  Noise is one of the primary sources of interference in seismic exploration.
Many authors have proposed various methods to remove noise from seismic data;
however, in the face of strong noise conditions, satisfactory results are often
not achievable. In recent years, methods based on diffusion models have been
applied to the task of strong noise processing in seismic data. However, due to
iterative computations, the computational efficiency of diffusion-based methods
is much lower than conventional methods. To address this issue, we propose
using an improved Bayesian equation for iterations, removing the stochastic
terms from the computation. Additionally, we proposed a new normalization
method adapted to the diffusion model. Through various improvements, on
synthetic datasets and field datasets, our proposed method achieves
significantly better noise attenuation effects compared to the benchmark
methods, while also achieving a several-fold increase in computational speed.
We employ transfer learning to demonstrate the robustness of our proposed
method on open-source synthetic seismic data and validate on open-source field
data sets. Finally, we open-sourced the code to promote the development of
high-precision and efficient seismic exploration work.
