Application of Recurrent Neural Network to Modeling Earth's Global Electron Density

The total electron density is a fundamental quantity in the Earth's magnetosphere and plays an important role in a number of physical processes, but its dynamic global evolution is not fully quantified yet. We present an implementation of a specific type of recurrent neural network (encoder-decoder), which is distinct from previous models, to construct global electron density based on the multiyear data from Van Allen Probes. The history of geomagnetic indices is first encoded into a hidden state H, then together with auxiliary information (satellite location), they are decoded into the quantity of interest (total electron density in this study). In this process the input of historical geomagnetic indices is detangled from the satellite location and is processed chronologically by the encoder. As a result, time evolution of geomagnetic indices is explicitly embedded in the structure and the encoded hidden state H can be viewed as the representation of the inner magnetospheric state. The magnetospheric state is then decoded to predict global electron density evolution. Our results show that the model can capture the dynamical evolution of total electron density with the formation and evolution of stable and evident plume configurations that roughly agree with global observations. Our findings demonstrate the importance of applying recurrent neural networks to specify the inner magnetospheric state in a novel way, which will potentially improve our fundamental understanding of wave and particle dynamics in the Earth's magnetosphere.

Automated summary

This study presents an implementation of a specific type of recurrent neural network to construct a global electron density model based on the data from Van Allen Probes. The model can capture the dynamical evolution of total electron density and form stable and evident plume configurations.