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  4. 中国科学院油气资源研究重点实验室
Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model

文献类型：期刊论文

作者Wu, Xueshan1,2,3; Huang, Song2,3; Li, Min1,2,3; Deng, Yufeng1,2,3
刊名APPLIED SCIENCES-BASEL
出版日期2021-12-01
卷号11期号:23页码:12
关键词deep learning Gaussian colored noise magnetic anomaly detection (MAD) three-axis magnetometer
DOI10.3390/app112311533
英文摘要Magnetic anomaly detection (MAD) is used for detecting moving ferromagnetic targets. In this study, we present an end-to-end deep-learning model for magnetic anomaly detection on data recorded by a single static three-axis magnetometer. We incorporate an attention mechanism into our network to improve the detection capability of long time-series signals. Our model has good performance under the Gaussian colored noise with the power spectral density of 1/f a which is similar to the field magnetic noise. Our method does not require another magnetometer to eliminate the effects of the Earth's magnetic field or external interferences. We evaluate the network's performance through computer simulations and real-world experiments. The high detection performance and the single magnetometer implementation show great potential for real-time detection and edge computing.
资助项目National Natural Science Foundation of China[91858214] ; National Key R&D Program of China[2018YFC0604004]
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
语种英语
WOS记录号WOS:000734596000001
出版者MDPI
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China
源URL[http://ir.iggcas.ac.cn/handle/132A11/103950]  
专题地质与地球物理研究所_中国科学院油气资源研究重点实验室
通讯作者Huang, Song
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
3.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing 100029, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Xueshan,Huang, Song,Li, Min,et al. Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model[J]. APPLIED SCIENCES-BASEL,2021,11(23):12.
APA Wu, Xueshan,Huang, Song,Li, Min,&Deng, Yufeng.(2021).Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model.APPLIED SCIENCES-BASEL,11(23),12.
MLA Wu, Xueshan,et al."Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model".APPLIED SCIENCES-BASEL 11.23(2021):12.

入库方式： OAI收割

来源：地质与地球物理研究所

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