Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model
Source abstract
Abstract
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/fα, 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Anomaly Detection Techniques and Applications · Geophysical and Geoelectrical Methods · Time Series Analysis and Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China
University of Chinese Academy of Sciences, Beijing 100049, China
Funding
National Natural Science Foundation of China · the National Key R&D Program of China
References (22)
Show source-provided references
1. Wiegert, R. (2003, January 22–26). Magnetic Anomaly Guidance System for Mine Countermeasures Using Autonomous Underwater Vehicles. Proceedings of the Oceans 2003. Celebrating the Past Teaming Toward the Future (IEEE Cat. No.03CH37492), San Diego, CA, USA.10.1109/OCEANS.2003.178206 ↗
2. A New Method for Magnetic Position and Orientation Tracking10.1109/20.951014 ↗
3. Magnetic Tracking System: Monitoring Heart Valve Prostheses10.1109/TMAG.2007.894002 ↗
4. Processing of a Scalar Magnetometer Signal Contaminated by 1/fα noise10.1016/j.sna.2007.04.018 ↗
5. Ginzburg, B., Sheinker, A., Salomonski, N., SOREQ, N., Frumkis, I.L., and Kaplan, B.Z. (2009, January 7–9). Application of Target-Based and Noise-Based Methods in Magnetic Anomaly Detection Systems. Proceedings of the International Marine Electromagnetics Conference (MARELEC 2009), Stockholm, Sweden.
6. Processing of Magnetic Scalar Gradiometer Signals Using Orthonormalized Functions10.1016/S0924-4247(02)00351-5 ↗
7. A Brief Review of Magnetic Anomaly Detection
8. Magnetic Anomaly Detection Using Entropy Filter10.1088/0957-0233/19/4/045205 ↗
9. Magnetic Anomaly Detection Using High-Order Crossing Method10.1109/TGRS.2011.2164086 ↗
10. Decreasing Noise in Magnetic Anomaly Detection Basing on Wavelet Denoising10.4028/www.scientific.net/AMM.368-370.1860 ↗
11. Adaptive Magnetic Anomaly Detection Method Using Support Vector Machine
12. Magnetic Dipole Target Signal Detection via Convolutional Neural Network
13. DeepMAD: Deep Learning for Magnetic Anomaly Detection and Denoising10.1109/ACCESS.2020.3006795 ↗
14. Williams, R.J. (1989). Complexity of Exact Gradient Computation Algorithms for Recurrent Neural Networks, Northeastern University, College of Computer Science. Technical Report NU-CCS-89-27.
15. Huang, Z., Xu, W., and Yu, K. (2015). Bidirectional LSTM-CRF Models for Sequence Tagging. arXiv.
16. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv.
17. Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015, January 5–9). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Munich, Germany.10.1007/978-3-319-24571-3 ↗
18. Web Traffic Anomaly Detection Using C-LSTM Neural Networks10.1016/j.eswa.2018.04.004 ↗
19. Input Quality Aware Convolutional LSTM Networks for Virtual Marine Sensors10.1016/j.neucom.2017.11.027 ↗
20. Recurrent Convolutional Neural Network for Answer Selection in Community Question Answering10.1016/j.neucom.2016.07.082 ↗
21. He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
22. Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv.