Ionosphere tomography using wavelet neural network and particle swarm optimization training algorithm in Iranian case study

Abstract

Computerized tomography provides valuable information for imaging the ionospheric electron density distribution. We use a wavelet neural network with a particle swarm optimization training algorithm to solve pixel-based ionospheric tomography. This new method is called ionospheric tomography based on the neural network (ITNN). In this method, vertical and horizontal objective functions are minimized. Due to a poor vertical resolution of ionospheric tomography, empirical orthogonal functions are used as vertical objective function. For numerical experimentation, observations collected at 38 GPS stations on 2 days in 2007 (April 3 and July 13) from the Iranian permanent GPS network (IPGN) are used. Ionosonde observations (ź = 35.7382°, ź = 51.3851°) are used for validating the reliability of the proposed method. The modeling region is between 24°E to 40°E and 44°N to 64°N. The results of the ITNN method have been compared to those of the international reference ionosphere model 2012 (IRI-2012) and the spherical cap harmonics (SCHs) method as a local model. The minimum relative error for ITNN is 1.41% and the maximum relative error is 24.03%. Also, the root-mean-square error of 0.1932 × 1011 (el/m3) has been computed for ITNN, which is less than the RMSE of the IRI-2012 and SCHs method. The comparison of ITNN results with IRI-2012 and SCHs method shows that the proposed approach is superior to those of the traditional methods.