## 4 Results and Discussion
### Porosity Prediction Results  
For porosity prediction, the Support Vector Regression (SVR) machine learning model was utilized, which was trained on well-labeled data divided into training and test sets. In total, 70% of the data was used for model training, with the remaining 30% used as blind data to evaluate model performance. The prediction model employed a radial basis function (RBF) kernel with parameter C set to one. Setting \(\epsilon\) to \(10^{-3}\) lowered the model's margin of error, improving precision. In the case of the porosity empirical equation, we used the fit equation parameters for clean sandstone to estimate porosity based on [PERSON]'s equation, as previously described. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{2-5}  & & \(\mathbf{R^{2}}\) & **MSE** & **RMSE** \\ \hline \multirow{3}{*}{AVO Inversion} & \(V_{P}\) & 0.677 & 0.020 & 0.141 \\  & \(V_{S}\) & 0.542 & 0.018 & 0.131 \\  & \(\rho\) & 0.607 & 0.022 & 0.153 \\ \hline \multirow{3}{*}{BLI inversion} & \(V_{P}\) & 0.784 & 0.014 & 0.118 \\  & \(V_{S}\) & 0.646 & 0.013 & 0.108 \\ \cline{1-1}  & \(\rho\) & 0.711 & 0.016 & 0.125 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of \(\rho\) estimation and P- and S-wave velocities using AVO and BLI inversion methods. This table presents the \(R^{2}\), MSE, and RMSE values for each parameter estimated, corresponding to the results depicted in Figure 10.  
Although the models closely reflect the well-log data, Figure 12 illustrates variations in the prediction models' outputs for porosity values. Especially when compared to the model based on [PERSON]'s empirical equation, the SVR algorithm-based model closely matches the actual porosity records. This performance difference could be explained by the SVR model's hyperparameters' correctness about the empirical model's control and adjustment constants. The optimal hyperparameter configuration is most likely derived from effective pre-processing of the original data during the BLI inversion, as well as continuing adjustments performed throughout each iteration of the process. Figure 12 illustrates the differences in resolution capacities and influence between empirical and SVR models. Even when using the wavelet in the exact position, there are discrepancies in resolution across the outputs. The empirical model's higher vertical resolution allows for detailed observation of the surrounding geological features and structures near the target horizon. On the other hand, the SVR model exhibits better horizontal continuity, making it easier to understand the connectivity inside the objective horizon, and thus, supporting more correct interpretation attempts. Figure 13 presents the well logs for P- and S-wave velocities, density (\(\rho\)), and porosity (\(\phi\)). Each record displays the original data (black line), the early results with the SS4 wavelet (green line), the final results with the SS4 wavelet (blue line), and the Ricker wavelet (red line). This picture allows for a direct comparison of the results of the BLI inversion and the SVR predictive and empirical models. Mean square error (MSE) and Pearson correlation coefficient (CC) were chosen to evaluate capacity for prediction. These criteria were selected to offer a thorough evaluation of the model's accuracy and precision. To improve computational efficiency, analyses were performed on individual traces for each 2D model result. The comparative analysis was performed on a single trace of each 2D model output, yielding three evaluations for each wavelet (see Figure 14). Figure 11: The 2D seismic section shows the inverted findings for P- and S-velocities, as well as density inversion achieved using the proposed approach. These maps represent inversions performed with (**a**) the Ricker wavelet, the sparse spike wavelets from well No-6608-10-04 (**b**), and (**c**) the sparse spike wavelets from well No-6608-10-02.