测试技术学报2024，Vol.38Issue(2)：210-220,11.DOI:10.3969/j.issn.1671-7449.2024.02.015
基于多阶段渐进式UNet压制地震勘探随机噪声
Suppressing Random Noise in Seismic Exploration Based on Multi-Stage Progressive UNet
摘要
Abstract
Data processing is a key link in seismic exploration,and the UNet network,as one of the typi-cal neural network architectures,has also been used in the field of seismic exploration in recent years as a means of suppressing random noise.The UNet network is based on its symmetric encoding and decoding structure,which can extract a wide range of contextual information.However,due to the excessive use of down-sampling operations in its encoding part,it is easy to lose the spatial details of the input image.Sec-ondly,the UNet architecture is a single-stage model with a simple network structure,making it difficult to achieve a balance between spatial accuracy and multi-scale information.Based on this,a multi-stage progressive UNet network(MPUNet)is proposed.The first two-stages of the network use an encoder-decoder to learn rich multi-scale information,and the last stage preserves accurate spatial details through the original resolution sub network.Introducing supervised attention modules between each two-stages to recalibrate features entering the next stage,and introducing cross stage feature fusion mechanisms to make the entire network framework more tightly connected and avoid the loss of effective information.The experimental results of artificial synthetic records and actual seismic data show that compared to time-frequency peak filtering(TFPF)and residual dense networks(RDNet),traditional UNet and UNet with residual dense blocks(RDBUNet),MPUNet has a more significant denoising effect,which can effec-tively improve the signal-to-noise ratio and resolution of seismic data,providing a favorable basis for sub-sequent analysis and interpretation of seismic data.关键词
UNet/噪声压制/MPUNet/神经网络/地震勘探Key words
UNet/noise suppression/MPUNet/neural networks/seismic exploration分类
信息技术与安全科学引用本文复制引用
贺守峰,李光辉,宁旭亮..基于多阶段渐进式UNet压制地震勘探随机噪声[J].测试技术学报,2024,38(2):210-220,11.基金项目
山西省自然科学基金面上资助项目(202103021224012) （202103021224012）
