Transformer-Based Seismic Image Enhancement: A Novel Approach for Improved Resolution

Image enhancement is crucial for improving the resolution of seismic images obtained from band-limited data. While machine learning techniques, particularly the U-Net model, have shown significant progress in this area, they often require substantial computational resources and time. To address these challenges, we introduce a transformer-based approach for enhancing seismic image resolution, which incorporates convolutional layers, an average pooling layer, and an efficient transformer (ET). The ET leverages efficient multi-head attention (EMHA) to capture long-term dependencies among image blocks, focusing on the pixels within their contextual surroundings. In our proposed model, we use a combined loss function consisting of the mean square error (MSE) and the structural similarity (SSIM) to enhance the network's learning capability. By training the model on synthetic seismic data, we observe improved structural features, enhanced resolution, and effective denoising. Notably, our approach outperforms the U-Net model in terms of structural similarity and peak signal-to-noise ratio. Furthermore, we evaluate the pretrained model on several field datasets, yielding promising results compared to the benchmark method. This demonstrates the potential applicability and effectiveness of our proposed approach in real-world scenarios.