## 5 Discussion  
There can be multiple causes for VLPT generation, and here are some potential sources: Lightning is the result of charge separation and discharge in the atmosphere, which can generate intense pulsed electromagnetic radiation [10; 11; 12]. Solar activities, such as solar flares and solar bursts, can also cause pulsed electromagnetic wave radiation, and the energy released from these solar activities can trigger electromagnetic pulses in nearby space on Earth. The ionosphere, which is a layer of ionized gas in the Earth's atmosphere, plays an important role in the propagation and reflection of electromagnetic waves. Disturbances in the ionosphere, such as ionospheric irregularities, shear layers, and fluctuations, can cause pulsed electromagnetic wave radiation. Conforming propagation is a phenomenon in the propagation of electromagnetic waves, where pulsed signals may be formed due to interference from different paths after the reflection of electromagnetic waves in the atmosphere. These are just some of the possible reasons for VLPT generation, and the actual situation may be more complex and require specific analysis based on the data and scenario. There have been many methods proposed by previous researchers for the identification of pulse signals. One approach is based on the extraction of time-domain features, such as pulse width  
as spectral distribution and frequency components are extracted for signal classification and recognition [45; 46]. The third method utilizes morphological transformations to process the pulse signal and extract morphological features, such as skeleton, convex hull, and valleys, for signal classification and recognition [47; 48]. The fourth method involves using machine learning algorithms for pulse signal classification, such as support vector machines, artificial neural networks, and decision trees [49; 50]. Although these are some common methods for pulse signal extraction, and in practical applications, specific tasks and data characteristics may require the selection of appropriate methods. For example, reference [43] proposed a pulse signal analysis and recognition method based on multiscale morphological component analysis. It applies multiscale morphological transformations to the original signal, extracts specific features from the transformed results, and then utilizes a classifier for signal recognition. The success of this method is sensitive to parameter selection, as the choice of parameters can significantly impact the final recognition results. Reference [51] presented a feature extraction framework for underwater pulse signals based on morphology and wavelet packet transform, specifically for the field of ocean and underwater signal processing. This method has the capability of multiscale analysis and powerful spectral analysis, but its applicability may be limited to specific types of underwater pulse signals or specific application scenarios. Reference [52] used a Gaussian distribution-based electromagnetic pulse model and estimated parameters such as shape, amplitude, and duration of the pulse to describe the characteristics of ultra-wideband electromagnetic pulses. The methods for estimating these parameters are detailed in the article, and their effectiveness is validated through experimental results. However, this method is only applicable to Gaussian-distribution-based electromagnetic pulses and may not be suitable for other pulse distributions. This paper employs computer vision techniques and unsupervised clustering algorithms on the spectrogram obtained through the Fourier transform to realize automatic recognition of VLPT. The vertical line recognition algorithm proposed in the paper is applicable to the recognition of vertical lines on any type of spectrogram. And this method not only identifies VLPT but also discovers their spatiotemporal locations.