Enhancing Seismic Data Denoising Through Multiscale Analysis Across Transformer and GAN

In geological exploration, due to various factors, raw seismic data often corrupted by random noise, posing significant challenges to data fidelity, signal-to-noise ratio (SNR), and resolution. Traditional denoising techniques are limited by their inability to fully consider spatiotemporal correlations within seismic data, reliance on fixed filter parameters, and inadequate modeling of complex noise, and they have limited effectiveness and applicability when processing complex seismic signals. Therefore, this study proposes an innovative seismic data denoising model named seismic transformer generative adversarial network (STGAN). This model combines Transformer technology with generative adversarial networks (GANs) to effectively remove noise from seismic data. By incorporating the GAN, the model can effectively learn complex noise features in seismic data and generate cleaner, more realistic data. Meanwhile, the adoption of Transformers makes it possible to captures the temporal dependencies of seismic signals, thus significantly improving the accuracy and efficiency of data processing. The model employs serial and parallel structures in the generator to effectively extract multiscale features and achieve a balance between denoising effect and computational efficiency. Additionally, traditional batch normalization (BN) is replaced with batch renormalization (BRN) to further improve the stability of the model and optimize model performance. Comparative experiments on synthetic and field datasets demonstrate that the STGAN model has better denoising performance than classical methods such as bicubic interpolation, nonlocal means algorithm, and DnCNN, providing a more accurate basis for further interpretation and analysis of seismic data.