Super-resolution method based on a Transformer: a novel solution for improving the quality of stacked or migrated seismic data

Seismic data obtained after stacking or migration typically exhibit low dominant frequency, sparse spatial and temporal sampling, and may retain a low signal-to-noise ratio (SNR) even after stacking. To enhance the resolution of seismic data, we propose a deep learning-based single-step super-resolution (SR) method that simultaneously performs denoising, interpolation, and frequency extrapolation. We design a seismic data super-resolution Transformer (SDST) to extract and fuse local and global features from low-resolution seismic data, with particular emphasis on the amplitude characteristics and spatial continuity of effective events (e.g., horizons, folds, faults). Edge information from the input data is extracted using the Canny algorithm and used as a mask to generate an edge map, which is then concatenated with the raw data to form a two-channel input, thereby improving amplitude preservation and noise suppression. The SDST backbone comprises residual groups containing Swin Transformer layers which capture features within windows while reducing the computational cost of self-attention by restricting attention to finite-sized windows. Pixel shuffle layers are employed to efficiently upsample the output feature maps from the SDST backbone, thereby enhancing the details of geological structures. In addition, upsampling the input data through a skip connection helps preserve the output amplitudes, particularly for weak events. We prepare 3-dimensional (3D) synthetic seismic volumes with complex geological structures, half of which contain a mixture of strong- and weak-amplitude events. 2-dimensional (2D) slices are then randomly selected from these volumes to construct training datasets that better approximate the characteristics of field data for supervised learning. Numerical tests on both synthetic and field data from different exploration regions demonstrate the feasibility of the proposed method.