Spatial Characteristics of Global Strong Constant-Frequency Electromagnetic Disturbances from Electric-Field VLF Data of the CSES

Abstract

Ionospheric disturbances are mainly caused by solar and Earth surface activity. The electromagnetic data collected by the CSES (China Seismo-Electromagnetic Satellite, popularly known as the Zhangheng-1 satellite) can capture many space disturbances. Different spatial disturbances can exhibit distinctive shapes on spectrograms. Constant-frequency electromagnetic disturbances (CFEDs) such as artificially transmitted VLF radio waves, power line harmonics, and satellite platform disturbances can appear as horizontal lines on spectrograms. Therefore, we used computer vision and machine learning techniques to extract the frequency of global CFEDs and analyze their strong spatial signal characteristics. First, we obtained time-frequency spectrograms from CSES VLF electric-field waveform data using Fourier transform. Next, we employed an unsupervised clustering algorithm to automatically recognize CFED horizontal lines on spectrograms, merging horizontal lines from different spectrograms, to obtain the CFED horizontal-line frequency range. In the third stage, we verified the presence of CFEDs in power spectrograms, thus extracting their true frequency values. Finally, for strong CFED signals, we generated eight revisited periods, resulting in 10,230 power spectrograms for analyzing each CFED’s spatial characteristics using a combined periodic sequence and spatial region that included frequency offsets, frequency fluctuations, and signal non-observation areas. These findings contribute to enhancing the quality of CSES observational data and provides a theoretical basis for constructing global CFED spatial background fields and earthquake monitoring and early prediction systems.