## 4 Analysis of Prediction Results  
To evaluate the efficacy of our adaptive graph convolutional network for deep sequence and feature correlation graphs (ATF-GCN), we conducted ablation and comparative experiments with the methodologies detailed in Table 2.  
Table 3 details the hyperparameter configurations for the methods mentioned in Table 2. Our proposed model aims to strike a balance between capturing data patterns and controlling complexity, to avoid overfitting. To this end, we set three graph convolutional layers and the number of heads in the attention mechanism is set to two. The window size (\(S\)), a critical hyperparameter, substantially influences both predictive accuracy and processing duration. We determined the ideal window size by conducting a grid search from 5 to 50, to balance between the enhanced prediction accuracy with larger windows and the reduced computational expense associated with smaller windows. Setting the window size to 12 achieved an optimal prediction performance, without significantly increasing the computational cost. Additionally, we chose ReLU as the activation function, to leverage its efficiency and effectiveness in capturing non-linear relationships. The learning rate was set to a common starting value of \(1\times 10^{-4}\), which helped the model to converge stably and prevented overly rapid parameter updates. For the optimizer, we selected Adam, which  
Figure 3: Violin diagrams of logging parameters and porosity data. Figure 2: Histogram of the distribution of logging parameters and porosity data. effectively updates model parameters through its adaptive learning rate mechanism. At the same time, we set the dropout rate to 0.3 to achieve a moderate regularization effect, enhancing the model's generalization ability without harming the learning performance due to an excessively high dropout rate. To ensure a fair comparison between models, all models in the ablation experiment adopted a unified set of hyperparameters. This consistency ensured that any performance differences could be attributed to the model architecture itself. The BiGRU model was configured with 128 GRU units per layer. The choice of 128 units struck a balance between model capacity and the potential for overfitting. The learning rate was set to 0.001. We configured a dropout rate of 0.3 and opted for the ReLU activation function, aligning with the ablation experiment model ensemble that controlled the experimental variables. Since BiLSTM and BiGRU are analogous in deep learning, the parameter settings were consistent, ensuring that the experimental variables remained manageable. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiments** & **Abbreviations** & **Particulars** \\ \hline \multirow{4}{*}{Ablation Experiments} & ADF-GCN & Adaptive GCN for deep sequence and feature correlation graph (ours) \\  & AD-GCN & Adaptive GCN for deep sequence correlation graph \\  & AF-GCN & Adaptive GCN for feature correlation graph \\  & DF-GCN & GCN for deep sequence and feature correlation graph \\  & D-GCN & GCN for deep sequence correlation graph \\  & F-GCN & GCN for feature correlation graph \\ \hline \multirow{2}{*}{Comparison Experiments} & BiGRU [37] & Bidirectional Gated Recurrent Unit \\  & BiLSTM [38] & Bidirectional Long Short-Term Memory \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Abbreviations and descriptions of ablation and comparison experiments.