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.

翻译：地理、物理或经济条件的限制常导致地震数据中存在缺失道，因此重建完整的地震数据是地震数据处理中的关键步骤。传统的地震数据重建方法需要选择多个经验参数，且难以处理大规模连续缺失数据。随着深度学习的发展，各类神经网络展现了强大的重建能力。然而，这些卷积神经网络采用的是一种点对点的重建方法，可能无法覆盖数据集的完整分布。因此，在处理具有复杂缺失模式的地震数据时，此类网络可能出现不同程度的性能下降。针对这一挑战，我们提出了一种面向三维地震数据的新型扩散模型重建框架。为约束扩散模型生成的结果，我们向扩散模型中引入了条件监督约束，基于待重建的输入数据对扩散模型的生成数据进行约束。我们将三维神经网络架构引入扩散模型，成功将二维扩散模型扩展至三维空间。此外，我们通过将缺失数据融入生成过程来优化模型的生成过程，从而获得一致性更高的重建结果。通过确定最优参数值的消融研究，我们的方法在现场数据集和合成数据集上均展现出更优的重建精度，有效处理了各类复杂缺失模式。我们的实现代码见https://github.com/WAL-l/SeisFusion。