Attenuating coherent environmental noise in seismic data via the U-net method - Wu, Yu-Tai; Stewart, Robert R. (Frontiers in Earth Science, 2023)
Noise attenuation is a key step in seismic data processing to enhance desired signal features, minimize artifacts, and avoid misinterpretation. However, traditional attenuation methods are often time-consuming and require expert parameter selection. Deep learning can successfully suppress various types of noise via a trained neural network, potentially saving time and effort while avoiding mistakes. We test a U-net method to assess its usefulness in attenuating repetitive coherent events (e.g., pumpjack noise) and to investigate the influence of gain methods on denoising quality. We used the U-net because it preserves fine-scale information during training. Its performance is controlled by network parameters and improved by minimizing misfits between true data and network estimates. A gain method is necessary to avoid the network’s parameter optimization being biased toward large values in data. We first generated synthetic seismic data with added noise for training. Next, we recovered amplitudes using automatic gain control (AGC) or a 2D AGC (using adjacent traces’ amplitudes). A back-propagation algorithm then minimized the Euclidean-norm cost function to optimize the network parameters for better performance. The updating step size and direction were determined by an adaptive momentum optimization method. Finally, we removed the gain effect and evaluated the denoising quality using a normalized root-mean-square error (RMSE). Based on the RMSE, the data pre-processed by the 2D AGC performed better with RMSE decreasing from 0.225 to 0.09. We also assessed the limitations of the network when source wavelets or noise differed from the training set. The denoising quality of the trained network was sensitive to the change of wavelet and noise type. The noisy data in the limitation test set were not substantially improved. The trained network was also tested on seismic field data collected at Hussar, Alberta by the CREWES Project. The data had excellent reflection events, but also substantial pumpjack noise on some shot gathers. We were able to significantly reduce the noise (favorably in comparison to traditional techniques) to allow considerably greater reflection continuity. U-net noise reduction techniques show considerable promise in seismic data processing.