Diffusion models for multidimensional seismic noise attenuation and superresolution

Abstract

Seismic data quality proves pivotal to its interpretation, necessitating the reduction of noise and enhancement of resolution. Traditional and deep-learning-based solutions have achieved varying degrees of success on low-dimensional seismic data. We develop a deep generative solution for high-dimensional seismic data denoising and superresolution through the innovative application of denoising diffusion probabilistic models (DDPMs), which we refer to as MD Diffusion. MD Diffusion treats degraded seismic data as a conditional prior that guides the generative process, enhancing the capability to recover data from complex noise. By iteratively training an implicit probability model, we achieve a sampling speed 10 times faster than the original DDPM. Extensive training allows us to explicitly model complex seismic data distributions in synthetic data sets to transfer this knowledge to the process of recovering field data with unknown noise levels, thereby attenuating the noise and enhancing the resolution in an unsupervised manner. Quantitative metrics and qualitative results for 3D synthetic and field data demonstrate that MD Diffusion exhibits superior performance in high-dimensional seismic data denoising and superresolution compared with the UNet and seismic superresolution methods, especially in enhancing thin-layer structures and preserving fault features, and indicates the potential for application to higher-dimensional data.