## 3 Key Processes for Deep Learning-Based Seepage Field Prediction
### Feature Engineering  
In traditional feature-engineering methods, after data cleaning, correlation between data sets is generally explored by principal control factor analysis, such as Pearson correlation analysis, Spearman correlation analysis, Kendall rank correlation analysis, the chi-square test, etc. ; the influential factors are ranked by the size of the correlation, and those with higher correlation are selected as input parameters. However, these methods usually only consider the relationship between individual features and target variables, ignoring the possible interaction effects between features; when the number of features is large, a large number of irrelevant features may exist in high-dimensional data, interfering  
Figure 4: Flow chart of Deep Learning method for seepage field prediction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Author** & **Network Type** & **Scenario** & **Model** \\ \hline
2017 & [PERSON] et al. [11] & FeedForward NN & Petrophysical modeling & Ensemble ANN \\
2021 & [PERSON] et al. [10] & FeedForward NN & Core-based permeability & MLP \\
2023 & [PERSON] et al. [20] & FeedForward NN & Pore-throat prediction & DNN \\ \hline
2022 & [PERSON] et al. [21] & Convolutional NN & Multiphase field prediction & CNN + PDE \\
2023 & [PERSON] et al. [23] & Convolutional NN & Digital rock two-phase flow & 3D CNN \\
2024 & [PERSON] et al. [24] & Convolutional NN & Mud invasion depth prediction & CNN + Regression \\ \hline
2021 & [PERSON] et al. [12] & Time-series NN & Lithology parameter modeling & RNN + SVM \\
2023 & [PERSON] et al. [13] & Time-series NN & Dam seepage sequence modeling & AutoML–LSTM \\
2024 & [PERSON] et al. [22] & Time-series NN & Gas–water microseepage simulation & LSTM \\
2023 & [PERSON] et al. [15] & Graph NN & Well pattern optimization & Production-GNN \\
2023 & [PERSON] et al. [18] & Graph NN & Connectivity-based flow simulation & Graph Topology Net \\
2024 & [PERSON] et al. [16] & Graph NN & Multi-scale heterogeneity modeling & GraphSAGE \\
2020 & [PERSON] et al. [5] & Physics-Informed NN & Hidden fluid mechanics & PINN \\ \hline
2022 & [PERSON] et al. [2] & Physics-Informed NN & Pressure prediction in porous media & PR-DNN \\
2023 & [PERSON] et al. [19] & Physics-Informed NN & Sinusoidal-space learning & Sin-PINN \\ \hline
2023 & [PERSON] et al. [3] & Multimodal NN & Saturation prediction from seismic + logs & Sample Pool + Fusion \\
2024 & [PERSON] et al. [14] & Multimodal NN & Permeability coefficient inversion & RF–SBO Hybrid \\
2024 & [PERSON] et al. [4] & Multimodal NN & Spatio–temporal production prediction & CSTN (CNN + LSTM) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Representative applications of neural network methods in seepage modeling. with the results of the correlation analysis; in addition, feature selection by correlation alone may ignore important information in some features [18; 21; 22]. With the development of Artificial Intelligence, feature-engineering methods have become more abundant. [PERSON] et al. [12] used SVM and shallow ANN to model porosity and permeability, and then standardized, normalized, and processed the outliers of the logging data, so as to eliminate the noise points that obviously deviated from the normal trend [12]. [PERSON] et al. [2] used a physics-regularized DNN (PR-CNN) in the feature engineering stage to firstly normalize and structurally transform the non-homogeneous permeability field, and they screened the sampling points in the seepage region to avoid the unstable model training caused by the concentration of sampling in the area of gradient changes. In the subsequent patent research, [PERSON] et al. [2] used a Physical Differential Convolutional Neural Network (PR-CNN) in feature engineering, they performed differential convolutional conversion and normalization of pressure and saturation field data, and they integrated the residual expression of physical control equations into the convolution kernel structure, realizing the unity of the data structure and physical laws.