Research on identification of nucleus-shaped anomaly regions in space electric field

Abstract

The presence of nucleus-shaped anomalous regions in the power spectrum image of the electric field VLF frequency band has been discovered in previous studies. To detect and analyze these nucleus-shaped abnormal areas and improve the recognition rate of nucleus-shaped abnormal areas, this paper proposes a new nucleus-shaped abnormal area detection model ODM_Unet (Omni-dimensional Dynamic Mobile U-net) based on U-net network. Firstly, the power spectrum image data used for training is created and labeled to form a dataset of nucleus-shaped anomalous regions; Secondly, the ODConv (Omni-dimensional Dynamic Convolution) module with embedded attention mechanism was introduced to improve the encoder, extracting nucleus-shaped anomaly region information from four dimensions and focusing on the features of different input data; An SDI (Semantics and Detail Infusion) module is introduced between the encoder and decoder to solve the problem of detail semantic loss in high-level images caused by the reduction of downsampling image size; In the decoder stage, the SCSE (Spatial and Channel Squeeze-and-Excitation) attention module is introduced to more finely adjust the feature maps output through the SDI module. The experimental results show that compared with the current popular semantic segmentation algorithms, the ODM_Unet model has the best detection performance in nucleus-shaped anomaly areas. Using this model to detect data from November 2021 to October 2022, it was found that the frequency of nucleus-shaped anomaly areas is mostly between 0 and 12.5KHz, with geographic spatial distribution ranging from 40° to 70° south and north latitudes, and magnetic latitude spatial distribution ranging from 58° to 80° south and north latitudes. This method has reference significance for detecting other types of spatial electromagnetic field disturbances.