Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering

Abstract

Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals appear as instantaneous, high-energy vertical-line pulse trains (VLPTs) on the spectrogram. This paper uses computer vision techniques and unsupervised clustering algorithms to process and analyze VLPT on very-low-frequency (VLF) waveform spectrograms collected by the China Seismo-Electromagnetic Satellite (CSES) electric field detector. First, the waveform data are transformed into time–frequency spectrograms with a duration of 8 s using the short-time Fourier transform. Then, the spectrograms are subjected to grayscale transformation, vertical line feature extraction, and binarization preprocessing. In the third step, the preprocessed data are dimensionally reduced and fed into an unsupervised K-means++ clustering model to achieve automatic recognition and labeling of VLPTs. By recognizing and studying VLPT, not only can interference be recognized, but the temporal and spatial locations of these interferences can also be determined. This lays the foundation for identifying VLPT sources and gaining deeper insights into the generation, propagation, and characteristics of electromagnetic radiation.

Detection Method and Application of Nuclear-Shaped Anomaly Areas in Spatial Electric Field Power Spectrum Images

“…The research on image-based statistical analysis of spatial electric field perturbations in the ionosphere has been developed more slowly, and the research on its detection technology has mainly focused on target detection, intelligent speech technology, traditional vision technology, and unsupervised clustering. Among them, object detection and intelligent speech technology are applied to the intelligent detection of the L-dispersion shape of lightning whistler waves [7][8][9][10], while traditional visual technology and unsupervised clustering technology are applied to the detection of horizontal electromagnetic wave disturbances [11][12][13][14][15][16][17], all of which have good effects. These technical methods all belong to the research field of time-frequency map feature detection technology and are affected by the size of the time window selected when producing time-frequency map data, resulting in significant errors between the produced time-frequency map and the original data.…”