A random noise suppression method for seismic data based on diffusion model
As the random noise in seismic data is highly irregular,conventional denoising methods often fail to achieve satisfactory results,thereby hindering subsequent interpretation and analysis of seismic data.Therefor,a seismic signal denoising method based on the diffusion model is proposed.The forward diffusion process of this method involves adding a certain degree of noise to the seismic data,transforming it into noisy seismic data with a large amount of isotropic Gaussian noise.Then,the trained diffusion model is usedtoreconstruct the noisy data and improve its signal-to-noise ratio.The prediction network component is an improved U-Net network,which incorpo-rates attention modules and ResNet modules.These modules elevate the network's attention on important regions and mitigate gradient disappearance in deep networks.Both theoretical and practical data have verified the effective-ness of the proposed method.In terms of denoising effect,it significantly surpasses traditional denoising methods such as FX filtering and SVD,and it also outperforms classic deep lear-ning networks like CNN and GAN.This approach effectively preserves valid signals,thereby significantly enhancing seismic dataquality.
random noise suppressiondiffusion modelresidual moduleattentionmodule
万方数据
维普
