriculture. Therefore, high nitrogen fertilisation rates may degrade ecosystems of rivers with catchments covered by the Kalahari sands. A study on Mashare Farm located in the Okavango River basin showed that irrigated Kalahari sandy soils had a nitrate retention capacity, which enabled the soil to resist nitrate leaching in water saturated conditions. The irrigated soils were modified by agricultural activities; hence this study investigated if uncultivated and cultivated Kalahari sand soils had similar nitrate retention properties. The elementary composition of the soils was investigated for obtaining an insight into chemical properties that may be causing the nitrate retention capacity. A permeameter was used to leach out nitrates from irrigated and uncultivated soil samples, and nitrate concentrations were measured on the leaching effluent from the permeameter. Elemental analysis was done on the cultivated and the uncultivated soil samples using a Scanning Electron Microscope, a portable X-Ray Fluorescence analyzer, and an X-Ray Diffraction machine, and the later was also used for crystalline structure analyses. Sieve analyses confirmed that the Mashare’s cultivated and uncultivated topsoils were similar, and both were similar to Botswana Kalahari topsoil. The irrigated and cultivated subsoil had a higher average nitrate retention capacity of 76% compared to 73% for the uncultivated subsoil. Both samples had the same elements, although the proportions were different. Both soil samples were dominated by a quartz mineral, but the field soil had traces of palygorskite. The presence of aluminum and transition metals outside the minerals structure, but as coatings on the quartz sand grains enhanced nitrate retention capacity properties of the Kalahari sand soils. |
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2468-2276 |
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THL @ christoph.kuells @ VUSHE2019e00193 |
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277 |
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