High-Precision Magnetic Field Reconstruction and Anomaly Classification

This study aims to maximize the utilization of vector information from small-scale magnetic targets and proposes a high-precision magnetic field reconstruction model based on the magnetic dipole model and the particle swarm optimization (PSO) algorithm. In addition, it leverages the unique capabilities of the Vision Transformer (ViT) model to effectively handle the characteristics of the reconstructed magnetic anomaly sequence data for classification. To begin with, we establish a magnetic field reconstruction model using the PSO and the magnetic dipole model, introduce the chaotic random inertia weight strategy and calculate the secondary magnetic moment to optimize the model, and suggest the CPSO_MD model. The model can reproduce the recorded magnetic field with excellent precision, and at various observation distances, its reconstruction error is decreased by 3.41% and 2.70%, respectively. In addition, build domain classifiers to address domain offset and short sample dataset issues, and fine-tune the ViT model in accordance with the features of successive magnetic field samples to address the overfitting and oscillation of magnetic anomaly classification issues. Finally, the proposed model is evaluated using a range of metrics. The accuracy of the model classification verification is 98.66%, while the accuracy of the area classification verification is 98.21%. Also, the model does not oscillate or overfit during the subsequent rounds of verification, which definitely demonstrates the efficacy of the domain classifier and fine-tuning in the model.