IGGCAS OpenIR  > 油气资源研究院重点实验室
Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model
Wu, Xueshan1,2,3; Huang, Song1,2; Li, Min1,2,3; Deng, Yufeng1,2,3
2021-12-01
Source PublicationAPPLIED SCIENCES-BASEL
Volume11Issue:23Pages:12
AbstractMagnetic 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.
Keyworddeep learning Gaussian colored noise magnetic anomaly detection (MAD) three-axis magnetometer
DOI10.3390/app112311533
Funding OrganizationNational 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
Language英语
Funding ProjectNational Natural Science Foundation of China[91858214] ; National Key R&D Program of China[2018YFC0604004]
Funding OrganizationNational 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
WOS Research AreaChemistry ; Engineering ; Materials Science ; Physics
WOS SubjectChemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000734596000001
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/103950
Collection油气资源研究院重点实验室
Corresponding AuthorHuang, Song
Affiliation1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing 100029, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author AffilicationKey Laboratory of Petroleum Resource Research, Chinese Academy of Sciences
Corresponding Author AffilicationKey Laboratory of Petroleum Resource Research, Chinese Academy of Sciences
Recommended Citation
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.
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