# Classification of Logging Data Using Machine Learning Algorithms  
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1 Institute of Automation and Information Technologies, Sathayev University (KazNRTU), 22 Satbayev Street, Almaty 050013, Kazakhstan, [EMAIL_ADDRESS] ([PERSON]ESS] (Z.K.) 1  
[PERSON]  
1 Institute of Automation and Information Technologies, Sathayev University (KazNRTU), 22 Satbayev Street, Almaty 050013, Kazakhstan, [EMAIL_ADDRESS] ([PERSON]ESS] (Z.K.) 1  
[PERSON]  
2 Institute of Information and Computational Technologies, Pushkin Str, 125, Almaty 050013, Kazakhstan, [EMAIL_ADDRESS] (N.Y.); [EMAIL_ADDRESS] (E.M.) 2  
[PERSON]  
1 Institute of Automation and Information Technologies, Sathayev University (KazNRTU), 22 Satbayev Street, Almaty 050013, Kazakhstan, [EMAIL_ADDRESS] ([PERSON]ESS] (Z.K.) 1  
[PERSON]  
2 Institute of Information and Computational Technologies, Pushkin Str, 125, Almaty 050013, Kazakhstan, [EMAIL_ADDRESS] (N.Y.); [EMAIL_ADDRESS] (E.M.) 2  
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of filtration properties of host rocks, as well as gamma ray logging (GR) for estimating uranium content based on the gamma radiation of radium and its decay products with a conversion factor using the radioactive equilibrium coefficient. The log data consist of physical parameters measured inside the borehole at 10 cm intervals in depth, and it is displayed as graphs (curves) for expert evaluation. However, the current automatic interpretation of electric logs (AR and SP) uses only AR data without considering other data sources, such as other logs, information from nearby boreholes, etc. This results in significant manual corrections produced by the interpreting engineer and also prevents correct lithologic interpretation when the AR curve is distorted. Application machine learning is one of the ways to improve the quality of borehole log interpretation from the 1990s [3; 4; 5; 6] to the present day [7]. Most studies on using ML for log data interpretation focus on the lithology classification of oil and gas fields [8; 9; 10; 11] and permeability prediction [12; 13; 14; 15]. ML methods are employed in uranium deposits for such tasks as lithologic classification [16], stratigraphy [17], estimation of filtration properties of host rocks [1], and evaluation of the impact of expert labeling of logging data [18]. Oil well logging and uranium well logging have many differences. Oil well logging needs to find layers that are at least one-meter-thick in deep wells. Uranium well logging needs to find layers that are as thin as 20 cm in shallow wells. Uranium wells also have smaller diameters, which limit the types of downhole tools and logging methods that can be used. Electrical logging methods are common, but they are not very accurate. Core sampling is also difficult because the rocks are loose and can be destroyed by in situ leaching. One of the most important tasks in uranium mining by the in situ leaching method (ISL) is determining the uranium concentration, which can be carried out either directly by neutron logging methods or indirectly by the gamma radiation of uranium decay products. The first approach is more reliable, while the second one is cheaper and faster, and it is used in uranium mining in Kazakhstan. However, in the reservoir oxidation zones (ROZs), this dependence does not work; in other words, uranium is absent in the presence of the gamma radiation of uranium decay products. Errors in ROZ identification or ignoring them leads to large financial losses, while their correct allocation is a real challenge. There is no formalized solution algorithm for this problem. The task of ROZ identification based on the data of a standard set of geophysical studies used in uranium mining in Kazakhstan is particularly relevant and complex.