# Classification of Logging Data Using Machine Learning Algorithms
## 6 Conclusions  
The task of analyzing well logging data is one of the most important in the process of exploration of mineral deposits. First of all, this concerns oil, gas, coal, and uranium deposits. A well logging data analysis allows for optimizing the production process. The correct interpretation of data allows for avoiding unproductive costs and increasing the economic indicators of the production process. In the process of interpretation, machine learning models are used to solve the most common problems. The selection and configuration of such models is one of the important scientific tasks. Most of this kind of work is devoted to the applications of machine learning to the analysis of oil and gas field data. Until now, when analyzing well logging data of uranium deposits, a significant limitation of research was the lack of a publicly available dataset. This study covers this gap. This paper presents a labeled well logging dataset. The set partially compensates for the deficit of such data for sandstone-type uranium deposits. In this research, this dataset is used to train and evaluate the machine learning models when solving two problems. Task 1 is the ROZ identification problem. This problem is based on electrical and gamma logging data and it was first posed in work [19]. It is of exceptional importance in uranium mining by the ISR method (more than 40% of the world's uranium production). The use of machine learning allows us to \"significantly (by 22%-70%) increase the accuracy of the filtration coefficient determination and, accordingly, improve the accuracy of recoverable reserves calculation and economic indicators of mining processes\" [1]. In this case, the target variable is the lit variable. However, if we use the variables y_LIT1_train and y_LIT1_test instead of y_train and y_test as the target variables, we can solve the problem of lithologic classification (Task 2). Correct lithologic classification allows for optimizing the mining process. In both cases, feature engineering in the form of increasing the size of the floating data window helped to significantly improve (by 9-12%) the classification result. In the future, it is possible to conduct computational experiments to select the optimal set of input parameters, for example, using the Mlextend library [57; 58] (Task 3), and to search for the best combination of hyperparameters of machine learning models using GridSearchCV, RandomizedSearchCV [54], BayesSearchCV [59], and other methods [60] (Task 4). This will also help to overcome the limitations of this study listed in Section 5. The dataset can also be used for a comparative analysis of log data from different fields or for transfer learning experiments (Task 5). One important task for future research is the application of deep learning models that can improve the classification quality. Deep learning models, in particular convolutional networks, can take into account the shape of the well log and, therefore, better imitate the classification processes performed by experts. It should be noted that this study is based on a dataset limited to 96 wells from one field. It is planned to increase the dataset, use deep learning models along with algorithm ensembles, and solve above-mentioned tasks, Tasks 3-5, in future studies. Conceptualization, R.M. and Y.K. ; methodology, R.M., E.M. and Z.K. ; validation, Y.K., P.R. and E.M.; formal analysis, Y.K., N.Y. and Z.K. ; investigation, R.M., Y.K., P.R. and V.G. ; data curation, Y.K. and N.Y.; visualization, R.M. and E.M.; writing--original draft, R.M. and Y.K. ; writing--review and editing, Y.K., E.M., P.R. and V.G. ; project administration, N.Y. and Y.K. ; funding acquisition, N.Y., V.G., Z.K. and R.M. All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.