## 2 Methods
### The Structure of the Hybrid Network  
The Inception module replaces traditional convolution layers to enhance feature extraction capabilities within the proposed model. The Inception module can capture multi-scale feature information by simultaneously using different-sized convolution kernels to process the input data. In geological data analysis, logging datasets have different scale features, such as subtle stratigraphic variations and large-scale geological structures; the use of the Inception module helps to extract these multi-scale features and improve the model's understanding of the geological data. Additionally, the design of the Inception module allows the network to simultaneously learn features at different scales at the same layer without having to go through multiple separate convolution layers, reducing parameter count, and boosting computational efficiency.  
The main objective of attention mechanism is to intensify the model's emphasis on significant features within the input data, enabling more effective learning and utilization of key information. In geological data analysis, stratigraphic datasets often contain various noise and redundant information, and the attention mechanism helps the model prioritize crucial features, thereby enhancing prediction accuracy and analysis. In addition, the attention mechanism dynamically adjusts feature weights based on different segments of the inputs, thus enabling the model to focus attention on features across different locations and scales depending on contextual information. This adaptive adjustment enhances the flexibility and adaptability of the model to better accommodate changes and complexities within geologic datasets.  
The BiLSTM module can learn features from the input sequence in both the forward and backward directions to more accurately capture the long-term dependencies within the sequence data [41]. When analyzing geological data, particularly stratigraphic information with complex spatio-temporal dependencies, the dataset used in this experiment organizes multiple logging datum in depth order, which also exhibits strong spatial relationships. The BiLSTM module is adept at analyzing the time evolution patterns in geological data, thus enabling accurate prediction of variables.  
The structure of the proposed network is shown in Figure 5.  
The overall network structure comprises a combination of modules, each responsible for different data processing and feature learning tasks, facilitating the prediction and analysis of logging data, and ultimately realizing the S-wave prediction.  
The input layer receives the feature vectors from logging data as input to the model. The Inception module conducts feature extraction through several branches. A dropout layer is incorporated to prevent overfitting by randomly deactivating neurons, enhancing model generalization. The attentional mechanism assigns weights to features, emphasizing important ones, thereby enhancing model expressiveness and accuracy. BiLSTM processes sequential data to capture temporal dependencies and features, combining output sequences from both forward and backward LSTMs for comprehensive temporal feature information. The fully connected layer maps the output features of the BiLSTM to the final predicted output space, followed by an activation function to generate model predictions. The output layer produces final model predictions.