Application of Machine Learning Methods to Assess Filtration Properties of Host Rocks of Uranium Deposits in Kazakhstan

• Published in: Applied sciences 2023-10, Vol.13 (19), p.10958
• Main Authors: Kuchin, Yan, Mukhamediev, Ravil, Yunicheva, Nadiya, Symagulov, Adilkhan, Abramov, Kirill, Mukhamedieva, Elena, Zaitseva, Elena, Levashenko, Vitaly
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; bagging; boosting; Deep learning; filtration characteristics; Fuzzy logic; Geology; Lithology; Machine learning; Methods; Mineral industry; Mining; Mining industry; Neural networks; Nuclear power plants; Oil reserves; Permeability; Petroleum production; Production planning; regression model; Uranium; uranium mining
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app131910958

The uranium required for power plants is mainly extracted by two methods in roughly equal amounts: quarries (underground and open pit) and in situ leaching (ISL). Uranium mining by in situ leaching is extremely attractive because it is economical and has a minimal impact on the region’s ecology. The effective use of ISL requires, among other things, the accurate assessment of the host rocks’ filtration characteristics. An accurate assessment of the filtration properties of the host rocks allows optimizing the mining process and improving the quality of the ore reserve prediction. At the same time, in Kazakhstan, this calculation is still based on methods that were developed more than 50 years ago and, in some cases, produce inaccurate results. According to our estimates, this method provides a prediction of filtration properties with a determination coefficient R2 = 0.32. This paper describes a method of calculating the filtration coefficient of ore-bearing rocks using machine learning methods. The proposed approach was based on nonlinear regression models providing a 20–75% increase in the accuracy of the filtration coefficient assessment compared with the current methodology. The work used different types of machine learning algorithms based on the gradient boosting technique, bagging technique, feed-forward neural networks, support vector machines, etc. The results of logging, core sampling, and hydrogeological studies obtained during the exploration stage of the Inkai deposit were used as the initial data. All used machine learning models demonstrated significantly better results than the old method. This resulted in improved results compared with previous studies. The LightGBM regressor demonstrated the best result (R2 = 0.710).