Seismic Signal Denoising Method Based on Generalized Diffusion Model

Abstract

The denoising of seismic signals contributes to an enhanced signal-to-noise ratio, thereby improving the quality of subsequent research work. This study employs a diffusion probability model to diffuse and reverse seismic signals in the STEAD dataset, simulating the process of noise contamination and removal, achieving effective signal recovery under different noise conditions. In practical applications, this study overlays actual noise with Gaussian noise as synthetic noise for the forward diffusion process and extracts the time-frequency characteristics of the resulting noisy seismic signal as conditional assistance for model training. This approach overcomes the general diffusion model's reliance on pure Gaussian noise and enhances the model's generalization ability. The experimental results demonstrate that the model significantly improves the signal-to-noise ratio of seismic signals, which is of great significance for the advancement of subsequent research in the field of seismology.