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| Titel |
Neural network based tomographic approach to detect earthquake-related ionospheric anomalies |
| VerfasserIn |
S. Hirooka, K. Hattori, M. Nishihashi, T. Takeda |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 11, no. 8 ; Nr. 11, no. 8 (2011-08-26), S.2341-2353 |
| Datensatznummer |
250009632
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| Publikation (Nr.) |
 copernicus.org/nhess-11-2341-2011.pdf |
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| Zusammenfassung |
| 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. |
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