## 3 Key Processes for Deep Learning-Based Seepage Field Prediction
### Neural Network Construction Methods  
With the deepening of Deep Learning for seepage field prediction, researchers have also begun to try different neural network construction methods to enhance the prediction ability of the model in different scenarios and under different objectives. 1. FeedForward Neural network model: A FeedForward Neural network is a kind of Deep Neural Network (DNN) with a simple structure but a powerful function, which is suitable for small- and medium-scale data modeling tasks. It can be used for single-point response prediction, such as pressure and saturation, by capturing the higher-order relationships between input and output variables through multi-layer linear and nonlinear transformations. For example, [PERSON] et al. [2] used physics-regularized DNN for seepage field prediction, they embedded the single-phase seepage control equation in the form of residuals into the loss function, they realized automatic constraints on physical laws during the model training process, and they effectively improved the physical consistency and generalization ability of the pressure prediction [2]. [PERSON] et al. [15] developed a hybrid modeling framework that integrates nonlinear coupled seepage control equations with deep FeedForward Neural networks to predict the evolution of pressure and saturation in complex reservoirs. The model obtains initial samples through numerical simulation and inputs the coupled pressure and saturation time series into the DNN for regression modeling, which realizes the joint data-physical-structural drive. Compared with traditional numerical methods, the model has better performance in simulation speed and predicting response trends, and it is suitable for the rapid assessment and response prediction of seepage behavior under complex injection and mining regimes [35; 36; 37; 38]. [PERSON] et al. [20] applied DNNs to the task of predicting pore throat features in micro-scale core images, and they constructed a seepage structure feature learning model based on parameters from well logging and core images. The model predicts the pore throat radius distribution with multidimensional static inputs (porosity, image features, gray moments, etc. ), which is used to reflect the influence of the core microstructure on the biphasic seepage capacity. The input variables were strictly screened and normalized in the study, and the nonlinear mapping between pore structure and seepage capacity was successfully established by a FeedForward Neural network, which provided a novel alternative path for seepage parameter inversion [39; 40; 41; 42]. Overall, DNN-like models are suitable for small- and medium-scale parameter prediction tasks, with their advantages of simple structure and efficient computation, and they can still play an important role in seepage modeling if combined with physical constraints or multivariate fusion mechanisms [43; 44; 45; 46]. 2. Spatio-temporal prediction network model: In order to more fully explore the influence of time series features on the evolution process of seepage response variables (e.g., pressure, WC, saturation, etc.) in the production data of reservoirs during the high WCP, some researchers have adapted and improved the structure of temporal neural networks, such as traditional RNNs, LSTMs, GRUs, etc., in order to enhance the ability of the model to portray complex nonlinear dynamic processes. In addition, some studies have also introduced heuristic optimization methods, such as genetic algorithms and particle swarms, to adaptively adjust the model hyper-parameters, thus further improving the training efficiency and time-series prediction accuracy, and providing a more stable and reliable technical path for modeling dynamic seepage behavior [47]. For example, [PERSON] et al. [13] proposed an integrated network model based on RNN in hydraulic seepage modeling, combining time-series clustering and automatic feature extraction to achieve dynamic identification and modeling of seepage risk from multivariate monitoring data; [PERSON] et al.