Automatic segmentation model and parameter extraction algorithm for lightning whistlers

Abstract

Based on the magnetic field data recorded by the ZH-1 electromagnetic satellite, we cerat a training set of 1,300 spectrograms containing the dispersion spectrum of lightning whistlers (LW). The Segment Anything Model (SAM) in the field of image segmentation is trained through the training set to obtain a fine-tuned SAM model that can be used to detect and segment the dispersion spectrum of LW at pixel level. All track regions of LW are effectively separated from other non-lightning whistlers regions in the spectrograms after being segmented by the model. The segmentation effect is excellent and detection accuracy is 96.89%, which is better than the previous segmentation model for LW based on ground station data. Then we apply the traditional image processing methods to extract the dispersion spectrum of LW one by one, and develop an algorithm to automatically extract the physical parameters of each LW. The root mean square error between the automatically extracted dispersion parameter and the manually extracted dispersion parameter is only 0.1654 s ^1/2 . The model and algorithm studied in this paper are employed to analyze the dispersion of LW received by the ZH-1 satellite over China. It is found that the whistlers dispersion received by satellites during summer in the northern hemisphere and summer in the southern hemisphere shows opposite trends with receiving latitude. Both trends can be explained by the relationship between the dispersion and the length of propagation paths of LW.