  Seismic data frequently exhibits missing traces, substantially affecting
subsequent seismic processing and interpretation. Deep learning-based
approaches have demonstrated significant advancements in reconstructing
irregularly missing seismic data through supervised and unsupervised methods.
Nonetheless, substantial challenges remain, such as generalization capacity and
computation time cost during the inference. Our work introduces a
reconstruction method that uses a pre-trained generative diffusion model for
image synthesis and incorporates Deep Image Prior (DIP) to maintain data
consistency when reconstructing missing traces in seismic images. The proposed
method has demonstrated strong robustness and high reconstruction capability
for field and synthetic seismic images with different levels of structural
complexity, even in scenarios where test images were outside the training
domain. This indicates that our method can handle the high geological
variability of different exploration targets. Additionally, compared to other
state-of-the-art seismic reconstruction methods using diffusion models, our
approach reduces the number of neural function evaluations by up to 4x.
