Residual learning with feedback for strong random noise attenuation in seismic data

In random seismic noise attenuation, when the noise en-ergy is higher than or close to a signal, it is difficult to dis-tinguish the signal from the noise. This random noise is relatively strong compared to the signal and is called strong random noise. We have developed a deep learning frame-work to recover the signal from the strong random noise. The framework is based on a residual learning network and feedback connection and is called the feedback residual network. The residual network (ResNet) suppresses random noise through residual fitting and improves the network's training efficiency. The feedback connection allows the framework to process data in iterations. In each iteration, the feedback connection proportionally combines the input and output of the ResNet to reconstruct a new input with a lower noise level. This enhances denoising performance by asymptotically decreasing the input noise level and retriev-ing the remaining signals from the estimated noise, thereby reducing the difficulty of strong random noise attenuation. We terminate the feedback iterations according to the energy change of the estimated noise in each iteration. Synthetic and field examples demonstrate that our network can effec-tively attenuate the strong random noise.