## 4 Data and Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Data** & **Regressor\_Name** & _MAE_ & _MSE_ & \(R^{2}\) & \(R\) & **Duration** \\ \hline AR + SP & XGB & 3,780 & 25.86 & 0.509 & 0.715 & 0.406 \\ \cline{2-7}  & RF & 4.449 & 33.01 & 0.373 & 0.724 & 0.490 \\ \cline{2-7}  & Lasso & 5.305 & 38.06 & 0.277 & 0.757 & 0.000 \\ \cline{2-7}  & LR & 4.627 & 33.40 & 0.365 & 0.770 & 0.000 \\ \cline{2-7}  & elasticinct & 6.809 & 60.94 & \(-\)0.158 & 0.000 & 0.000 \\ \cline{2-7}  & LGBM & 4.420 & 30.97 & 0.412 & 0.738 & 0.160 \\ \cline{2-7}  & Ridge & 5,770 & 80.78 & \(-\)0.535 & 0.490 & 0.020 \\ \cline{2-7}  & SVM & 6.554 & 58.63 & \(-\)0.114 & 0.178 & 0.000 \\ \cline{2-7}  & MLP & 4,870 & 34.74 & 0.340 & 0.708 & 0.547 \\ \hline AR + into & XGB & 2.976 & 15.93 & 0.682 & 0.829 & 0.709 \\ \cline{2-7}  & RF & 2.964 & 15.77 & 0.665 & 0.829 & 0.344 \\ \cline{2-7}  & Lasso & 4.666 & 28.84 & 0.423 & 0.723 & 0.000 \\ \cline{2-7}  & LR & 3.385 & 18.48 & 0.631 & 0.799 & 0.000 \\ \cline{2-7}  & elasticinct & 6.289 & 50.75 & \(-\)0.014 & 0.000 & 0.000 \\ \cline{2-7}  & LGBM & 2.875 & 14.52 & 0.710 & 0.845 & 0.149 \\ \cline{2-7}  & Ridge & 3.196 & 25.24 & 0.495 & 0.728 & 0.010 \\ \cline{2-7}  & SVM & 3.593 & 23.48 & 0.531 & 0.254 & 0.000 \\ \cline{2-7}  & MLP & 3,250 & 18.12 & 0.638 & 0.800 & 0.559 \\ \hline SP + into & XGB & 4,801 & 36.85 & 0.300 & 0.551 & 0.425 \\ \cline{2-7}  & RF & 5.013 & 41.81 & 0.206 & 0.535 & 0.320 \\ \cline{2-7}  & Lasso & 6.918 & 63.00 & \(-\)0.197 & \(-\)0.033 & 0.000 \\ \cline{2-7}  & LR & 5.208 & 39.53 & 0.249 & 0.587 & 0.000 \\ \cline{2-7}  & elasticinct & 6.809 & 60.94 & \(-\)0.158 & 0.000 & 0.000 \\ \cline{2-7}  & LGBM & 4,880 & 36.26 & 0.311 & 0.601 & 0.175 \\ \cline{2-7}  & Ridge & 5,883 & 119.05 & \(-\)1.262 & 0.229 & 0.010 \\ \cline{2-7}  & SVM & 6.140 & 60.65 & \(-\)0.152 & 0.235 & 0.000 \\ \cline{2-7}  & MLP & 6.380 & 62.02 & \(-\)0.178 & \(-\)0.271 & 0.573 \\ \hline AR+SP+ & XGB & 3,538 & 22.084 & 0.580 & 0.764 & 0.453 \\ \cline{2-7}  & RF & 4.032 & 27.889 & 0.470 & 0.754 & 0.496 \\ \cline{2-7}  & Lasso & 5.305 & 38.055 & 0.277 & 0.757 & 0.016 \\ \cline{2-7}  & LR & 4.188 & 26.522 & 0.496 & 0.806 & 0.000 \\ \cline{2-7}  & elasticinct & 6.809 & 60.940 & \(-\)0.158 & 0.000 & 0.000 \\ \cline{2-7}  & LGBM & 3.856 & 25.013 & 0.525 & 0.781 & 0.165 \\ \cline{2-7}  & Ridge & 5.637 & 96.590 & \(-\)0.835 & 0.439 & 0.020 \\ \cline{2-7}  & SVM & 6.366 & 55.389 & \(-\)0.052 & 0.154 & 0.000 \\ \cline{2-7}  & MLP & 4.538 & 31.637 & 0.399 & 0.726 & 0.569 \\ \hline Current method & 13.89 & 192.93 & 0.32 & 0.584 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._potential depended on the difference between the salinity of groundwater and the drilling fluid, for contrast and differentiable recording of SP, it was necessary to strictly adhere to the requirements for the preparation of the drilling fluid, which was often not observed in practice. Because the size of the dataset was small, the training time for all algorithms was less than 1 s.  
The LGBM regressor showed the best results when using (AR, Lith code) as the input parameters (\(R^{2}\) = 0.710). At the same time, when we could use only a part of the input parameters, other regression models could also be used. For example, a linear regression model performed well when we could only use SP, and we did not know the lithological code (\(R^{2}\) = 0.415). In cases where we could only use the AR values, the best result again showed LGBM (\(R^{2}\) = 0.604). In addition, linear regression (LR) showed a stable good result. The currently used methodology showed significantly worse results compared with machine learning models (\(R^{2}\) = 0.32). A comparison of the data calculated with accepted methodology with actual data showed that the RMSE was 13.89 and the linear correlation value was 0.584. This low accuracy was probably due to the fact that 0.5 m intervals were used for comparison, while the accepted methodology was designed for intervals with thickness more than 2 m.