Identification and extraction of type II and III radio bursts based on YOLOv7

Abstract

Solar radio bursts (SRBs) are extreme space weather events characterized by intense solar radio emissions that are closely related to solar flares. They represent signatures of the same underlying processes that are responsible for well-documented solar phenomena such as sunspots, solar flares, and coronal mass ejections (CMEs). The study of SRBs holds significant importance as it provides a means to monitor and predict solar flares and CMEs, enhancing our ability to forecast potential impacts on Earth’s communications and satellites. Typically, SRBs below several hundred megahertz can be categorized into five types (I–V), with type II and type III bursts being the most prevalent. This study introduces a novel approach based on the YOLOv7 model for the detection and classification of type II and type III SRBs. The proposed method effectively identifies and classifies various SRB types, achieving a mean average precision accuracy of 73.5%. A trained neural network was deployed for SRB detection in the Chashan Broadband Solar radio spectrograph at meter wavelength (CBSm) data, enabling the extraction of valuable SRB information for subsequent research. This demonstrates that even when we are dealing with extensive datasets, this method can automatically recognize outbursts and extract pertinent physical information. Although our experiments with the CBSm dataset currently rely on the daily spectrum, further advancements in CBSm backend data processing techniques are expected to enable near-real-time burst detection, which is a powerful tool for accurately assessing and analyzing SRBs, and significantly contribute to the field of space weather forecasting and protective measures. Furthermore, the applicability of this method to other stations within the Chinese Meridian Project II (e.g., Mingantu Spectral Radioheliograph and Daocheng Solar Radio Telescope) enhances the capability of space weather data fusion and model development. Therefore, this research represents a substantial contribution to the domain of space weather research, offering a valuable tool for the detection and classification of SRBs and thereby improving our ability to predict and mitigate the impacts of extreme space weather events on Earth’s technology and infrastructure.