Multi-model stacked structure based on particle swarm optimization for random noise attenuation of seismic data

Random noise attenuation is a fundamental task in seismic data processing aimed at improving the signal-to-noise ratio of seismic data, thereby improving the efficiency and accuracy of subsequent seismic data processing and interpretation. To this end, model-based and data-driven seismic data denoising methods have been widely applied, including f-x deconvolution, K-singular value decomposition, feed-forward denoising convolutional neural network and U-Net (an improved fully convolutional neural network structure), which have received widespread attention for their effectiveness in attenuating random noise. However, they often struggle with low-signal-to-noise ratio data and complex noise environments, leading to poor performance and leakage of effective signals. To address these issues, we propose a novel approach for random noise attenuation. This approach employs a multi-model stacking structure, where the parameters governing this structure are optimized using a particle swarm optimizer. In the model-based denoising method, we choose the f-x deconvolution method, whereas in the data-driven denoising method, we choose K-singular value decomposition for shallow learning and U-Net for deep learning as components of the multi-model stacking structure. The optimal parameters for the multi-model stacking structure are obtained using a particle swarm optimizer, guided by the proposed novel hybrid fitness function incorporating weighted signal-to-noise ratio, structural similarity and correlation parameters. Finally, the effectiveness of the proposed method is verified with three synthetic and two real seismic datasets. The results demonstrate that the proposed method is effective in attenuating random noise and outperforms the benchmark methods in denoising both synthetic and real seismic data.