## 1 Introduction  
Since the mid-20 th century, with the development of space technology, satellites have been used as observation platforms to acquire electromagnetic wave data from Earth and the universe. They have been widely applied in fields such as meteorology, geophysics, environmental science, and astronomy [1; 2; 3; 4; 5]. Satellite observations provide global-scale electromagnetic wave data, including a large number of electromagnetic disturbances such as lightning, solar magnetic storms, and ionospheric disturbances [6; 7; 8; 9]. By identifying and studying these disturbances, we can not only monitor and predict space weather phenomena and predict earthquakes but also provide information on ionospheric and atmospheric activities for sectors such as aviation, aerospace, communications, and navigation. In addition, it helps to gain a deeper understanding of the generation, propagation, and characteristics of electromagnetic radiation. Electromagnetic pulse interference signals refer to electromagnetic waveforms with short time duration and high peak energy. Spatial electromagnetic pulse interference signals can be caused by various factors, such as lightning, arc discharge, solar disturbances, and spatial electromagnetic wave disturbances [10; 11; 12]. A spectrogram is a tool used to describe the temporal and spectral variations of a signal. By applying time-frequency analysis techniques (such as the short-time Fourier transform), we can visualize the temporal and frequency domain information of the signal [13; 14; 15; 16]. Pulse disturbance signals appear as instantaneous, high-energy vertical lines on the spectrogram [17]. Pulse signals typically have a wide frequency spectrum, so vertical lines on the spectrogram may cover multiple frequencies. These vertical-line pulse trains (VLPTs) indicate the pulse occurrence time and frequency distribution characteristics. At present, the identification of different types of spatial electromagnetic waves on spectrograms mainly focuses on two aspects: constant frequency electromagnetic wave identification and L-shape whistler wave identification. However, there is currently limited research and application for the identification of other types of electromagnetic waves. Constant frequency electromagnetic wave identification refers to the classification and identification of electromagnetic waves within specific frequency ranges, which appear as horizontal lines on the spectrogram, such as wireless communication frequency bands. By using computer techniques to identify horizontal lines on the spectrogram, different types of constant-frequency electromagnetic wave sources can be distinguished, enabling identification and classification [18; 19; 20]. L-shaped whistler waves, on the other hand, refer to a special-shaped electromagnetic wave signal with unique spectral characteristics and temporal patterns. L-shape whistler waves can be manually labeled with an L-shape tag and be automatically identified using deep learning methods [21]. In addition, article [22] introduces the method of using convolutional neural networks (ConvNet) for automated ULF (ultra-low frequency) wave classification in swarm time series. However, for other types of electromagnetic waves, such as random frequencies or irregular signal spectra, there are currently no definite methods or research findings for their identification. During its five years of operation, the CSES satellite has accumulated a significant amount of data resources, which contain rich and complex information on electromagnetic wave disturbances in space. In order to delve into the inherent value of these data, there is an urgent need to employ advanced batch processing techniques of big data to extract hidden electromagnetic disturbance information from it. This paper uses computer vision techniques and unsupervised clustering algorithms to automatically recognize VLPTs on spectrogram of VLF waveform data collected by the CSES electric field detector.