Application of Convolutional Neural Network in Automated Swell Noise Attenuation

Noise attenuation is a crucial and recurrent step in the seismic processing sequence. After noise attenuation, quality control (QC) is a mandatory process to ensure that the level of noise left in the data is acceptable and no signal leakage has occurred. This process is usually done by geophysicist and is time consuming and subjective. We train a U-Net convolutional neural network model to automatically perform the QC after swell noise attenuation and label the seismic samples as signal, noise, or signal leakage. We show that the classification of the acquired seismic data after the swell noise attenuation with the trained model is very reliable and robust and model is able to detect both residual noise and signal leakage. We also propose a framework to use the classification result to steer the denoise process in an automated fashion. If the model detects residual noise or signal leakage during the denoise process, the selected parameters are automatically tuned to produce the best possible result for each seismic record. We demonstrate that the automated denoise process outperforms the fixed parameters denoise process.