Regional application of multi-layer artificial neural networks in 3-D ionosphere tomography

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• 作者：Mir Reza Ghaffari Razin ; ^{rghaffari@mail.kntu.ac.ir" class="auth_mail" title="E-mail the corresponding author} ; Behzad Voosoghi
• 关键词：RMTNN ; Ionosphere modeling ; Neural network ; IRI-2012 ; GPS ; EOFs
• 刊名：Advances in Space Research
• 出版年：2016
• 出版时间：1 August 2016
• 年：2016
• 卷：58
• 期：3
• 页码：339-348
• 全文大小：2729 K

文摘

Tomography is a very cost-effective method to study physical properties of the ionosphere. In this paper, residual minimization training neural network (RMTNN) is used in voxel-based tomography to reconstruct of 3-D ionosphere electron density with high spatial resolution. For numerical experiments, observations collected at 37 GPS stations from Iranian permanent GPS network (IPGN) are used. A smoothed TEC approach was used for absolute STEC recovery. To improve the vertical resolution, empirical orthogonal functions (EOFs) obtained from international reference ionosphere 2012 (IRI-2012) used as object function in training neural network. Ionosonde observations is used for validate reliability of the proposed method. Minimum relative error for RMTNN is 1.64% and maximum relative error is 15.61%. Also root mean square error (RMSE) of 0.17 × 10¹¹ (electrons/m³) is computed for RMTNN which is less than RMSE of IRI2012. The results show that RMTNN has higher accuracy and compiles speed than other ionosphere reconstruction methods.