  Geographical, physical, or economic constraints often result in missing
traces within seismic data, making the reconstruction of complete seismic data
a crucial step in seismic data processing. Traditional methods for seismic data
reconstruction require the selection of multiple empirical parameters and
struggle to handle large-scale continuous missing data. With the development of
deep learning, various neural networks have demonstrated powerful
reconstruction capabilities. However, these convolutional neural networks
represent a point-to-point reconstruction approach that may not cover the
entire distribution of the dataset. Consequently, when dealing with seismic
data featuring complex missing patterns, such networks may experience varying
degrees of performance degradation. In response to this challenge, we propose a
novel diffusion model reconstruction framework tailored for 3D seismic data. To
constrain the results generated by the diffusion model, we introduce
conditional supervision constraints into the diffusion model, constraining the
generated data of the diffusion model based on the input data to be
reconstructed. We introduce a 3D neural network architecture into the diffusion
model, successfully extending the 2D diffusion model to 3D space. Additionally,
we refine the model's generation process by incorporating missing data into the
generation process, resulting in reconstructions with higher consistency.
Through ablation studies determining optimal parameter values, our method
exhibits superior reconstruction accuracy when applied to both field datasets
and synthetic datasets, effectively addressing a wide range of complex missing
patterns. Our implementation is available at
https://github.com/WAL-l/SeisFusion.
