Joint-Guided Denoising Network for Erratic Noise Attenuation
Tie Zhong, Ming Cheng, Sibo Wang, Shiqi Dong, Shaoping Lu, Xintong Dong · IEEE Transactions on Geoscience and Remote Sensing · 2024
In seismic exploration, erratic noise is a type of intense and complicated interference with large-amplitude and non-Gaussian distributions. The presence of erratic noise has been demonstrated to corrupt reflection events, adversely affecting the identification of effective signals. Nonetheless, conventional and time-frequency thresholding denoising methods based on the least-squares scheme usually assume that the seismic random noise has a Gaussian distribution, which is not the case for erratic noise. Therefore, the attenuation for erratic noise is challenging, owing to the deviation from the assumptions of the conventional methods. The recent application of convolutional neural networks (CNNs) to seismic data processing has yielded promising results. However, these CNN-based frameworks always have limited feature-interaction capability, resulting in the degeneration in denoising performance when coping with intense erratic noise. To address this issue, a novel joint-guided denoising network (JGD-Net) is proposed in this study. Unlike conventional CNN frameworks, JGD-Net uses a joint-guided scheme and attention mechanism to enhance the denoising capability. We generate synthetic records using published geological models such as Marmousi and salt dome to compose our training dataset. Furthermore, a novel loss function based on L1 norm and hyperbolic tangent function is designed to further ensure the optimization process of the training procedure and ease the influence of abnormal energy of erratic noise. Both synthetic and field data are processed sfor the evaluation of denoising performance. Compared with other popular methods, JDG-Net shows advantages in attenuating intense erratic noise, particularly under extremely low signal-to-noise ratio (SNR) conditions.