石油地球物理勘探2023，Vol.58Issue(6)：1322-1331,10.DOI:10.13810/j.cnki.issn.1000-7210.2023.06.003
三维地震数据频域无监督随机噪声压制方法
An unsupervised random noise suppression method in frequency domain for 3D seismic data
摘要
Abstract
Improving the signal-to-noise ratio is a key step in seismic data processing.The current deep learning based noise reduction methods have achieved better results.However,these methods are carried out in the temporal-spatial domain based on the local similarity of the seismic data and the processing efficiency is low.In view of the lateral continuity of geological structure,the shot gathers are very similar.Thus,an unsupervised rank-reduction denoise method in frequency domain is proposed based on the low-rank feature of the same frequency component of 3D data.The low-rank principle in frequency domain of 3D data is expounded and the singular value decomposition theory is used to guide the establishment of autoencoding network;Considering the characteristics of random noise distribution in frequency domain,K-L(Kullback-Leibler)divergence is used to constrain the loss function to improve the denoising effect.The experiments on synthetic and field data verified the advantages of the proposed method in denoising performance and computational efficiency compared with the multichannel singular spectrum analysis(MSSA)and K-SVD(K-Singular Value Decomposition)methods.关键词
无监督网络/频域去噪/奇异值分解/K-L散度/自编码网络Key words
unsupervised net work/denoising in frequency domain/singular value decomposition/K-L divergence/autoencoding network分类
天文与地球科学引用本文复制引用
薛亚茹,苏军利,冯璐瑜,张程,梁琪..三维地震数据频域无监督随机噪声压制方法[J].石油地球物理勘探,2023,58(6):1322-1331,10.基金项目
本项研究受中国石油科技创新基金项目"基于小波神经网络的多源地震数据分离方法研究"(2020D-5007-0301)资助. （2020D-5007-0301）
