摘要
分布式声传感(DAS:Distributed fiber Acoustic Sensing)是一种新型的传感技术,但DAS数据中信号被复杂噪声覆盖,信噪比非常低,衰落噪声严重影响到后续信号的反演和解释。为此,提出基于注意力引导的深度网络(ADNet:Attention-guided Denoising convolutionalneural Network)实现DAS数据智能消噪。与传统的用于处理DAS信号方法相比,选择在网络中引入注意力引导模块,生成注意力特征图,使深层网络着重处理特征性强的部分,以此提高网络模型在去噪方面的性能。通过测试并与传统方法实验对比,证明了ADNet在噪声消减和效率提高方面具有较大优势。
DAS(Distributed fiber Acoustic Sensing)is a new sensing technology.However,the effective signals in DAS data are covered by a variety of complex and strong noise with a very low signal-to-noise ratio,which seriously affects the subsequent signal inversion and interpretation.Therefore,an attention-guided deep network(ADNet:Attention-guided Denoising convolutional neural Network)is proposed for DAS exploration data intelligent denoising.Compared to traditional methods,an attention-guiding module is introduced into the network to generate an attention-characteristic map,so that the deep network focuses on the parts with strong characteristics to improve the performance of network model in denoising.Through testing and comparing with traditional methods,it is proved that ADNet has great advantages in noise reduction and efficiency improvement.
作者
田雅男
孙浩然
宋明绅
刘涛
刘瀚林
赵晓龙
TIAN Yanan;SUN Haoran;SONG Mingshen;LIU Tao;LIU Hanlin;ZHAO Xiaolong(College of Communication Engineering,Jilin University,Changchun 130012,China)
出处
《吉林大学学报（信息科学版）》
CAS
2022年第4期525-530,共6页
Journal of Jilin University（Information Science Edition）
基金
国家自然科学基金资助项目(41730422)。
关键词
注意力机制
分布式声传感
井中剖面
信噪比
噪声消减
attentional mechanism
distributed fiber acoustic sensing(DAS)
borehole profile
signal to noise ratio
noise suppression
