# Enhancing Channelized Feature Interpretability Using Deep Learning Predictive Modeling
## 4 Discussion  
### Binary Classification Using Synthetic Model  
Figure 7 illustrates small SEAM dataset overlaid by five random north-south and west-east line sections to validate the robustness of the approach. As shown in Figure 8, green arrows indicate structures missed by seismic attributes (inset shown the sweetness attributes). From our observation, even though the size of the trained model is challengingly small (6.4%) compared to the overall SEAM dataset, predicted deepwater channels (Prediction column) matched very well with the labeled data (Ground Truth column), where the validation and test accuracy (in Mean IoU) reached 95% and 85%, respectively. This implies that the 2D U-Net is capable of producing accurate segmented channelized 3D volumes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**InputAttributes**} & \multirow{2}{*}{**Dropout**} & \multicolumn{4}{c}{**Facies Accuracy:**} & \multirow{2}{*}{**Overall Mean IoU**} \\ \cline{3-3} \cline{5-6}  & & **Class I** & **Class II** & **Class III** & **Class IV and V** \\ \hline \multirow{4}{*}{16 Features} & 0.0 & 0.671 & 0.762 & 0.344 & 0.904 & **0.670** \\  & 0.1 & 0.688 & 0.652 & 0.254 & 0.907 & **0.650** \\  & 0.2 & 0.652 & 0.785 & 0.328 & 0.905 & **0.667** \\  & 0.3 & 0.640 & 0.682 & 0.310 & 0.893 & **0.631** \\ \hline \multirow{4}{*}{9 Features} & 0.0 & 0.681 & 0.766 & 0.322 & 0.905 & **0.668** \\  & 0.1 & 0.702 & 0.757 & 0.302 & 0.907 & **0.651** \\ \cline{1-1}  & 0.2 & 0.615 & 0.700 & 0.256 & 0.872 & **0.611** \\ \cline{1-1}  & 0.3 & 0.666 & 0.668 & 0.293 & 0.882 & **0.631** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results in the two categories, with dropout variations, in the test dataset. All metrics are in the range of 0 to 1, with larger values being better. The best-performing model for every metric is highlighted in bold.  
Figure 6: Performance plot represents different dropout values ranging from 0 to 0.5 during validation stages. All experiments show a similar progress pattern before reaching plateau phase due to the stochasticity of data augmentations.  
Figure 8: Overall input dataset, label data and prediction results from the five (5) geological cross-sections. (Input Column): Green arrows indicate subtle distributary channel series unable to be captured by sweetness attributes. (Ground truth Column): The dataset was built using a blob-based method where facies have been re-classified into binary dataset. (Prediction Column): Good quality of ground truth dataset is capable of guiding the neural network in achieving higher prediction accuracy.  
Figure 7: Overview of training dataset from SEAM Phase I illustrating five (5) cross-sections (A–A), (B–B), (C–C), (D–D’) and (E–E’) cutting across SEAM Reservoir Model. Blue lines represent inline sections, and the remaining lines represents the crosslines.  
We used two scenarios to predict channels in the real dataset:  
* Binary case.
* Multi-facies case.  
In order to produce consistent results, the same algorithms were applied. However, detailed analysis will be discussed in the multi-facies case study, as it deals with the current scenarios in any real data.