## 1 Introduction  
Intensive development of nuclear energy can be considered one of the means to combat global warming. For example, nuclear power plants in Europe annually avoid the emission of 700 million tons of CO\({}_{2}\). Uranium mining is carried out in more than 20 countries, but the main uranium reserves are concentrated in Australia, Kazakhstan, and Canada [1]. According to the World Nuclear Association, in 2018, the largest uranium mining companies produced 86% of the world's total uranium production [2], of which NAC Kazatomprom JSC accounted for 21%. There are two main extraction methods used: open pit (underground and open-pit) and in situ leaching (ISL). Open pit (accounting for 45.9% of production) is used with a sufficiently high uranium content, ILS (accounting for 48.3% of production) is used with a fairly low uranium content, and the reserves must be located in highly permeable rocks (most often sands). Approximately 5.8% of uranium is mined as a by-product, such as in gold mining [3] (Figure 1). ISL is a method of developing of sandstone-type uranium deposits without raising ore to the surface by selectively transferring uranium ions into a productive solution directly in the subsurface using a network of injection and extraction wells. Uranium-bearing ore remains underground, unlike traditional mining methods (mines and quarries). ISL is the most economical and environmentally friendly mining method, widely used in Kazakhstan, Uzbekistan, Canada, and Australia. However, to successfully apply the ISL method, it is necessary to solve the following problems:  
* Accurate determination of the lithological composition of the host rocks and the depth of permeable and impermeable strata using geophysical methods;
* Assessment of filtration properties of host rocks, for the correct assessment of recoverable reserves and production planning. Inaccuracies in solving these problems not only prevent the organization of an optimal production process but often lead to serious financial losses. For example, economic losses from incorrect lithological classification in the deposits of Kazakhstan can be estimated to be approximately USD 1 to 4 million per year [4]. Inaccuracies in assessing the filtration properties of host rocks are caused both by inaccuracies in electrical logging and, to a large extent, by the methodology for determining the lithological composition and filtration properties of rocks. When determining the filtration properties of host rocks in a field, the key point is to determine the relationship between the filtration coefficient \(K_{f}\), determined as a result of hydrogeological studies at the exploratory drilling stage, and electrical logging data, which are subsequently used to calculate the filtration properties of technological wells. However, the accepted methodology, based on analytical methods, has not changed since the end of the last century [5]. At the same time, the correct determination of \(K_{f}\) is necessary for calculating recoverable reserves, predicting production dynamics, and calculating the optimal number of wells and the distance between them (the diameter of a hexagonal cell or the distance between rows of wells). One of the promising ways to improve the quality of the filtration properties assessment is the use of artificial intelligence [6], more specifically, machine learning methods [7]. Machine learning is used in problems of stratigraphy [8], geological mapping [9], assessment of the prospects of tungsten deposits [10], composition of iron ore deposits [11], and lithology [12; 13; 14; 15]. The application of ML for lithological classification is considered in [16]. The purpose of the study was to evaluate the possibilities of using machine learning models for \(K_{f}\) calculation based on well log data in sandstone-type uranium deposits. Figure 1: World uranium mining volumes by mining methods. This study considered the application of machine learning methods to estimate the filtration characteristics of ore-bearing rocks.