Local Geomagnetic Component Modeling of Auroral Images Based on Local‑Global Feature

Accurately predicting geomagnetic field is of great significance for space environment monitoring and space weather forecasting worldwide. This paper proposes a vision Transformer （ViT） hybrid model that leverages aurora images to predict local geomagnetic station component， breaking the spatial limitations of geomagnetic stations. Our method utilizes the ViT backbone model in combination with convolutional networks to capture both the large-scale spatial correlation and distinct local feature correlation between aurora images and geomagnetic station data. Essentially， the model comprises a visual geometry group （VGG） image feature extraction network， a ViT-based encoder network， and a regression prediction network. Our experimental findings indicate that global features of aurora images play a more substantial role in predicting geomagnetic data than local features. Specifically， the hybrid model achieves a 39.1% reduction in root mean square error compared to the VGG model， a 29.5% reduction compared to the ViT model and a 35.3% reduction relative to the residual network （ResNet） model. Moreover， the fitting accuracy of the model surpasses that of the VGG， ViT， and ResNet models by 2.14% 1.58%， and 4.1%， respectively.