基于子午工程气辉成像观测的中高层大气波动智能识别及关键参数提取OA

To address the demand for efficient processing of massive airglow images in the Meridian Project,this study developed a machine-learning-based method for automatic identification and parame-ter extraction of Atmospheric Gravity Waves(AGWs)and Medium-Scale Traveling Ionospheric Distur-bances(MSTIDs).A Convolutional Neural Network(CNN)classification model was employed to filter clear-night-sky images,achieving accuracies of 99％(OH airglow)and 96.9％(OI airglow).Wave struc-tures were localized using a Fast Region-Based CNN with an Intersection-over-Union(IoU)value excee-ding 75％.For AGWs,parameters including wavelength,propagation direction,and horizontal phase velocity were extracted via 2D Fourier transform,while Canny edge detection and linear fitting were applied to MSTIDs.Analysis of the extracted parameter dataset revealed long-term trends of atmospher-ic waves:At the Dandong station(40.0°N,124.0°E),OH airglow observations showed a bimodal season-al distribution of AGW occurrence,with peaks during both winter and summer,with propagation direc-tions being predominantly southwestward in winter and northeastward in summer.At the Xinglong sta-tion(40.2°N,117.4°E),94％of MSTID events detected via OI airglow exhibited southwestward propaga-tion(azimuths of 200°～230°).These statistical characteristics align with established patterns in the literature,validating the reliability of the dataset.This tool resolves the inefficiency and subjectivity of traditional manual analysis,providing robust data support for long-term atmospheric wave studies.The associated algorithms and datasets will be open-sourced.