## 3 Experimental Process and Results  
### Data Preparation  
To rigorously validate the model's effectiveness and generalization ability, this study constructs a hybrid dataset system encompassing both official standard datasets and synthetic datasets. Specifically, synthetic seismic profile signals are generated using the precise modeling capabilities of MATLAB (2019b), while official seismic data is sourced from the authoritative Open SEG-Y platform--a benchmark repository in the field of seismic data that provides reliable real-world data for experimentation. During data preparation, this study prioritizes diversity in scales and frequencies of seismic signals, selecting two distinct seismic profile datasets for model training and validation:  
Figure 10: Structure diagram of the residual channel attention mechanism. Figure 9: Schematic diagram of channel attention mechanism. Simulated dataset: 6 s duration, 9030 horizontal traces, 4 ms vertical sampling interval; 250 Hz sampling frequency, effective frequency 10-100 Hz. Official dataset: 9.2 s duration, 400,000 horizontal traces, 8 ms vertical sampling interval; 125 Hz sampling frequency, effective frequency 5-50 Hz. Their selection stems from complementary scale and frequency traits: the simulated dataset excels in temporal resolution and high-frequency coverage (reflecting fine medium-to-high frequency geological signals), while the official dataset is representative in horizontal coverage and low-frequency traits (embodying macro medium-to-low frequency geological scales). Together, they cover common ranges in seismic exploration, providing diverse training inputs to enhance the model's adaptability and generalization in practical trace restoration. Considering the prevalent issue of missing seismic traces in actual exploration, this research systematically simulates data loss scenarios under natural acquisition conditions by setting three different decimation ratios: 0.1, 0.3, and 0.5. Figure 11, The original data and their corresponding decimation ratios of 0.1, 0.2, 0.3, visually compares the original intact seismic profile with its decimated counterparts, providing a visual demonstration of the characteristic changes in signals under varying degrees of data loss. In the experimental demonstration section, given the universality of the proposed method across both dataset types, the official dataset is prioritized as a representative case to illustrate the processing workflow. This strategy mitigates information redundancy from repetitive demonstrations while maintaining a rigorous focus on the core methodology. By conducting an in-depth analysis of this representative case, we systematically validate the algorithm's consistent superiority across diverse data sources, thereby establishing a robust empirical and theoretical foundation for subsequent model evaluations. To meet the sample requirements for model training and enhance dataset diversity, this study employs a multi-dimensional data augmentation strategy involving seven transformation operations: vertical flipping, 90\({}^{\circ}\) counterclockwise rotation, 90\({}^{\circ}\) rotation followed by vertical flipping, 180\({}^{\circ}\) rotation, 180\({}^{\circ}\) rotation followed by flipping, 270\({}^{\circ}\) rotation, and 270\({}^{\circ}\) rotation followed by flipping. Through this combination of techniques, a total of 5520 seismic data slices were generated. During data partitioning, the conventional practices of medium-sized datasets in the field of seismic profile signal reconstruction were referred to in order to balance the sufficiency of training data and the stability of evaluation. A stratified random split with an 8:1:1 ratio was applied to construct training, testing, and validation sets. Figure 12, The original data slice and its corresponding thinned slice, presents randomly selected examples of augmented seismic slices, visually illustrating the feature distribution of the enhanced dataset.