Abstract
Seismic data collected under complex field conditions often contain missing traces. Traditional theory-driven methods rely heavily on empirically selected parameters and struggle to reconstruct continuous missing traces effectively. With advancements in deep learning, various generative models have exhibited strong reconstruction capabilities. However, diffusion model-based methods face significant reconstruction time overhead due to their iterative sampling strategies. Existing transformer-based autoregressive methods flatten two-dimensional seismic data into one-dimensional sequences, disrupting the inherent two-dimensional structure and compromising the spatial locality of seismic information. To address these limitations, we propose a conditional autoregressive model based on next-scale prediction. Starting from the smallest scale, the model incrementally predicts larger-scale data using information from preceding smaller scales, ultimately achieving robust data reconstruction. This next-scale prediction approach avoids flattening the data, thereby preserving its spatial structure. Additionally, conditional constraints during autoregressive generation ensure that the predicted data at each scale remains consistent and aligns with the distribution of the known data. Reconstruction experiments on both field and synthetic datasets demonstrate that our method achieves superior reconstruction accuracy compared to existing approaches and effectively handles various complex missing data scenarios.