  The incompleteness of the seismic data caused by missing traces along the
spatial extension is a common issue in seismic acquisition due to the existence
of obstacles and economic constraints, which severely impairs the imaging
quality of subsurface geological structures. Recently, deep learningbased
seismic interpolation methods have attained promising progress, while achieving
stable training of generative adversarial networks is not easy, and performance
degradation is usually notable if the missing patterns in the testing and
training do not match. In this paper, we propose a novel seismic denoising
diffusion implicit model with resampling. The model training is established on
the denoising diffusion probabilistic model, where U-Net is equipped with the
multi-head self-attention to match the noise in each step. The cosine noise
schedule, serving as the global noise configuration, promotes the high
utilization of known trace information by accelerating the passage of the
excessive noise stages. The model inference utilizes the denoising diffusion
implicit model, conditioning on the known traces, to enable high-quality
interpolation with fewer diffusion steps. To enhance the coherency between the
known traces and the missing traces within each reverse step, the inference
process integrates a resampling strategy to achieve an information recap on the
former interpolated traces. Extensive experiments conducted on synthetic and
field seismic data validate the superiority of our model and its robustness to
various missing patterns. In addition, uncertainty quantification and ablation
studies are also investigated.
