Deghosting of marine seismic data is an important and challenging step in the seismic processing flow. We describe a novel approach to train a supervised convolutional neural network to perform joint source and receiver deghosting of single‐component (hydrophone) data. The training dataset is generated by demigration of stacked depth migrated images into shot gathers with and without ghosts using the actual source and receiver locations from a real survey. To create demigrated data with ghosts, we need an estimate of the depth of the sources and receivers and the reflectivity of the sea surface. In the training process, we systematically perturbed these parameters to create variability in the ghost timing and amplitude and show that this makes the convolutional neural network more robust to variability in source/receiver depth, swells and sea surface reflectivity. We tested the new method on the Marmousi synthetic data and real North Sea field data and show that, in some respects, it performs better than a standard deterministic deghosting method based on least‐squares inversion in the τ‐p domain. On the synthetic data, we also demonstrate the robustness of the new method to variations in swells and sea‐surface reflectivity.

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Removing the receiver ghost before migration provides better low and high frequency response as well as a higher signal-to-noise ratio. We recognize these benefits for preprocessing steps like multiple suppression and velocity analysis. In this paper, we modify a previously published bootstrap approach that self-determines its own parameters for receiver deghosting in a x t  window. Similarly to the x t  bootstrap method, the recorded data is first used to create a mirror data set through a 1D ray-tracing-based normal moveout correction method. The recorded and mirror data are then transformed into p   domain and used to jointly invert for the receiver-ghost-free data. We apply this new algorithm to two field data sets with: 1) constant streamer depth of 27 m; and 2) variable streamer depth from 10 to 50 m. Our deghosting method effectively removes the receiver ghost, and the resulting image has broader bandwidth and a higher signal-to-noise ratio.

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