Research on the Response Characteristics and Identification of Infrasound Signals in the Jialongcuo Ice Avalanche, Tibet

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-09, Vol.15 (18), p.4482
• Main Authors: Zhang, Yifang, Chen, Qiao, Su, Pengcheng, Liu, Dunlong, Cui, Jianzhao, Chen, Jilong, Ma, Jianrong, Xing, Qiao, Xu, Fenglin, Fan, Yuanchao, Wei, Fangqiang
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Avalanches; Back propagation networks; Background noise; Classification; classified recognition; Climate change; Earthquakes; Entropy (Information theory); feature extraction; Frequency distribution; Glaciers; Humidity; Ice; ice avalanche; ice lake collapse; Infrasound; infrasound signal; Kurtosis; Landslides; Monitoring; Neural networks; Precipitation; Real time; Remote sensing; Research methodology; Sensors; Signal classification; Support vector machines; Waveforms; Weather
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15184482

Due to the inability of remote sensing satellites to monitor avalanches in real time, this study focuses on the glaciers in the rear edge of Jialongcuo, Tibet, and uses infrasound sensors to conduct real-time monitoring of ice avalanches. The following conclusions are drawn: (1) In terms of waveform, compared to background noise, ice avalanche events have a slight left deviation and a slightly steep shape; compared to wind, rain, and floods events, ice avalanche events have less obvious kurtosis and skewness. (2) In terms of frequency distribution, the infrasound frequency generated by ice avalanche events is mainly distributed in the range of 1.5 Hz to 9.5 Hz; compared to other events, ice avalanche events differ some in frequency characteristics. (3) The model based on information entropy and marginal spectral frequency distribution characteristics of infrasound have higher accuracy in signal classification and recognition, as they can better represent the differences between infrasound signals of different events than other features. (4) Compared with the K-nearest neighbor algorithm and classification tree algorithm, the support vector machine and BP (Back Propagation) neural network algorithm are more suitable for identifying infrasound signals in the Jialongcuo ice avalanche. The research results can provide theoretical support for the application of infrasound-based ice avalanche monitoring technology.