Seismic Data Reconstruction Via A Hybrid CNN-Low-Rank Method

Abstract

Convolutional Neural Networks (CNNs) have shown promising results in seismic data reconstruction due to their exceptional ability to extract local features. However, incomplete seismic data may impede CNNs’ ability to discern meaningful features, leading to a degradation of reconstruction performance. Therefore, we propose a hybrid CNN-Low-Rank (CNN-LR) method which leverages Low-Rank (LR) techniques to recover data structure information, followed by detail refinement using a plug-and-play (PnP) trained CNN within an iterative framework. This approach utilizes LR techniques to restore inherent structural information, compensating for the absence of local information, thereby enabling CNNs to infer missing traces more accurately. Specifically, we first train a network to learn the mapping from low-rank recovered data patches with randomly selected rank parameters to complete data patches. Next, we incorporate the PnP trained CNN into an iterative framework, where low-rank recovery and network refinement are interleaved within each iteration. This creates a continuous reconstruction process until a termination condition is met. Performance evaluations of our proposed algorithm, conducted on synthetic and field datasets with a high missing trace ratio, demonstrate its superiority over the traditional low-rank method (multi-channel singular spectrum analysis, MSSA) and CNN-based method (U-net). Moreover, our approach shows strong performance in reconstructing large contiguous gaps and handling higher-dimensional seismic data (including 3D and 5D), broadening its applicability and robustness in practical seismic data reconstruction tasks.