## 4 Data and Results  
In total, there were 3558 intervals in the collected dataset, with a thickness of 0.5 to 2.2 m. As input parameters, sections of the AR and SP curves with a thickness of 0.5 m, recorded with a step of 0.1 m, as well as the lithological code of the rock, determined by the experts, were used. During the experiments, nine regression models were tested: XGB, RF, Lasso, Linear regression, Elastic Net, LGBM, Ridge, SVM, and ANN (hidden_layer_size = 91) on all combinations of input parameters (AR, SP, AR + SP, AR + lithocode, SP + lithocode, and AR + SP + lithocode). The XGB, RF, and LGBM models did not require data normalization (more precisely, normalization of input parameters can degrade the performance of these models), while other models worked better with normalized data. The normalization was performed using the standard function MinMaxScaler() included in the sklearn package. The results of the experiments are shown in Table 3. \begin{table}
\begin{tabular}{c c c} \hline
**Evaluation Index** & **Equation** & **Explanation** \\ \hline Determination coefficient & \(\begin{array}{c}R^{2}=1-\frac{SS_{\text{res}}}{SS_{\text{st}}}\\ SS_{\text{res}}=\sum\limits_{i=1}^{n}\left(y^{(i)}-h^{(i)}\right)^{2}\\ SS_{\text{tot}}=\sum\limits_{i=1}^{n}\left(y^{(i)}-\hat{y}\right)^{2},\bar{y}= \frac{1}{n}\frac{\bar{n}}{i=1}y^{(i)}\\ \end{array}\) \\ \hline Linear correlation coefficient (or Pearson correlation coefficient) & \(\begin{array}{c}R(y,h)=\frac{\sum_{i=1}^{n}\left(h-\bar{h}\right)(y_{i}-\bar{ y})}{\sum_{i=1}^{n}\left(y_{i}-\bar{y}\right)^{2}\sum_{i=1}^{n}\left(h-\bar{h} \right)^{2}},\\ \end{array}\) & where \(\bar{h}=\frac{1}{\bar{h}}\sum_{i=1}^{n}h_{i}\). \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Input Data** & **Regressor\_Name** & _MAE_ & _MSE_ & \(R^{2}\) & \(R\) & **Duration** \\ \hline AR & XGB & 3.432 & 21.94 & 0.562 & 0.759 & 0.557 \\ \cline{2-6}  & RF & 3.519 & 22.96 & 0.541 & 0.739 & 0.320 \\ \cline{2-6}  & Lasso & 4.666 & 28.84 & 0.423 & 0.723 & 0.000 \\ \cline{2-6}  & LR & 3.786 & 23.18 & 0.537 & 0.733 & 0.016 \\ \cline{2-6}  & elasticnet & 6.289 & 50.75 & \(-\)0.014 & 0.000 & 0.000 \\ \cline{2-6}  & LGBM & 3.397 & 19.81 & 0.604 & 0.780 & 0.157 \\ \cline{2-6}  & Ridge & 3.488 & 21.57 & 0.569 & 0.760 & 0.010 \\ \cline{2-6}  & SVM & 3.613 & 25.68 & 0.487 & 0.193 & 0.000 \\ \cline{2-6}  & MLP & 3.999 & 23.88 & 0.523 & 0.736 & 0.495 \\ \hline SP & XGB & 6.4580 & 63.01 & 0.259 & 0.172 & 0.731 \\ \cline{2-6}  & RF & 6.5580 & 75.65 & \(-\)0.312 & 0.036 & 0.357 \\ \cline{2-6}  & Lasso & 6.2890 & 50.75 & 0.386 & 0.000 & 0.000 \\ \cline{2-6}  & LR & 6.2570 & 49.30 & 0.415 & 0.277 & 0.000 \\ \cline{2-6}  & elasticnet & 6.2890 & 50.75 & 0.386 & 0.000 & 0.000 \\ \cline{2-6}  & LGBM & 6.2600 & 50.60 & 0.389 & 0.118 & 0.151 \\ \cline{2-6}  & Ridge & 6.4590 & 55.56 & 0.289 & 0.104 & 0.010 \\ \cline{2-6}  & SVM & 6.9040 & 81.00 & \(-\)0.419 & 0.171 & 0.000 \\ \cline{2-6}  & MLP & 7.5120 & 80.91 & \(-\)0.217 & \(-\)0.229 & 0.569 \\ \hline \end{tabular}
\end{table} 
Table 3: Assessments of the performance of the models trained on the exploration wells. In the last row, for comparison, the results of the calculations according to the currently accepted method are given. It can be seen that the best set of input data was the combination of AR + lithocode, while the use of SP significantly worsened the accuracy of the models. This was probably due to the low quality of the PS curve recording. Since the recorded