基于卷积对抗降噪网络的塔里木盆地沙漠地震资料消噪方法研究  

The denoising of desert seismic data acquired from tarim basin based on convolutional adversarial denoising network

作　　者：董新桐 钟铁[3] 王洪洲 吴宁[4] 李月[4] 杨宝俊[5] DONG XinTong;ZHONG Tie;WANG HongZhou;WU Ning;LI Yue;YANG BaoJun(College of Instrumentation and Electrical Engineering,Jilin University,Changchun 130026,China;Southern Marine Science and Engineering Guangdong Laboratory(Zhanjiang),Zhanjiang 524000,China;Department of Communication Engineering,Northeast Electric Power University,Jilin 132012,China;College of Communication Engineering,Jilin University,Changchun 130012,China;College of Geo-exploration Science and Technology,Jilin University,Changchun 130026,China)

机构地区：[1]吉林大学仪器科学与电气工程学院,长春130026 [2]南方海洋科学与工程广东省实验室(湛江),湛江524000 [3]东北电力大学通信工程系,吉林132012 [4]吉林大学通信工程学院,长春130012 [5]吉林大学地球探测与信息技术学院,长春130026

出　　处：《地球物理学报》2022年第7期2661-2672,共12页Chinese Journal of Geophysics

基　　金：国家自然科学基金重点项目(41730422);2022年广东省促进经济高质量发展专项(海洋经济发展)重点项目“海洋可控震源系统关键技术与装备研发”(GDNRC[2022]29);博士后创新人才支持计划(BX2021111)资助。

摘　　要：塔里木盆地作为我国重要的油气勘探地区,其地表主要由沙漠覆盖.塔里木地区获取的沙漠地震资料通常表现为低信噪比,并且有效信号与背景噪声在低频段存在严重的混叠现象.这两点给沙漠地震资料的消噪带来了巨大的困难,进而影响后期的反演、成像以及解释等工作.为了压制沙漠背景噪声并完整恢复有效信号,本文采用生成对抗网络(Generative Adversarial Network,GAN)的基本思路并利用去噪器代替GAN中的生成器,提出一种针对沙漠地震资料的全新消噪网络,命名为沙漠地震卷积对抗降噪网络(Desert Seismic Convolutional Adversarial Denoising Network,DSCA-Net).在DSCA-Net中,我们将去噪器的均方误差损失与去噪器、鉴别器之间的对抗损失相结合,提出了一种全新损失函数;利用该损失函数来优化网络,进而得到适合沙漠地震资料去噪的模型.模拟与实际实验均表明本文提出的DSCA-Net可以有效地压制沙漠地震资料中的背景噪声,同时显著增强同相轴的连续性;经过DSCA-Net处理后的沙漠地震资料信噪比得到显著提升.Tarim basin mainly composed of desert regions is an important oil and gas exploration area.The desert seismic data acquired from Tarim basin is often characterized by low Signal-to-Noise Ratio(SNR);also,the effective signals and noise seriously overlaps in low-frequency domain.These two points bring numerous difficulties to the denoising of desert seismic data,so as to affect the following inversion,imaging,and interpretation.In order to suppress the background noise effectively and recover the effective signals completely,we adopt the basic strategy of Generative Adversarial Network(GAN)and then utilize a denoiser to replace the generator of GAN,so as to propose a novel denoising network for the desert seismic data,named Desert Seismic Convolutional Adversarial Denoising Network(DSCA-Net).In DSCA-Net,we propose a novel loss function by combining the mean square error loss and adversarial loss.Then,this loss function is used to optimize the network parameters of DSCA-Net,so as to obtain the denoising model aiming at the desert seismic data.Synthetic and real experiments show that(1)the proposed DSCA-Net can effectively suppress the desert background noise and significantly enhance the continuity of events;(2)after processed by DSCA-Net,the signal-to-noise ratio of desert seismic data is obviously improved.

关 键 词：低信噪比 频谱混叠 塔里木盆地 噪声压制 地震资料 

分 类 号：P631[天文地球—地质矿产勘探]