Deblending of Simultaneous-Source Seismic Data Based on Deep Convolutional Neural Network

Abstract

The simultaneous-source technique improves the acquisition efficiency in marine seismic exploration. A high-quality deblending procedure is an important step when implementing the simultaneous-source technique. Deblending is often implemented in a domain (other than the common-source domain) where the blending noise is incoherent. In this article, we present a denoising method based on a deep convolutional neural network (CNN) for deblending. The denoising generalization ability of CNN-based denoising on real seismic data is limited because the training dataset, especially the labeled data, is often difficult to acquire. To make the CNN applicable to real data, we generate the training dataset directly from the real common-shot blended record itself, which has the same dynamic seismic wavefield characteristics as the real data. The input dataset for the CNN is acquired by adding A to B. A is the random time-delay encoding data of each trace of the common-shot blended data, while B is the data of each trace of the common-shot blended data; then, the blended data naturally become labeled data. The CNN model obtained through deep learning is used to remove the blending noise to complete the separation of blended data. Numerical tests using synthetic data and real data show that our method can provide high-precision separation results.