# Classification of Logging Data Using Machine Learning Algorithms
The peculiarity of the task is that none of the registered parameters are directly related to ROZ; all of them are only indirect signs and make sense only when analyzing their interrelation and spatial distribution. The use of machine learning is promising for such weakly formalized tasks. The problem of identifying the reservoir oxidation zones (zones with disturbed radioactive equilibrium) using machine learning methods based on logging data is addressed in this paper. This problem was first considered in [19]. The second task that is solved when interpreting logging data is the classification of lithological types of host rocks, which directly affects the technological processes of production and, ultimately, economic indicators. A formalized algorithm for lithological interpretation based on the AR curve shape was developed over 50 years ago and described in [20]. There is software implementing this algorithm. When compared with the core, the program shows an accuracy of about 0.4. When performing manual interpretation, experts rely not only on the AR curve shape, but also on data from other logging, neighboring wells, and accumulated experience at the field. Due to this, the average accuracy of experts when comparing with the core is about 0.6. The use of machine learning makes it possible to obtain an accuracy of automatic interpretation comparable to the accuracy of experts' assessments. Despite the importance of these tasks, there is a lack of research on using ML for identifying ROZs and lithological classification in uranium deposits. One of the limits is the deficiency of labeled well logs for this type of research. Here, we consider the application of classical machine learning methods to solve these problems and describe the dataset that allows for training and evaluating ML models. We also show how a relatively simple feature engineering method called a floating window can significantly improve the classification quality when using a range of classical machine learning models. The main contributions of this paper are as follows:  
* The main results of well log data interpretation using machine learning for different types of deposits are presented;
* A uranium well log (UWL) dataset is presented and described, allowing us to set up machine learning methods for ROZ detection and lithological classification;
* This paper presents the state-of-the-art result in solving ROZ detection and lithological classification tasks obtained using the UWL dataset;
* The influence of floating window size on the quality of classification is investigated. This paper consists of the following sections:  
Section 2 discusses the main results of machine learning application in processing the well logging data for various types of minerals and details the features of the problem for uranium deposits. Section 3 describes the method of forming the UWL dataset and its analysis using machine learning methods to solve the problems of the lithological classification and identification of reservoir oxidation zones. Section 4 presents the classification results. Section 5 briefly discusses the obtained results. The conclusion summarizes the results of the analysis and its limitations and formulates the tasks of further research.