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Generalized two-dimensional principal component analysis and two artificial neural network models to detect traveling ionospheric disturbances

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Abstract

A weak tsunami was induced by the 2016 Mw = 7.8 Sumatra earthquake, which occurred at 12:49 on March 2, 2016 (UTC). The epicenter was at 5.060°S, 94.170°E at a depth of 10 km. At 15.02 on March 2 (UTC), the weak tsunami (amplitude: 0.11 m) arrived at the station located at 10.40°S, 105.67°E. Two largest principal eigenvalues derived using the bilateral projection-based two-dimensional principal component analysis (B2DPCA) indicated a spatial traveling ionospheric disturbance (TID), which was caused by internal gravity waves, at 13:20 on March 2. Two largest principal eigenvalues represented another TID expanding to the southwest. These two TIDs were also determined using two back-propagation neural network (BPNN) models and two convolutional neural network models, called the BPNN-B2DPCA and CNN-B2DPCA methods, respectively. These two methods yielded the same results as the B2DPCA. Therefore, the reliability of B2DPCA was validated.

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Source: GDGPS system)

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Data Availability

The TEC data can be obtained in NASA Global Differential GPS system (GDGPS).

http://www.gdgps.net/products/tec-maps.html.

The Dst indices: http://wdc.kugi.kyoto-u.ac.jp/dst_final/index.html.

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Acknowledgements

The author is grateful to the NASA Global Differential GPS system (GDGPS) and to the World Data Center for Geomagnetism, Kyoto (M. Nose, T. Iyemori, M. Sugiura, T. Kamei [2015], Geomagnetic Dst index, https://doi.org/10.17593/14515-74000). The author is also grateful to several geomagnetic observatories (Kakioka [JMA], Honolulu and San Juan [USGS], Hermanus [RSA], INTERMAGNET and many others) for their cooperation to make the final Dst index available. The author is also grateful to Dr. Primoz Potocnik for providing the MatLab code. The author dedicates this paper to the memory of his mother, who died on October 9, 2016.

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Correspondence to Jyh-Woei Lin.

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Lin, JW. Generalized two-dimensional principal component analysis and two artificial neural network models to detect traveling ionospheric disturbances. Nat Hazards 111, 1245–1270 (2022). https://doi.org/10.1007/s11069-021-05093-x

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  • DOI: https://doi.org/10.1007/s11069-021-05093-x

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