Deep-seismic-prior-based reconstruction of seismic data using convolutional neural networks

The reconstruction of seismic data with missing traces has been a long-standing issue in seismic data processing; deep learning methods have attracted significant attention in seismic data reconstruction. One barrier associated with these deep-learning based reconstruction methods is the need for large training datasets, which are difficult to acquire owing to physical or financial constraints in practice. A novel method for the recovery of incomplete seismic data without the need of training datasets was developed. Seismic prior is implicitly captured based on the particular CNN structure choice, referred to as the “deep-seismic-prior-based”. The learned network weights are the parameters that represent seismic data, and as the convolutional filter weights are shared for spatial invariance, the CNN structure can function as a regularizer to guide the network learning. The reconstruction is realized during the iterative process by minimizing the mean square error (MSE) between the network output and the original corrupted seismic data. Our method could handle both irregular and regular seismic data, and testing its performance using both synthetic and field data showed it was more advantageous compared with the singular spectrum analysis (SSA) and de-aliased Cadzow methods employed in the reconstruction of irregular and regular data, respectively. The experimental results showed that the proposed method provided better reconstruction performance than the SSA and Cadzow methods.