Transformer for seismic image super-resolution

TL;DR

This paper introduces a deep learning-based Transformer model for seismic image super-resolution that simultaneously denoises, interpolates, and extrapolates frequency, enhancing seismic image clarity and resolution in a single step.

Contribution

The proposed SIST model uniquely combines local and global feature extraction with edge-aware input processing and residual Swin-Transformer blocks for improved seismic image super-resolution.

Findings

Effective on synthetic and field data

Preserves amplitude and enhances weak signals

Reduces computational cost with residual Swin-Transformer blocks

Abstract

Seismic images obtained by stacking or migration are usually characterized as low signal-to-noise ratio (SNR), low dominant frequency and sparse sampling both in depth (or time) and offset dimensions. For improving the resolution of seismic images, we proposed a deep learning-based method to achieve super-resolution (SR) in only one step, which means performing the denoising, interpolation and frequency extrapolation at the same time. We design a seismic image super-resolution Transformer (SIST) to extract and fuse local and global features, which focuses more on the energy and extension shapes of effective events (horizons, folds and faults, etc.) from noisy seismic images. We extract the edge images of input images by Canny algorithm as masks to generate the input data with double channels, which improves the amplitude preservation and reduces the interference of noises. The residual groups containing Swin-Transformer blocks and residual connections consist of the backbone of SIST, which extract the global features in a window with preset size and decrease computational cost meanwhile. The pixel shuffle layers are used to up-sample the output feature maps from the backbone to improve the edges, meanwhile up-sampling the input data through a skip connection to enhance the amplitude preservation of the final images especially for clarifying weak events. 3-dimensional synthetic seismic volumes with complex geological structures are created, and the amplitudes of half of the volumes are mixtures of strong and weak, then select 2-dimensional slices randomly to generate training datasets which fits field data well to perform supervised learning. Both numerical tests on synthetic and field data in different exploration regions demonstrate the feasibility of our method.