多源数据三维电离层层析算法及电离层异常扰动特性研究

项目名称： 多源数据三维电离层层析算法及电离层异常扰动特性研究

项目编号： No.41504025

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 汤俊

作者单位： 华东交通大学

项目金额： 23万元

中文摘要： 空间环境与人类的生存息息相关，电离层探测是人类认知空间环境的手段之一。随着探测技术的发展，电离层层析成像技术的出现为对空间环境的认知精细提供了一个新途径。然而，参与建模的数据信息不足及其衍生的层析模式所固有的不适定性问题，成为制约该技术推广应用的主要瓶颈。本项目联合地基GNSS、LEO 掩星、卫星测高等多类大地测量观测数据，充分互补不同观测值时空分布特点，采用严密的数据融合方法，建立能更加有效反演电离层电子密度三维空间特性的模型。针对电离层数学函数和参数选取的困难性，提出一种贝叶斯MARS的电离层层析模型，实现基于多源数据的层析成像模式。在此基础上，利用磁暴以及磁静等空间环境下的资料，对电离层电子密度三维空间结构变化特征及其产生机制进行分析研究。本项目旨在研究多源数据的融合以及层析模式的拓展，从而有效的改善数据信息不足和精化现有的电离层层析模式，为人类对空间环境的认知奠定理论和技术基础。

中文关键词： 电离层层析；GNSS；多源数据；贝叶斯MARS；电离层扰动

英文摘要： Space environment is closely related to human survival. The ionospheric sounding is one of the ways for human to cognize space environment. With the development of sounding technique, the computerized ionospheric tomography for the understanding and refining of the space environment is a new way. However, involved in the modeling data information is lack and its derivative tomographic pattern have inherently ill-posed problems, which become the main bottleneck restricting popularization and application of the technique. This project combined many kinds of geodetic observation data which are ground-based GNSS, LEO occultation, satellite altimetry and so on to establish more effective inversion model of ionospheric electron density three-dimensional space characteristics, which are fully complementary different observation time and space distribution characteristics and use the strict method of data fusion. In view of the difficulties of the ionospheric mathematical functions and parameters selection, we propose a kind of CIT model used bayesian method and multivariate adaptive-regression splines, and realize multi-source data of tomographic imaging mode. On this basis, we use the data under the condition of magnetic storm and magnetic static to analyze and research the characteristics of ionospheric electron density three-dimensional space structure change and its mechanism. This project aims to study of multi-source data fusion and the expansion of CIT mode, so as to effectively improve the data information and refine the existing CIT analysis model, and lay the theoretical and technical foundation for our understanding of the space environment.

英文关键词： computerized ionospheric tomography;GNSS;multi-source data ;bayesian multivariate adaptive-regression splines;ionospheric disturbances