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Seismic data noise processing is an important part of seismic explorationdata processing, and the effect of noise elimination is directly related to thefollow-up processing of data. In response to this problem, many authors haveproposed methods based on rank reduction, sparse transformation, domaintransformation, and deep learning. However, such methods are often not idealwhen faced with strong noise. Therefore, we propose to use diffusion modeltheory for noise removal. The Bayesian equation is used to reverse the noiseaddition process, and the noise reduction work is divided into multiple stepsto effectively deal with high-noise situations. Furthermore, we propose toevaluate the noise level of blind Gaussian seismic data using principalcomponent analysis to determine the number of steps for noise reductionprocessing of seismic data. We train the model on synthetic data and validateit on field data through transfer learning. Experiments show that our proposedmethod can identify most of the noise with less signal leakage. This haspositive significance for high-precision seismic exploration and future seismicdata signal processing research.

Seismic Data Strong Noise Attenuation Based on Diffusion Model and Principal Component Analysis