Validation of Ionospheric Tomography using Residual Minimization Training Neural Network

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  • Hirooka Shinji
    Graduate School of Science, Chiba University Graduate Institute of Space Science, National Central University
  • Hattori Katsumi
    Graduate School of Science, Chiba University

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  • 残差最小化学習ニューラルネットワークを用いた電離圏トモグラフィーの性能評価
  • ザンサ サイショウカ ガクシュウ ニューラルネットワーク オ モチイタ デンリケン トモグラフィー ノ セイノウ ヒョウカ

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Abstract

A numerical simulation has been done to evaluate the performance of the ionospheric tomography using the residual minimization training neural network (RMTNN) method. The results indicated that reconstruction with high-precision is possible when the standard deviation of the noise is about 2.5% or less of the average value of observed data (Slant TEC: STEC). Moreover, in the daytime when the value of STEC becomes large, the signal to noise ratio (SNR) increases and reconstruction accuracy becomes high; at night when the SNR falls conversely, it becomes low. Results of detectability tests show that the RMTNN method has a good performance around F-layer height with shape and peak intensity reconstruction. In conclusion, the developed RMTNN ionospheric tomography is effective in reconstructing 3D electron density distribution from realistic STEC data in the daytime, and is able to estimate images around F-layer.

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