Open Journal of Applied Sciences

Volume 10, Issue 12 (December 2020)

ISSN Print: 2165-3917   ISSN Online: 2165-3925

Google-based Impact Factor: 0.92  Citations  h5-index & Ranking

Analyzing the Annual Maximum Magnitude of Earthquakes in Japan by Extreme Value Theory

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DOI: 10.4236/ojapps.2020.1012057    423 Downloads   1,411 Views  Citations
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ABSTRACT

One of the most important and interesting issues associated with the earthquakes is the long-term trend of the extreme events. Extreme value theory provides methods for analysis of the most extreme parts of data. We estimated the annual maximum magnitude of earthquakes in Japan by extreme value theory using earthquake data between 1900 and 2019. Generalized extreme value (GEV) distribution was applied to fit the extreme indices. The distribution was used to estimate the probability of extreme values in specified time periods. The various diagnostic plots for assessing the accuracy of the GEV model fitted to the magnitude of maximum earthquakes data in Japan gave the validity of the GEV model. The extreme value index, ξ was evaluated as 0.163, with a 95% confidence interval of [0.260, 0.0174] by the use of profile likelihood. Hence, the annual maximum magnitude of earthquakes has a finite upper limit. We obtained the maximum return level for the return periods of 10, 20, 50, 100 and 500 years along with their respective 95% confidence interval. Further, to get a more accurate confidence interval, we estimated the profile log-likelihood. The return level estimate was obtained as 7.83, 8.60 and 8.99, with a 95% confidence interval of [7.67, 8.06], [8.32, 9.21] and [8.61, 10.0] for the 10-, 100- and 500-year return periods, respectively. Hence, the 2011 off the Pacific coast of Tohoku Earthquake, which was the largest in the observation history of Japan, had a magnitude of 9.0, and it was a phenomenon that occurs once every 500 year.

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Maruyama, F. (2020) Analyzing the Annual Maximum Magnitude of Earthquakes in Japan by Extreme Value Theory. Open Journal of Applied Sciences, 10, 817-824. doi: 10.4236/ojapps.2020.1012057.

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