项目名称: 基于多源数据的电离层三维精细建模及震前电离层异常时空分布规律和触发机制探究
项目编号: No.41274022
项目类型: 面上项目
立项/批准年度: 2013
项目学科: 天文学、地球科学
项目作者: 姚宜斌
作者单位: 武汉大学
项目金额: 80万元
中文摘要: 地球空间环境变化蕴含丰富的地学信息,对其变化特性的科学认知提供了一种解释地学现象的新途径。近年来电离层在地震预报中的应用引起普遍关注,由于其前兆的出现有稳定的时间尺度,这一突出特点将使它在短临地震预报中更具实用价值。本项目在地基GNSS数据的基础之上,联合LEO掩星、卫星测高等多类大地测量观测数据,充分考虑不同观测值的时空分布特点,优势互补,采用严密的数据融合方法,建立能更加有效反演电离层时变特性的高精度高分辨率全球电离层格网模型。综合现有的像素基和函数基的电离层三维层析模型的优缺点,提出一种像素基和函数基结合的新层析模型,可有效避免现有算法的缺陷,实现基于多源数据的电离层三维精细建模。在此基础上,对震前电离层异常的时空特性进行统计分析,对观测到的电离层各层电子密度的异常变化进行解释,进而初步分析震前电离层异常的触发和孕育机制,为将来实现基于震前电离层异常的地震预报新途径奠定基础。
中文关键词: 多源数据融合;全球电离层格网模型;电离层层析成像;震前电离层异常;
英文摘要: Variations of earth's space environment contain a wealth of geo-information, scientific cognition of its variation characteristics provides a new way to interpret geo-phenomenon.In recent years, ionospheric precursor in earthquake prediction has caused widespread concern.Comparing to other methods, ionospheric precursor has a relatively stable time scale, which makes it feasible for short-term prediction. On the basis of ground-based GNSS data, measurements of LEO occultation satellite and altimetry satellite can be united to inverse the ionosphere. Taking advantage of the complementary characteristics of temporal and spatial distribution of different observations, using rigorous data fusion method, the project establishes a high-precision and high-resolution global ionospheric grid model, which can inverse time-varying characteristics of the ionosphere more effectively. Considering the advantages and disadvantages of pixel-based and function-based three-dimensional ionospheric tomography method, a new tomography model acombinating pixel-based and function-based methods is proposed, which can effectively avoid the defects of the existing algorithms, and achieve a multi-source data based and three-dimensional detailed ionospheric model. On this basis, the project statistically analyzes the temporal characteristic
英文关键词: Intergration of multi-source data;Global ionospheric grid model;Computerized ionospheric tomography;Pre-earthquake ionospheric anomaly;