# Enhancing Channelized Feature Interpretability Using Deep Learning Predictive Modeling
## 2 Methodology
### Python-Based Labeling Tool  
The essence of supervised learning methods highlights the capability of neural network algorithms to generalize training dataset in the unfamiliar scenarios and \"trained\" DL inferences to make accurate predictions [22]. In preparation for these, the 3D seismic data cube was 2 millisecond-sampled with a size of 1024 \(\times\) 256 (inlines, crosslines) and 64 layers of data were annotated in the z-direction. Figure 5 shows how fluvial facies are labeled in I-35 Lower Reservoir, in A Field, and categorized according to specific classes, as indicated in Table 2. Reservoir field issues, reservoir geometry, and interpreted depositional environments were considered to further understand the regional geology of Malay Basin. To ensure the accuracy of the labeling process, we executed Log Pattern Analysis from twelve nearby wells including one cored well to the annotated data layers. Using commercial packages, interpreters need to validate the quality of the labeled features and certify that no empty spaces were found to avoid misleading in classification by the neural network later. (7)  
\[\text{F1 Score}=\frac{2*\ T}{2 TP+TP+FN} \tag{8}\]  
Intersection of Union (IoU):  
The term measured accuracy of a set of object detections from a model when compared to the ground truth, where a group of pixels belong to Class \(i\) known as A\({}_{i}\) and the set of classified pixels is known as Class \(i\) as B\({}_{i}\). A higher IoU score means better consistencies between the predicted pixels and the ground truth. The intuition behind IoU is that if a geobody prediction is good and is similar to the ground truth, both the prediction and the ground truth label will share a large number of pixels. \[\text{IoU}=\frac{\left|\ \text{A}_{i}\ \cap\ \text{B}_{i}\ \right|}{\text{A}_{i}\ \cup\ \text{B}_{i}\ \right|} \tag{9}\]  
Mean Intersection of Union (Mean IoU): \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class I** **(Channel)**} & **Class II** & **Class III** & **Class IV and V** \\  & **(Point Bar)** & **(Mud-Filled Channel)** & **(Crevasse Splays and Crevasse Channel)** \\ \hline
7.12 & 4.15 & 0.16 & 88.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The percentage of pixels for each facies class in the training dataset of A Field. Figure 5: Detailed description of fluvial facies elements, as observed in time slice 1564 milliseconds. In order to measure the prediction accuracy of the trained model, mean Intersection of Union (mean IoU) originally from the Jaccard Index was applied. We applied a threshold value above 0.5, and hence the metrics validate parameters in semantic segmentation, which can be calculated by:  
\[\left(\frac{1}{n_{cl}}\right)\sum\frac{n_{ii}}{t_{i}+\sum n_{ii}-n_{ii}} \tag{10}\]  
where \(n_{cl}\) is the class number;  
\(n_{ii}\) is the number of pixels of class \(i\) predicted to belong to class \(j\); and  
\[t_{i}=\sum n_{ij} \tag{11}\]  
is the total number of pixels of class \(i\)[23]. A range of learning rates were tested, from \(5\times 10^{-6}\) to \(1\times 10^{-4}\). We chose a learning rate of \(5\times 10^{-5}\), which provides the fastest training time without adversely affect the training process by introducing unwanted instabilities. A weight decay multiplier was used to introduce regularization in mitigating the overfit issue. We used a batch size of 32 for 420 epochs because, beyond that, the benefit of increasing batch size is negligible and network performance is observed as reaching a plateau. In order to achieve highest mean IoU, selection of appropriate optimizer needs to be carefully considered during hyperparameter testing. An Adaptive Moment Optimizer (Adam) was selected as it is the improvised version of the conventional stochastic gradient descent procedures to update training weights iteratively through backpropagation. By observing the learning curve during training, we could identify any potential biases, convergence, and validate the performance of the neural network model.