Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning

Understanding the variation of the Thermospheric Mass Density (TMD) is important for solar-terrestrial physics and applications for spacecraft safety. The thermosphere, as an open system, is impacted by various space environment conditions and has complicated temporal and spatial features. Consequently, TMD observations contain a wealth of multi-scale feature information. How to extract such information from observations is a challenge that requires ongoing research. It is vital to improving our understanding of the TMD features. Deep learning (DL) can learn complex representations directly from raw data, which makes it a compelling feature extraction and modeling tool for providing a novel perspective for TMD modeling. The Residual Network is used in this study to build a DL model with deep network architecture. The observations of CHAllenging Minisatellite Payload are utilized in the training phase, while the Gravity Recovery and Climate Experiment, High Accuracy Satellite Drag Model and Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Extended model are used to evaluate the performance of the DL model. The results reveal that, compared with the shallow model of the typical Multi-Layer Perceptron, the DL model can better extract multi-scale features in the observations while retaining generalization capabilities. Controlled simulation experiments allow us to extract the effects of different physical processes, which improves the interpretability of the DL model. It is demonstrated that the DL model can discriminate the physical processes corresponding to the different space environment indices by simulating Equatorial Mass density Anomaly and geomagnetic storms.