## 4 Results and Discussion
### Porosity Prediction Results
It is worth noting that the best prediction occurs when we utilize the wavelet associated with the region surrounding the well. Furthermore, Figure 13 shows that the initial Bayesian Linear Inversion (BLI) employing the SS4 wavelet (green line) has a low signal-to-noise ratio, which is most likely due to a poor fit of the model's hyperparameters. Furthermore, the image depicts the findings after the iterative adjustment method was performed on the inversion model, demonstrating an increased signal-to-noise ratio (blue line) and a better fit when compared to the original results. The inversion results using the Ricker wavelet are also provided, with a strong signal-to-noise ratio despite a rather low fit to the original data. A comparison is made between the initial results (represented by the black line) and the predicted porosity values (represented by the blue line) in Figure 14. This comparison is based on two distinct models: the SVR machine learning model (represented by the red line) and the empirical model (represented by the blue line), estimated using [PERSON]'s equation. The Pearson correlation coefficient and MSE metrics are presented herein as iterative techniques for quantifying the extent of correction. The results were derived through an iterative process of contrasting the initial data with the results generated by the SVR and empirical models. Figure 14 additionally compares the initial porosity data and the results obtained from our predictive models (empirical and SVR) while modifying the initial wavelet. Furthermore, these results suggest that the SVR model fits better than the empirical predictive model; this is most likely due to the empirical model's constant values, which are approximations of ideal geological conditions (rock type and geological environment). It should also be noted that the prediction models, both empirical and SVR, perform better when using the SS4 wavelet, which is most likely due to its similarity to a high-frequency wavelet. The procedure was iterative, thus the Pearson's Correlation Coefficient (CC) and MSE were computed in each iteration to determine the values that provide the best fit to the models (see Table 3). Figure 12: The 2D seismic section depicts the four results of the provided predictive models applied to regions locating wells No-6608-10-02 and No-6608-10-04. (**a,c**) The porosity estimations produced from the model using [PERSON]’s empirical equation. Similarly, (**b,d**) show the porosity calculated using the Support Vector Regression (SVR) model. These techniques used the SS2 and SS4 wavelets, which were applied in their predetermined locations. Figure 14: A comparison between the porosity prediction findings and the original values (black line) is shown. Two different models were applied, to achieve the predictions utilizing different wavelets (Ricker, SS4, and SS2). The models are the empirical model (blue line), which is estimated from [PERSON]’s equation, and the Support Vector Regression (red line). These comparisons are related to well No-6608-10-04. The correlation coefficient (CC) and mean squared error (MSE) values are also reported, demonstrating that the Support Vector Regression (SVR) model exhibits superior performance compared to the Han models in terms of predictive accuracy. Figure 13: The well-log curves from No-6608-10-04 show the results obtained by using both the BLI inversion and prediction models for \(V_{p},V_{S}\), \(\rho\), and \(\phi\). The logs show the following inversion using data inverted (with the wavelet SS4) in the first iteration (green line) and the final iteration (blue line) with the same wavelet. The data are inverted with the Ricker wavelet (red line). It should be noted that inversion with the Ricker wavelet tends to smooth the estimated parameter.