Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model

Xueshan Wu,Song Huang,Min Li,Yufeng Deng

doi:10.3390/app112311533

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.

Highlights

The attention deep-learning model we proposed enables only one magnetometer to detect magnetic anomaly under Gaussian color background noise
In addition to developing a powerful tool for magnetic anomaly detection, our study provides a novel perspective on the observation system
Different methods have different limitations; for example, orthogonal basis function (OBF) is not good at colored noise, MED is limited by signal-to-noise ratio (SNR), and DeepMAD relies on datasets

Summary

Introduction

Geomagnetic noise as background noise data does not provide good robustness of the model. The geomagnetic data that can be collected are recorded by different instruments for a limited period at specific locations. To improve the model’s generalization, we obtained the colored background noise by transforming the Gaussian white noise in the frequency domain. We set its power spectral density as 1/ f α (0 < α < 2), inverted it to the time domain.

Methods

Findings

Discussion

Conclusion