Prediction On TEC And Its Application In Seismic Detection Based On Model EEMD-SE-ARIMA

Ionosphere is an important part of terrestrial space and key point in study on exploration of the space,deep research on ionosphere is good for better recognition,utilization and protection of it.As a significant parameter of form and structure of the ionosphere,the features of temporal and spatial distribution and change in Total Electron Content(TEC)of ionosphere reveal basic features of ionosphere.The study on TEC has provided data and theoretical supports for increase in accuracy of navigator fix and short-imminent precursor of the earthquake.The main research contents in this thesis are as follows:First,it has introduced the content and format of Global Ionosphere Maps(GIM)published by The Center for Orbit Determination in Europe(CODE)and researched influences of solar activity,geomagnetic activity,daily variation and seasonal fluctuation on temporal and spatial distribution of TEC using this data.The result of the study shows that changes in TEC have consistency with solar activity;magnetic storm would expand abnormal scope of the equator;daily variation and seasonal fluctuation would cause mobility of peak value of TEC globally.Second,this thesis has introduced effects of decomposition of Empirical Mode Decomposition(EMD)and Ensemble Empirical Mode Decomposition(EEMD)on TEC data and put forward data processing model EEMD-SE based on EEMD and Sample Entropy(SampEn,SE).Processing TEC data with this model could withdraw disturbance term,cycle term,stochastic term and trend term of TEC data effectively,which would provide proper method of data decomposition for building more accurate TEC predictive model.Third,in view of the nonlinear and non-stationary characteristics of TEC data and the wide application of ARIMA model in the field of time series prediction,this thesis has put forward TEC predictive model of ionosphere EEMD-SE-ARIMA and conducted detailed introduction on the procedure of model building and selection of parameters and thresholds of the model.It has conducted prediction in 5 days successively on TEC in different seasons and latitudes with models ARIMA and EEMD-SE-ARIMA respectively using TEC data provided by CODE in 2017,it shows after analysis on the result of prediction that the accuracy of prediction of the model EEMD-SE-ARIMA is obvious better than that of the model ARIMA,which has improved the issue of poor accuracy near the extreme point of the model ARIMA effectively and provided a new way for study and modeling of TEC and short-term forecast on TEC.Fourth,in view of the poor accuracy of the reference background value calculated by the traditional ionospheric TEC anomaly detection method,it has put forward detecting method of abnormal TEC before earthquake based on the model EEMD-SE-ARIMA and avoided probe error caused by systematic deviation.It has conducted abnormal detections with this method for earthquakes occurred in Jiuzaigou on 8th August,2017 and Alaska on 30 th November,2018 respectively,the results show that anomalies of TEC detected in the seismogenic zone and around in 13 days,11 days and 4 days before the earthquake and on the day of earthquake in Jiuzhaigou were caused by gestation of an earthquake and anomalies of TEC detected in the seismogenic zone and around in 10 days and 2 days before the earthquake in Alaska were caused bygestation of an earthquake,anomalies detected have not been vertical to the epicenter but rather distributed to the south or north of the seismogenic zone,anomalies move from the east to the west and gradually shift to the equator and have conjugated structure.