Japan Geoscience Union Meeting 2015

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Symbol M (Multidisciplinary and Interdisciplinary) » M-GI General Geosciences, Information Geosciences & Simulations

[M-GI37] Earth and planetary informatics with huge data management

Wed. May 27, 2015 4:15 PM - 6:00 PM 203 (2F)

Convener:*Eizi TOYODA(Numerical Prediction Division, Japan Meteorological Agency), Mayumi Wakabayashi(Kiso-Jiban Consultants Co.,Ltd), Susumu Nonogaki(Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology), Eizi TOYODA(Numerical Prediction Division, Japan Meteorological Agency), Ken T. Murata(National Institute of Information and Communications Technology), Junya Terazono(The University of Aizu), Tomoaki Hori(Nagoya University Solar Terrestrial Environment Laboratory Geospace Research Center), Kazuo Ohtake(Japan Meteorological Agency), Takeshi Horinouchi(Faculty of Environmental Earth Science, Hokkaido University), Chair:Susumu Nonogaki(Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology), Kazuo Ohtake(Japan Meteorological Agency)

5:15 PM - 5:30 PM

[MGI37-08] Constructing applied Database System of Early Earthquake Warning information

Ryoga SATO1, *Kazuo OHTAKE1 (1.Meteorological College)

Keywords:Earthquake Early Warning, database, JSON, NoSQL, MongoDB

To make Earthquake Early Warning (EEW) more valuable, evaluation and verification of information are essential, as we all know. However, there is not any easy-to-use EEW dataset of alerted information up to now. We studied data structure of EEW information and constructed a new dataset suitable for evaluation, to accelerate EEW improvement process.

The EEW information has features such as:
○ one earthquake makes multiple (and variable) EEW informations
○ each data size of information is variable
○ data itself has a layer structure
Such features indicate that the table structure (or "conventional" Relational Database System) is not suitable to contain EEW information.

Emphasizing on simplicity, we chose to use JSON to write down our data format, and to utilize MongoDB to contain them. Our compilation resulted in 14.8MB data from EEW information of 7124 earthquakes from Oct. 2009 to Feb. 2014.