A Self-Supervised Method of Suppressing Interference Affected by the Varied Ambient Magnetic Field in Magnetic Anomaly Detection

Airborne magnetic anomaly detection is an important passive remote sensing technique. However, since the magnetic field caused by the aircraft interferes with the detection accuracy, this part of interference should be eliminated by an aeromagnetic compensation method. Most existing compensation methods assume that the ambient magnetic field is uniform when calculating the compensation model parameters. However, as the ambient magnetic field is actually not uniform and varies with the aircraft location, the solved parameters ignore the part of aircraft interference related to the varied ambient magnetic field. Although some of the latest deep learning-based aeromagnetic compensation methods avoid the assumption of uniformity of the ambient magnetic field, the insufficient supervision leads to a poor model generalization. To address these limitations, we propose a self-supervised compensation method. The proposed method utilizes a network to separate the total measured magnetic field into the ambient magnetic field part and the aircraft magnetic field part. By doing so, the method avoids the influence of the uniform ambient magnetic field assumption and enhances the model generalization. In addition, we introduce an improvement ratio loss function to distinguish the aircraft magnetic field from the ambient magnetic field when updating the model parameters. The proposed method is verified using measurement data from real flights. The experimental results indicate that the proposed method significantly outperforms state-of-the-art methods in real flights compensation.