Classication of High Density Regions in Global Ionospheric Maps with Neural Networks

The database of Global Ionospheric Maps (GIMs) produced at Jet Propulsion Laboratory is analyzed. We define high density Total Electron Content (TEC) regions (HDRs) in a map, following certain selection criteria. For the first time, we use convolutional neural network (CNN) approach to analyze a diachronic sequence of GIMs by treating these HDRs as objects to be identified in the GIMs. We trained four CNNs corresponding to four phases of a solar cycle to classify the GIMs by the number of HDRs in each map with 76% accuracy. We compared HDR counts for GIMs across ten years to draw conclusions on how the number of HDRs in the GIMs changes throughout the solar cycle. Occurrence of HDRs during different geomagnetic activity conditions is discussed. Catalogue of selected HDRs for ten years and four CNN-based models that can be used to extend classification to other years are provided for the community to use.