SeisRDT: Latent Diffusion Model Based On Representation Learning For Seismic Data Interpolation And Reconstruction

Due to limitations such as geographic, physical, or economic factors, collected seismic data often contain missing traces. Traditional reconstruction methods require extensive empirical parameter tuning and perform poorly on large contiguous sections of missing traces. Recent advance in deep learning have led to diffusion models with strong reconstruction capabilities. However, UNet-based diffusion models are computationally intensive and struggle to learn correlations between different traces. To address these challenges, we propose SeisRDT—a latent diffusion transformer based on representation learning for seismic data reconstruction. This uses a new architecture that we propose, the representation diffusion transformer architecture, which incorporates a relative positional bias that is added when calculating attention. It also employs a mask modeling scheme based on representation learning to enhance reconstructed data amplitude distribution consistency. The combination of these features enables the method to have global information awareness of the seismic dataset, which is especially beneficial when there are large contiguous gaps in the data. Despite having these advantages that improve the quality of results, the proposed method has smaller computational resource requirements than most of the alternative approaches we compare it against. This is in part achieved due to its use of a pre-trained data compression model that compresses data into a latent space. Experiments on field and synthetic datasets show that our method achieves higher reconstruction accuracy than some existing methods and can handle various complex missing data scenarios. However, as the SeisRDT learns the probability distribution of the training data, it may not generalize well to datasets with significantly different distributions.