LAUSR

The supervised neural network-based method provides an effective way for seismic data denoising. The noise level of seismic erratic noise, i.e., outlier, varies from traces, time windows, and shot gathers. The networks with popular structures may damage reflections because the network cannot learn the exact location of the noises. In order to accommodate the characteristics of erratic noise, we propose using an attention-based network that focuses more on noisy regions. The network outputs an attention map, which shows the spatial distribution probability of erratic noise and a raw noise that may have leakage reflections. To train the network with two kinds of outputs, we use two different loss functions to optimize the network. The erratic noises in the specified area can be extracted by multiplying the attention map and the raw noise, and the reflections in areas without noise are preserved. For generating the training set, we proposed a shuffled noise strategy, which starts from the inaccurate denoised data with the conventional method. Only the noisy data are available in the whole denoising workflow. The network can learn the noise features effectively with the shuffle noise strategy and can achieve better denoising effect than that based on the conventional method. In addition, the denoising capability of the network can be controlled manually by filtering the attention map with probability, and the denoising effect can be further improved through recovering the leaked signals. Synthetic and field data examples show that the proposed method has potential for field erratic noise attenuation.