A deep neural network based method for magnetic anomaly detection

Abstract

Magnetic anomaly detection (MAD) is a technique to find ferromagnets hiding in strong and complicated magnetic background. In many practical cases, the targets are very far from the detection sensor, which leads to low signal-to-noise ratio (SNR) and high detection difficulty. Most of the current methods determine the existence of target by some approaches based on signal analysis, such as the orthogonal basis function (OBF) and the minimum entropy (ME). However, although these methods consume low resources, the detection performances are not satisfactory enough. In recent years, due to the increase of computer capability, complex methods become applicable in MAD. In this study, a deep neural network (DNN) is adopted to detect the magnetic anomalies. The DNN has shown its better ability to represent natural data in many applications. A feature automatically learned by a DNN from data in the raw form is more effective for detecting target signals and suppressing irrelevant variations. Herein, a convolutional network with residual structure to implement the feature extraction is designed and an MAD method based on it is proposed. Through the semi-real tests, the proposed method exhibits a strong capability to extract features and shows excellent performances on detection.