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The incompleteness of the seismic data caused by missing traces along thespatial extension is a common issue in seismic acquisition due to the existenceof obstacles and economic constraints, which severely impairs the imagingquality of subsurface geological structures. Recently, deep learningbasedseismic interpolation methods have attained promising progress, while achievingstable training of generative adversarial networks is not easy, and performancedegradation is usually notable if the missing patterns in the testing andtraining do not match. In this paper, we propose a novel seismic denoisingdiffusion implicit model with resampling. The model training is established onthe denoising diffusion probabilistic model, where U-Net is equipped with themulti-head self-attention to match the noise in each step. The cosine noiseschedule, serving as the global noise configuration, promotes the highutilization of known trace information by accelerating the passage of theexcessive noise stages. The model inference utilizes the denoising diffusionimplicit model, conditioning on the known traces, to enable high-qualityinterpolation with fewer diffusion steps. To enhance the coherency between theknown traces and the missing traces within each reverse step, the inferenceprocess integrates a resampling strategy to achieve an information recap on theformer interpolated traces. Extensive experiments conducted on synthetic andfield seismic data validate the superiority of our model and its robustness tovarious missing patterns. In addition, uncertainty quantification and ablationstudies are also investigated.

Seismic Data Interpolation based on Denoising Diffusion Implicit Models with Resampling