The deep-seated information is reflected in the late-stage data of the second field. By introducing the deep learning algorithm integrated with the characteristics of the secondary field data, we can map the contaminated data in late track data to a high-probability position. By comparing several filtering algorithms, the SFSDSA method has better performance and the denoising signal is conducive to further improving the effective detection depth.

This research intends to characterize the South Atlantic Anomaly (SAA) by applying the power spectrum analysis approach. The outcomes of the research revealed that the SAA region had a tendency to be persistent during active periods and normal periods. It can be said that it experiences this characteristic because of the Earth’s magnetic field strength. It is very important for spacecraft entering the SAA to take safety precautions in order to minimize the damage.