Generative interpolation via diffusion probabilistic model

Abstract

Seismic data interpolation is essential in a seismic data processing workflow, recovering data from sparse sampling. Traditional and deep learning based methods have been widely used in the seismic data interpolation field and have achieved remarkable results. In this paper, we propose a seismic data interpolation method through the novel application of diffusion probabilistic models (DPM). DPM transform the complex end-to-end mapping problem into a progressive denoising problem, enhancing the ability to reconstruct complex situations of missing data, such as large proportions and large-gap missing data. The inter polation process begins with a standard Gaussian distribution and seismic data with missing traces, then removes noise iteratively with a Unet trained for different noise levels. Our#xD;proposed DPM-based interpolation method allows interpolation for various missing cases, including regularly missing, irregularly missing, consecutively missing, noisy missing, and different ratios of missing cases. The generalization ability to different seismic datasets is also discussed in this article. Numerical results of synthetic and field data show satisfactory interpolation performance of the DPM-based interpolation method in comparison with the f- x prediction filtering method, the curvelet transform method, the low dimensional mani fold method (LDMM) and the coordinate attention (CA)-based Unet method, particularly in cases with large proportions and large-gap missing data. Diffusion is all we need for seismic data interpolation.