Deep Learning Modeling Research On The Relationship Between Solar Wind And Geomagnetic Storms And Earthquakes

The sun is the closest star to the Earth,and solar activity will have a constant impact on the Earth’s environment.On the one hand,the solar wind generated by solar activity reaches the Earth’s near-Earth space and interacts with the magnetosphere,which will cause disastrous space weather events such as geomagnetic storms,and have a serious impact on satellites,communications,and navigation.On the other hand,the energy injected by the solar wind into the Earth through the solar-terrestrial coupling system may affect the activity process inside the Earth,thereby triggering natural disasters such as earthquakes,and causing losses to human production activities and life safety.Therefore,it is of great significance for the prediction and prevention of natural disasters to study the influence of solar-terrestrial coupling on natural phenomena such as geomagnetic storms and earthquakes,and to establish mathematical models.This paper will use the method of deep learning to study these two aspects.The specific work is as follows:(1)For the prediction of geomagnetic storms,a convolution neural network model based on gramian angular field transformation was designed in this paper,which overcomes the problem of discontinuous information extraction and insufficient convolution receptive field that may be caused by dilated convolution.Compared with the prediction performance of the geomagnetic storm prediction model based on the dilated causal convolution and the geomagnetic storm prediction model based on the Transformer encoder structure,the results show that the model with this structure can indeed capture more inter-sequence dependencies,and the highest prediction accuracy was achieved in the short-term and medium-long term prediction of geomagnetic storms.(2)To study the correlation between earthquakes and solar activity,a one-dimensional convolution neural network model based on the dilated causal convolution structure was designed in this paper to predict the frequency of earthquakes.In the experiment,two earthquake frequency prediction models with solar activity variables and without solar activity variables were trained,and the prediction performances of the two models were analyzed.The results show that the prediction accuracy of the former is higher,indicating the correlation between solar activity and earthquakes.Furthermore,15 earthquake frequency prediction models with different inputs are trained in this paper to screen the solar activity variables that may affect the earthquake frequency prediction.The results show that all solar activity variables used in the experiment have an impact on earthquake frequency prediction.The geomagnetic storm prediction model based on gramian angular field transformation designed in this paper has achieved good prediction results in the short-term and medium-long term prediction of geomagnetic storms,and has certain practical and engineering application value.The earthquake frequency prediction model based on dilated causal convolution can better learn the complex nonlinear relationship of the solar-terrestrial coupling system,which can provide support for astronomers to study the solar-terrestrial system and provide a novel perspective for earthquake prediction.