中南民族大学学报（自然科学版）2025，Vol.44Issue(4)：497-506,10.DOI:10.20056/j.cnki.ZNMDZK.20250409
基于深度学习的频高图自动标定
Ionogram automatic scaling based on Deep-learning
摘要
Abstract
Ionogram is the conventional data of ionospheric sounding by ionosonde on the ground.The amount of data is large.Various parameters of the ionosphere need to be scaled one by one to obtain.Traditionally,manual scaling is required but it is time-consuming,laborious and error-prone.It is imperative to realize computer-assisted manual scaling potential.Herein a deep-learning method for ionogram automatic scaling(DIAS)is presented,and the method is based on the U-shaped structure and using the characteristic pyramid with horizontal connection as the connection,using the data of manual scaling to generate the model sample data,and then randomly select part of the data as the training data input,so that the predicted value of the model gradually approaches the true value by constantly updating the parameters.The results show that compared with Automatic Real-Time Ionogram Scaling with True-height(ARTIST),the accuracy and recall rate of DIAS are improved by 8%and 17%respectively,and the results of DIAS are similar to those of manual scaling.This results shows that ionograms provided by deep-learning method can be applied to real-time global ionospheric weather nowcasting.关键词
频高图标定/深度学习/电离层测高仪/电离层Key words
ionogram scaling/deep-learning/ionosonde/ionosphere分类
地球科学引用本文复制引用
朱正平,邢蕴辉..基于深度学习的频高图自动标定[J].中南民族大学学报（自然科学版）,2025,44(4):497-506,10.基金项目
国家自然科学基金资助项目(41474135) （41474135）
