Numerical validations of neural-network-based ionospheric tomography for disturbed ionospheric conditions and sparse data

Three-dimensional ionospheric tomography is effective for investigations of the dynamics of ionospheric phenomena. However, it is an ill-posed problem in the context of sparse data, and accurate electron density reconstruction is difficult. The Residual Minimization Training Neural Network (RMTNN) tomographic approach, a multilayer neural network trained by minimizing an objective function, allows reconstruction of sparse data. In this study, we validate the reconstruction performance of RMTNN using numerical simulations based on both sufficiently sampled and sparse data. First, we use a simple plasma-bubble model representing the disturbed ionosphere and evaluate the reconstruction performance based on 40 GPS receivers in Japan. We subsequently apply our approach to a sparse data set obtained from 24 receivers in Indonesia. The reconstructed images from the disturbed and sparse data are consistent with the model data, except below 200 km altitude. To improve this performance and limit any discrepancies, we used information on the electron density in the lower ionosphere. The results suggest the restricted RMTNN-tomography-assisted approach is very promising for investigations of ionospheric electron density distributions, including studies of irregular structures in different regions. In particular, RMTNN constrained by low-Earth-orbit satellite data is effective in improving the reconstruction accuracy.