Lightning-generated Whistlers recognition for accurate disaster monitoring in China and its surrounding areas based on a homologous dual-feature information enhancement framework

Abstract

Natural disasters, such as earthquakes and volcanic eruptions, pose a significant threat to Earth’s biodiversity and ecological environment. The ability of Lightning-generated Whistlers (LWs) to foresee these events is invaluable. However, the accurate recognition of LWs is hindered due to the spatial environmental interference and a lack of comprehensive information. This study proposes a novel framework called Dual-features Information Enhancement Framework (DIEF) with three versions: the cost-effective DIEF-B, the highly accurate DIEF-M, and the lightweight yet efficient DIEF-T. This framework aims to integrate homologous dual-feature and mitigate space environment effects for LWs recognition. Specifically, the Dual-feature Information Enhancement (DIE) module, which is based on Transformers, merges the waveform signal with the time-frequency spectrum of LWs to enhance the information representation within the feature space. In addition, Multi-scale Feature Integration (MFI) is designed to address the challenge of recognizing faint LWs in waveform signal. To correct errors in time-frequency spectrum recognition caused by space environmental interference, we adopt Mel-scale Frequency Cepstrum Coefficients (MFCCs) to enhance waveform signal features. Afterwards, the long-distance dependences between signals are improved through the Bi-directional Long Short-Term Memory (BiLSTM) network. Finally, an efficient Lightning-generated Whistlers Classifier (LWC) is developed. Numerous tests demonstrate the excellent performance and robustness of the DIEF series, which achieve 99.30% recognition accuracy on the 10,200 segments of LWs dataset acquired by Zhangheng-1 (ZH-1) satellite. The DIEF series achieves accuracy of 95.27% in audio recognition on the UrbanSound8k dataset, which is better than most current ones. Our framework can quickly and accurately recognize valuable LWs events in an interference environment, thereby benefiting for global natural disaster monitoring. Source Code is available at https://github.com/KotlinWang/DIEF.