## 4 Results and Discussion  
To evaluate the effectiveness of the proposed method, we have conducted blind testing by utilizing actual seismic data from wells from a field in the Malay basin. We gauged the predicted results qualitatively and quantitatively. The qualitative analysis was conducted by examining the background trend fitting and relative variation between predicted and actual elastic properties logs. Meanwhile, the quantitative analysis was mainly performed via the cross-correlation method to estimate correlation accuracy between predicted and  
Figure 3: Detailed workflow to conduct the deep learning network training. Figure 2: U-Net architecture with ResNet-18 as encoder. actual elastic properties logs. Figure 4 exhibits the Loss vs. Epoch diagnostic cross plot between training and validation loss trends for the network training. The training loss indicates the adaptability of the network model to fit the training data. In contrast, the validation loss indicates the flexibility of the trained network model to fit the new data. In general, a very good network model performance was observed for training and validation Loss trends for each elastic property, as the trends decrease with an increase in Epoch trends and start to stabilize around some point. Only marginal error contrast between validation and training trends was observed. Figures 5 and 6 demonstrate the well section window for the prediction results from two actual blind wells. The first column represents the litho-fluid log, which comprises the logs' normalization between clay volume, coal volume, and water saturation. The grey, blue, red, and black colors indicate clay, brine sand, hydrocarbon sand, and coal, respectively. The second column is P-Impedance, the third column is S-Impedance, and the fourth column is density. In each of these columns, the black line indicates the measured elastic properties logs, the blue line is the inversion results from the conventional approach, and the red line indicates inversion results from the proposed approach. The last column is actual seismic pseudo-gathers used as input for inversion. Overall, moderate to good correlation accuracy can be observed between measured elastic properties predicted and predicted elastic properties from both approaches qualitatively. Both approaches can capture the background trend and detect the relative variation of each elastic property. However, the inversion results from the proposed approach are relatively more stable, with less presence of ringing noises and closer proximity to the measured well logs compared to the inversion results from the conventional approach, where many mismatches and ringing noises were observed. Table 1 demonstrates the correlation accuracy between inversion results and measured logs for both approaches at blind well 1 and 2. There are significant improvements of up to 31% in correlation accuracy for elastic properties prediction based on the proposed approach compared to the conventional, especially for density. This indicates that the trained network model can efficiently capture the background trend and relative variation with proper magnitude and scaling for all elastic properties. Figure 4: Loss vs. Epoch diagnostic cross plot with between training and validation loss trend of (**a**) P-Impedance, (**b**) S-Impedance, and (**c**) density. Figure 5: Actual well logs (black), inversion result from conventional method (blue) and proposed method (red) of (**b**) P-Impedance (AI), (**c**) S-Impedance (SI), (**d**) density (RHO), predicted based on (**e**) actual seismic as input at blind well 2. Column (**a**) represents the litho-fluid log normalization that comprises of water saturation (SWT), clay volume (VCL), and coal volume (COAL) logs. Figure 6: Actual well logs (black), inversion result from conventional method (blue), and proposed method (red) of (**b**) P-Impedance (AI), (**c**) S-Impedance (SI), (**d**) density (RHO), predicted based on (**e**) actual seismic as input at blind well 2.