Seasonal Variation of the D-Region Ionosphere Modelled using Machine Learning Based VLF Remote Sensing

Abstract

This work improves upon a previously developed neural network modelling process that predicted waveguide parameters for the D-region ionosphere on two days [1]. The previous model was limited by manually determining the ideal set of transmitters (Tx) and receivers (Rx) and by computation time. An automatic quality assessment tool was developed to automatically evaluate the optimal network for each day [2]. We also obtained a 14x improvement in model training time by leveraging GPUs and improving the parallelization of the training process. These advancements allowed us to model 328 days across up to 21 paths. With this larger sample size, we show the model is capable of following expected seasonal trends. The model has also been adapted to be used with nighttime data, and is showing promising early results.