空间科学学报2025，Vol.45Issue(1)：66-81,16.DOI:10.11728/cjss2025.01.2024-0018
基于一维残差卷积神经网络的Pi2脉动识别模型
Identification Model of Pi2 Pulsation Based on One-dimensional Residual Convolutional Neural Network
摘要
Abstract
Pi2 pulsations are irregular ultra-low frequency waves,representing a significant transient response to the coupling between the magnetosphere and ionosphere.Their occurrence is associated with onset of substorms.As a disturbance phenomenon in the Earth's magnetosphere,the occurrence signal of Pi2 pulsations is hidden within the observation data of geomagnetic field components.Addressing the in-creasing amount of observation data,how to efficiently determine whether Pi2 pulsation has occurred in a segment of geomagnetic field component observational data is the key to build a Pi2 pulsation identifi-cation model.Based on the time series observation data of the FGM from the Chinese Meridian Project and One-Dimensional Residual Convolutional Neural Network(1D-ResCNN),this paper establishes an end-to-end Pi2 pulsation identification model.This model can distinguish whether Pi2 pulsation occurs in the observation data of a certain geomagnetic field component.Experimental results show that this model has higher recognition accuracy and lower false positive rate and false negative rate than the existing Pi2 pulsation machine learning identification model.关键词
Pi2脉动/Pi2脉动识别模型/一维残差卷积神经网络Key words
Pi2 pulsations/Pi2 pulsation identification model/One-dimensional residual convolutional neural network分类
天文与地球科学引用本文复制引用
张怡悦,邹自明,方少峰..基于一维残差卷积神经网络的Pi2脉动识别模型[J].空间科学学报,2025,45(1):66-81,16.基金项目
国家重点研发计划项目(2022YFF0711400)和中国科学院网信专项(CAS-WX2022SF-0103)共同资助 （2022YFF0711400）
