Random noises in seismic data will deteriorate data quality and have a negative impact on interpretation.In random noise reduction,it is difficult to restore effective information in seismic data using a conventional generative adversarial network.Based on the U-net network,we develop a modified generative adversarial network with optimized batch normalization and pooling layers to improve effective information restoration.A multi-scale discriminator network is established to improve the performance of the network model.A set of multi-module loss functions are formulated with feature matching loss and structural information loss.Ow-ing to the new network structure,it is unnecessary to estimate noises in advance,and thus end-to-end blind denoising could be a-chieved.The model also features improved ability of generalization and data restoration.Field data tests in the northern South China Sea show improved performance of noise reduction and signal preservation compared with other denoising algorithms,leading to better imaging of boundaries.The improved generative adversarial network is a good method for seismic data denoising and could be applied to seismic data processing in additional prospects.
generative adversarial networkU-net networkseismic data denoisinggeneralization abilitydata detail
国家科技期刊平台
NSTL
维普
万方数据
