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Neural network based tomographic approach to detect earthquake-related ionospheric anomalies

机译：基于神经网络的层析成像方法来检测与地震有关的电离层异常

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摘要

A tomographic approach is used to investigate the fine structure of electron density in the ionosphere. In the present paper, the Residual Minimization Training Neural Network (RMTNN) method is selected as the ionospheric tomography with which to investigate the detailed structure that may be associated with earthquakes. The 2007 Southern Sumatra earthquake (M = 8.5) was selected because significant decreases in the Total Electron Content (TEC) have been confirmed by GPS and global ionosphere map (GIM) analyses. The results of the RMTNN approach are consistent with those of TEC approaches. With respect to the analyzed earthquake, we observed significant decreases at heights of 250-400 km, especially at 330 km. However, the height that yields the maximum electron density does not change. In the obtained structures, the regions of decrease are located on the southwest and southeast sides of the Integrated Electron Content (IEC) (altitudes in the range of 400-550 km) and on the southern side of the IEC (altitudes in the range of 250-400 km). The global tendency is that the decreased region expands to the east with increasing altitude and concentrates in the Southern hemisphere over the epicenter. These results indicate that the RMTNN method is applicable to the estimation of ionospheric electron density.
机译：层析成像方法用于研究电离层中电子密度的精细结构。在本文中，选择残差最小化训练神经网络（RMTNN）方法作为电离层层析成像技术，用它来研究可能与地震有关的详细结构。之所以选择2007年苏门答腊岛南部地震（M = 8.5），是因为GPS和全球电离层图（GIM）分析已证实总电子含量（TEC）显着下降。 RMTNN方法的结果与TEC方法的结果一致。关于分析的地震，我们观察到在250-400 km的高度，特别是在330 km的高度显着下降。但是，产生最大电子密度的高度不变。在获得的结构中，减小的区域位于综合电子含量（IEC）的西南和东南侧（海拔范围在400-550 km之间）和IEC的南侧（海拔范围在200至550 km之间）。 250-400公里）。全球趋势是，随着海拔的升高，减少的区域向东扩展，并集中在震中的南半球。这些结果表明，RMTNN方法适用于电离层电子密度的估算。

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