  Seismic data noise processing is an important part of seismic exploration
data processing, and the effect of noise elimination is directly related to the
follow-up processing of data. In response to this problem, many authors have
proposed methods based on rank reduction, sparse transformation, domain
transformation, and deep learning. However, such methods are often not ideal
when faced with strong noise. Therefore, we propose to use diffusion model
theory for noise removal. The Bayesian equation is used to reverse the noise
addition process, and the noise reduction work is divided into multiple steps
to effectively deal with high-noise situations. Furthermore, we propose to
evaluate the noise level of blind Gaussian seismic data using principal
component analysis to determine the number of steps for noise reduction
processing of seismic data. We train the model on synthetic data and validate
it on field data through transfer learning. Experiments show that our proposed
method can identify most of the noise with less signal leakage. This has
positive significance for high-precision seismic exploration and future seismic
data signal processing research.
