Total electron content prediction method for global ionosphere based on deep learning framework

Abstract

The invention relates to a total electron content prediction method for a global ionosphere based on a deep learning framework. The method comprises the steps: performing grid partitioning on a globaltotal electron content according to different response characteristics during geomagnetic disturbance, and on the basis, constructing a deep learning prediction model through an LSTM framework; setting the training sample length and the training frequency according to the periodic change characteristics of the global total electron content, and acquiring a pre-training model; analyzing the lengthof the training sample and the number of training times in combination with different sun activity and geomagnetic activity environments to obtain an optimal training model; finally, predicting the total electron content of the future global ionized layer by utilizing the training model, so a prediction error is within an expected range. The method can effectively predict the total electron content of the ionized layer in different regions of the world, so as to predict the total electron content of the global ionosphere, and provide better technical support for related applications such as space weather prediction and satellite navigation ionized layer error correction.

Description

technical field [0001] The invention belongs to the field of ionospheric prediction, in particular to a method for predicting the total electron content of the global ionosphere based on a deep learning framework. Background technique [0002] The ionosphere is the upper atmosphere located 60km to 1000km above the earth, and it is an important space strategic resource. The ionosphere is filled with a large number of charged particles and free electrons, which have a certain impact on the radio waves penetrating the earth's atmosphere, and are one of the important sources of ranging errors for satellite navigation signals in the atmosphere. At the same time, the ionosphere is closely affected by solar activity and the space magnetic field, and strong disturbances will occur during solar storms and geomagnetic storms, forming an anomaly in the total electron content. At present, the in-depth understanding of ionospheric activities is a hot and difficult research topic in rela...

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Problems solved by technology

Considering that during solar storms, geomagnetic storms and geomagnetic disturbances, there may be fluctuations and anomalies in the total electron content of the ionosphere, which is an important physical quantity reflecting the evolution of space weather, so the accurate total electron content of the ionosphere is the key to unraveling the relationship between space weather. An important link of deep theoretical mechanism; at the same time, the nonlinear and non-stationary changes of the total electron content of the ionosphere will affect the signal reception and application performance of navigation communication, leading to the deterioration of the availability of navigation communication system

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