Unsupervised Seismic Random Noise Suppression Based on Local Similarity and Replacement Strategy

Improving the signal-to-noise ratio and suppressing random noise in seismic data is critical for high-precision processing. Although deep learning-based algorithms have gained popularity as denoising methods, they suffer from poor generalization ability, resulting high training set construction cost computation cost. To address this problem, we propose an unsupervised method that includes improved strategy based on local similarity replacement, a corresponding method, network UNet. Our takes advantage of convergence allows direct test region, effectively solving problems associated with methods using ability while improving performance. In addition, our specifically designed incorporates various improvements could further enhance effectiveness. outperforms traditional demonstrated by tests synthetic field data, superior performance attenuation reflection event reconstruction.