A Deep-Learning-Based Denoising Method for Multiarea Surface Seismic Data

At present, almost no denoising method can effectively suppress the seismic random noise in different areas. This phenomenon is partially because of two reasons: 1) the variable dominant frequency (DF) distribution of random noise in different areas and 2) the different signal-to-noise ratios (SNRs) of the seismic data acquired from different areas. We have developed a deep-learning denoising method to suppress the random noise in different areas based on convolutional neural network (CNN). For a certain area, we leverage the wave equation and power spectrum analysis to construct a noise set whose DF distribution is close to that of the real random noise in this area, and then a CNN denoising model for this area can be obtained via the training of this noise set. In addition, an energy ratio factor is used to adjust the energy ratio of effective signal patch and noise patch in the training process, so as to improve the generalization ability of CNN denoising model to different SNRs. Experiments demonstrate that our method can effectively suppress the random noise in different areas and completely recover the effective events.st no denoising method can effectively suppress the seismic random noise in different areas. This phenomenon is partially because of two reasons: 1) the variable dominant frequency (DF) distribution of random noise in different areas and 2) the different signal-to-noise ratios (SNRs) of the seismic data acquired from different areas. We have developed a deep-learning denoising method to suppress the random noise in different areas based on convolutional neural network (CNN). For a certain area, we leverage the wave equation and power spectrum analysis to construct a noise set whose DF distribution is close to that of the real random noise in this area, and then a CNN denoising model for this area can be obtained via the training of this noise set. In addition, an energy ratio factor is used to adjust the energy ratio of effective signal patch and noise patch in the training process, so as to improve the generalization ability of CNN denoising model to different SNRs. Experiments demonstrate that our method can effectively suppress the random noise in different areas and completely recover the effective events.