Seismic deghosting using convolutional neural networks

Abstract

Ghost reflections deteriorate the quality of seismic data in towed-streamer acquisition, and various acquisition and processing solutions have been proposed to remove them from seismic data. A common issue with the proposed solutions is their limited ability to remove source-side ghosts because of the sparse source sampling. Satisfactory receiver-side deghosting solutions are facilitated by complementary measurements (e.g., particle motion data) for wavefield separation and also can be achieved using pressure data acquired at a single recording level only. We develop a solution based on convolutional neural networks (CNNs) to remove source- and receiver-side ghosts in the shot domain. The solution does not require complementary measurements, i.e., it can remove ghost reflections in conventional pressure data measured at a single recording level. Our method requires knowledge of the acquisition geometry to create training data that replicate the field acquisition geometry and require the ocean floor bathymetry to be known. A CNN learns to map ghost-contaminated gathers to corresponding ghost-free gathers through an iterative training process. We find that the CNN-based deghosting operator can remove ghost reflections from previously unseen data and demonstrate that the solution generalizes well when training is done on models unrelated to the actual field geology.