摘要
地震勘探是油气和矿产资源开发领域使用最为广泛的物探方法之一.由于采集条件的限制,地震记录中通常混杂有大量的随机噪声,导致勘探资料普遍信噪比(Signal-to-Noise Ratio,SNR)较低,这严重影响有效信号辨识的精度,为后续反演、解释等工作带来巨大挑战.此外,地震勘探随机噪声通常具有非平稳、非高斯和与信号存在频带混叠等复杂特性,导致传统方法在处理复杂勘探记录时,消噪性能可能发生退化.针对复杂勘探随机噪声消减问题,本文提出了一种新型的双层多尺度特征融合去噪网络(Double-layer Multi-scale Feature Fusion Denoising Network,DMFF-Net).该网络具有多尺度网络结构,利用多分支模块提取勘探数据不同尺度和不同分支的潜在特征,提升网络对于勘探记录复杂特征的学习能力.同时,采用跳跃连接实现浅层和深层信息的融合,提升网络对微弱信号的恢复能力.模拟和实际资料处理结果表明,相较传统地震勘探资料消噪方法而言,DMFF-Net可以更加有效地压制随机噪声,完整恢复有效信号,显著提升地震资料信噪比,在信号保幅性和微弱信号恢复能力方面更具优势.
Seismic exploration is one of the most widely used geophysical prospecting methods in oil-gas and mineral resources development.Due to the limitations of acquisition conditions,the seismic records are usually contaminated with a large amount of random noise,resulting in a low Signal-to-Noise Ratio(SNR).It seriously affects the identification accuracy of the effective signals,thereby bringing challenges to subsequent inversion and interpretation procedures.In addition,the random noise usually has complex characteristics,such as non-stationary,non-Gaussian and spectral aliasing.The denoising performance for the conventional methods may degrade when confronted with such complex interferences.To achieve the complex noise attenuation,a novel double-layer multi-scale feature fusion denoising network(DMFF-Net)is proposed in this paper.In general,the proposed network has a multi-scale network structure.It utilizes the multi-branch modules to extract the potential features existing in different scales and branches so as to improve the learning ability of the network for complex features of the analyzed seismic data.Meanwhile,we also employ skip connections to fuse the shallow and deep features;then,improve the recover ability of the weak signals.The synthetic and field data processing results indicate that DMFF-Net can suppress the random noise effectively and restore the desired signals accurately.Moreover,it also can significantly improve the SNR.Compared with conventional denoising methods,DMFF-Net has advantages in signal amplitude retention and weak signal recovery.
作者
钟铁
陈云
卢绍平
董新桐
李月
杨宝俊
ZHONG Tie;CHEN Yun;LU ShaoPing;DONG XinTong;LI Yue;YANG BaoJun(Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology,Ministry of Education,Jilin 132012,China;Department of Communication Engineering,Northeast Electric Power University,Jilin 132012,China;School of Earth Sciences and Engineering,SUN YAT-SEN University,Guangzhou 510275,China;Southern Marine Science and Engineering Guangdong Laboratory,Zhuhai Guangdong 519000,China;College of Instrumentation and Electrical Engineering,Jilin University,Changchun 130026,China;College of Communication Engineering,Jilin University,Changchun 130012,China;College of Geo-exploration Science and Technology,Jilin University,Changchun 130026,China)
出处
《地球物理学报》
SCIE
EI
CAS
CSCD
北大核心
2022年第11期4418-4432,共15页
Chinese Journal of Geophysics
基金
国家自然科学基金重点项目(41730422)
国家自然科学基金(42074123)
博士后创新人才支持计划(BX2021111)
吉林省教育厅科学技术研究规划项目(JJKH20210094KJ)共同资助.
关键词
地震勘探
低信噪比
卷积神经网络
多尺度特征融合
噪声消减
随机噪声
Seismic exploration
Low Signal-to-Noise Ratio(SNR)
Convolutional neural network
Multiscale feature fusion
Noise attenuation
Random noise
