地基GPS电离层异常探测研究

发布时间：2018-04-24 06:52

本文选题：电离层模型 + GPS　；参考：《西南交通大学》2014年硕士论文

【摘要】：电离层是日地空间环境的一个重要组成部分,它的剧烈变动会对人类的生产生活产生巨大的影响。对电离层本身的动态变化以及灾害前后电离层的异常变动等现象的探测研究已然成为了当前研究的热点。据相关研究发现,剧烈天气(台风、寒潮等)、日食、地震、火山爆发等都会对电离层造成不同程度的影响,从而引发电离层异常。利用地基GPS台网观测数据,通过计算电离层电子密度以及总电子含量(TEC),可以发现电离层在不同时空尺度的分布与变化特性。同时,通过探测TEC以及电子密度时间序列可以发现电离层异常现象。但是,电离层自身存在动态变化。太阳活动等引起地球空间环境的扰动,使得电离层发生不同程度的变化,呈现出周日、逐日变化,季节变化等。这使得探测难度加大。如何更可靠地探测捕捉电离层异常现象,就成为亟需解决的关键性问题。针对于这些问题,本文就电离层的周期性变化规律、电离层TEC模型以及三维层析模型的构建、TEC时间序列的异常探测等方面进行了讨论。本文的研究内容主要包括： 1)概括了电离层异常国内外研究现状,介绍了电离层分层结构及其特性。详细讨论了多种参考框架、电离层模型具体构造和特点； 2)利用CODE提供的太阳活动高峰年2013年和低峰年2006年全球电离层TEC格网模型GIM分析武汉站上空天顶方向总电子含量的周日、逐日、季节变化情况； 3)详细阐述了利用GPS双频观测值计算电离层TEC的算法原理,对GPS数据预处理、硬件延迟解算、建立格网模型进行详细的描述； 4)介绍了滑动四分位距法、滑动时窗法、卡尔曼滤波法和ARIMA时间序列法。利用上述方法对一段正常的TEC数据直接进行预测,对比分析不同方法下的预测背景值的精度：对同一段TEC数据,去除长周期和趋势变化后,再通过上述方法进行预测,比较得到的预测背景值精度。对比处理和未处理的TEC数据采用四种方法进行预测得到的背景值精度,实验结果表明：去除长周期和趋势性变化的TEC数据的预测精度要高于未处理的TEC数据；处理后的TEC数据采用滑动四分位距法和卡尔曼滤波法的预测精度要高于其余两种算法的预测精度； 5)利用IGS跟踪站、四川观测网络以及陆态网的数据对汶川地震、东日本大地震和芦山地震震前后的电离层构建局域的精细格网模型以及三维层析模型,利用两种去周期和趋势性的滑动四分位距法和卡尔曼滤波法分析地震前后震中附近地区电离层VTEC的异常变化情况,排除太阳活动和地磁扰动等影响因素后,发现三次大地震的震中附近震前几天均有TEC异常现象发生,并且异常区呈现出共轭的结构。
[Abstract]:The ionosphere is an important part of the solar-terrestrial space environment. The research on the dynamic changes of the ionosphere itself and the anomalous changes of the ionosphere before and after disasters has become a hot topic. According to relevant studies, severe weather (typhoons, cold waves, solar eclipses, earthquakes, volcanic eruptions, etc.) will cause varying degrees of influence on the ionosphere, thus causing ionospheric anomalies. The distribution and variation characteristics of ionosphere at different space-time scales can be found by calculating the ionospheric electron density and total electron content by using the ground-based GPS network data. At the same time, ionospheric anomalies can be found by detecting TEC and electron density time series. However, there are dynamic changes in the ionosphere itself. The solar activity causes the disturbance of the Earth's space environment, which makes the ionosphere change in different degrees, showing diurnal, diurnal and seasonal variations. This makes detection more difficult. How to detect and capture ionospheric anomalies more reliably has become a key problem that needs to be solved. Aiming at these problems, the regularity of periodic variation of ionosphere, the construction of ionospheric TEC model and the construction of 3D tomography model are discussed in this paper. The main contents of this paper are as follows: 1) the research status of ionospheric anomalies at home and abroad is summarized, and the structure and characteristics of ionospheric stratification are introduced. In this paper, the structure and characteristics of the ionospheric model are discussed in detail. 2) the global ionospheric TEC grid model GIM provided by CODE in 2013 and 2006 is used to analyze the total electron content in the zenith direction over Wuhan station. 3) the algorithm principle of calculating ionospheric TEC using GPS dual-frequency observations is described in detail. The data preprocessing of GPS, the solution of hardware delay and the establishment of grid model are described in detail. 4) the sliding quartile method, sliding window method, Kalman filter method and ARIMA time series method are introduced. The method is used to predict a normal TEC data directly, and the accuracy of the predicted background value under different methods is compared and analyzed. For the same section of TEC data, after removing the long period and trend changes, the method is used to predict, The accuracy of the predicted background value is compared. Compared with the unprocessed TEC data, four methods are used to predict the background value accuracy. The experimental results show that the prediction accuracy of the long-period and trend TEC data is higher than that of the unprocessed TEC data. The prediction accuracy of the processed TEC data by the sliding quartile distance method and the Kalman filter method is higher than that of the other two algorithms. 5) using the data of IGS tracking station, Sichuan observation network and land network to construct local fine grid model and three-dimensional tomography model for the ionosphere before and after Wenchuan earthquake, East Japan earthquake and Lushan earthquake. Two kinds of aperiodic and trending sliding quartile distance method and Kalman filter method are used to analyze the anomalous variation of ionospheric VTEC in the area near the epicenter before and after the earthquake. The influence factors such as solar activity and geomagnetic disturbance are excluded. It is found that the TEC anomaly occurred several days before the epicenter of the three major earthquakes, and the anomalous region presents conjugate structure.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：P352;P228.4

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