## 4 Analysis of Prediction Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Parameters** & **Values** \\ \hline \multirow{8}{*}{Ablation Model Ensemble *} & Graph convolution layer & 4 \\  & The number of heads & 2 \\  & The window size & 12 \\  & Activation function & ReLU \\  & Learning rate & \(1\times 10^{-4}\) \\  & Optimizer & Adam \\  & Dropout & 0.3 \\  & Maximum iterations & 1000 \\  & Batch size & 32 \\ \hline \multirow{8}{*}{BiGRU} & The number of GRU units & 128 \\  & in each layer & \multirow{2}{*}{128} \\  & Activation function & ReLU \\  & Learning rate & \(1\times 10^{-4}\) \\ \cline{1-1}  & Optimizer & Adam \\ \cline{1-1}  & Dropout & 0.3 \\ \cline{1-1}  & Maximum iterations & 1000 \\ \cline{1-1}  & Batch size & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Deep learning model parameters. Table 4 presents the four evaluation metrics for the deep learning model: RMSE, \(R^{2}\), MAE, and MAPE. RMSE measures the difference between a model's predicted and actual values by squaring the error, averaging, and then taking the square root. A smaller RMSE indicates a better model fit. The formula for RMSE is shown in Equation (24). \(R^{2}\) evaluates how well a model explains the variance in the dependent variable, with values closer to 1 indicating better explanation. Equation (25) provides the formula for \(R^{2}\). MAE is the mean absolute error between the predicted and actual values, being less sensitive to outliers and reflecting overall model fit. The formula for MAE is given in Equation (26). MAPE measures the average relative error; a smaller MAPE indicates a better fit. Equation (27) shows the MAPE formula. \[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\bigl{(}\mathit{POR}_{i}-\mathit{ P\dot{OR}}_{i}\bigr{)}^{2}}, \tag{24}\]  
\[R^{2}=1-\sum_{i=1}^{N}\bigl{(}\mathit{POR}_{i}-\mathit{P\dot{OR}}_{i}\bigr{)}^{ 2}/\sum_{i=1}^{N}\bigl{(}\mathit{POR}_{i}-\overline{\mathit{POR}_{i}}\bigr{)}^{ 2}, \tag{25}\]  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\bigl{|}\mathit{P\dot{OR}}_{i}-\mathit{POR} _{i}\bigr{|}, \tag{26}\]  
\[\text{MAPE}=\frac{1}{N}\sum_{i=1}^{N}\biggl{|}\frac{\mathit{P\dot{OR}}_{i}- \mathit{POR}_{i}}{\mathit{POR}_{i}}\biggr{|}\times 100\%, \tag{27}\]  
where \(\mathit{POR}_{i}\) is the predicted porosity value, \(\mathit{P\dot{OR}}_{i}\) is the actual porosity value, \(\overline{\mathit{POR}_{i}}\) is the average porosity, and \(N\) is the number of sample points. Figures 4 and 5 display scatter plots of the true versus predicted values for the training and test sets, respectively. From the data in the figure, it can be seen that the ADF-GCN model (\(R^{2}=0.9509\)) has the closest scatter to the diagonal line, indicating that the model's predictions were closest to the true values on the training set, and the overall prediction accuracy was the highest. The scatter of the DF-GCN model (\(R^{2}=0.9235\)) is closer to the diagonal line than the other ablation model ensemble, indicating that the prediction accuracy of the model that considered both the deep sequence and the feature-related graph was higher. The model that took into account both the deep sequence and the feature-related graph could capture the correlation between the deep sequence and the features. Compared with the DF-GCN model (\(R^{2}=0.9235\)), the D-GCN model (\(R^{2}=0.8723\)), and the F-GCN model (\(R^{2}=0.8076\)), the ADF-GCN model (\(R^{2}=0.9509\)), the AD-GCN model (\(R^{2}=0.8937\)), and the AF-GCN model (\(R^{2}=0.8448\)) have scatters closer to the perfect line. This indicates that models based on the attention-adaptive adjacency matrix