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| Citation: | Wu Di, Liu Gang, Chen Qiyu, Cui Zhesi, Fang Hongfeng, Zhang Ce, 2026. Seismic Image Super-Resolution Reconstruction Based on Enhanced Residual Feature Distillation. Earth Science, 51(9): 3501-3512. doi: 10.3799/dqkx.2026.076
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Seismic image super-resolution reconstruction is of great significance for improving the accuracy of seismic interpretation and geological modeling. However, existing methods often suffer from large model sizes and high computational costs, and exhibit limited detail recovery capability under strong noise and complex geological structures. To address these issues, this paper proposes an enhanced residual feature distillation generative adversarial network (ERFDN-GAN) for seismic image super-resolution reconstruction. The network is built upon enhanced residual feature distillation blocks (ERFDBs), where a feature distillation mechanism is employed to reduce model complexity. Spatial attention and channel attention mechanisms are further introduced to enhance feature representation capability. Experimental results demonstrate that the proposed method achieves overall superior performance compared with representative methods in terms of PSNR and SSIM, while maintaining low model complexity and stable reconstruction performance under complex scenarios. The proposed ERFDN-GAN method effectively balances computational efficiency and reconstruction quality, providing technical support for seismic interpretation, geological modeling, and the intelligent acquisition and mining of multi-source geoscience data.
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