# Classification of Logging Data Using Machine Learning Algorithms
## 2 Related Works  
The interpretation of well logging data is one of the important stages in assessing the lithological structure of ore-bearing horizons, predicting the productivity of a deposit, and selecting a drilling location for mining. One of the frequently studied issues in such interpretation is the problem of choosing the most effective machine learning methods. Both classical and modern machine learning methods are widely used [21], including deep neural networks (see Figure 1). At the same time, the supervised learning (SL) methods are favored under appropriate conditions, which shows better results compared to unsupervised learning (UL) [22]. According to the information available to the authors, the main models used for interpreting well logging data are classical models and ensembles of machine learning algorithms based on decision trees: Linear Regression (LR), Logistic Regression (LogR), k-nearest neighbors (kNNs), decision tree (DT), artificial neural network or multilayer perceptron (ANN or MLP), Naive Bayes Classifier (NB), support vector machines (SVMs), random forest (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM) (Table 1). Deep learning models are also used: Deep Feed Forward Neural Network (DFFNN), long short-term memory (LSTM), Convolution Neural Network (CNN), etc. Figure 1: Machine learning models. To assess the quality of the models, the following standard metrics are widely used: accuracy (Acc), harmonic measure--f1 score (f1), determination coefficient (R2), linear correlation coefficient (Rp). In some cases, the area under the curve (Receiver Operating Characteristic (ROC)) or Area Under the Curve (AUC) is used--ROC AUC. The main metrics of regression and classification models are presented in Appendix A. Table 2 shows the main results obtained in the course of solving problems of processing well logging data with employment of machine learning methods. To assess the quality of the models, the following standard metrics are widely used: accuracy (Acc), harmonic measure--f1 score (f1), determination coefficient (R2), linear correlation coefficient (Rp). In some cases, the area under the curve (Receiver Operating Characteristic (ROC)) or Area Under the Curve (AUC) is used--ROC AUC. The main metrics of regression and classification models are presented in Appendix A. Table 2 shows the main results obtained in the course of solving problems of processing well logging data with employment of machine learning methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**X** & **Classifier** & **Abbreviated Name** & **References** \\ \hline
1. & Light gradient boosting machine & LGBM & [23; 24; 25] \\ \hline
2. & Random forest classifier & RF & [26] \\ \hline
3. & Extreme gradient boosting & XGB & [27] \\ \hline
4. & k-nearest neighbors & kNN & [28] \\ \hline
5. & Decision tree & DT & [29] \\ \hline
6. & Artificial neural network or multilayer perceptron & MLP or ANN & [30; 31] \\ \hline
7. & Naive Bayes classifier & NB & [32] \\ \hline
8. & Support vector machines with linear kernel & Linear SVM & [33] \\ \hline
9. & Support vector machines with rbf kernel & RBF SVM & [33] \\ \hline
10. & Long short-term memory & LSTM & [34] \\ \hline
11. & Convolution neural network & CNN & [35] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Machine learning models used for well log data analysis.