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Non-parametric detection of atmospheric radon concentration anomalies related to earthquakes.


ABSTRACT: Anomalous phenomena related to earthquakes have been studied to aid in the forecasting of large earthquakes. Radon (222Rn) concentration changes are known to be one of those phenomena. Many studies have quantified radon anomalies to identify physical aspects of radon emanations related to earthquakes. Here, we apply singular spectrum transformation, non-parametric analysis to estimate change points in time series, to atmospheric radon concentration. From 10 years of data from continuous observation of the atmospheric radon concentration over northeastern Japan and Hokkaido, we identify anomalies in the atmospheric radon concentration related to the moment releases of large earthquakes. Compared with a conventional model-based method, the singular spectrum transformation method identifies more anomalies. Moreover, we also find that change points in the atmospheric radon concentration prior to the 2011 Tohoku-Oki earthquake (Mw 9.0; 11 Mar. 2011, N38.1°, E142.9°) coincided with periods of other anomalous precursory phenomena. Our results indicate that singular spectrum transformation can be used to detect anomalies in atmospheric radon concentration related to the occurrences of large earthquakes.

SUBMITTER: Iwata D 

PROVIDER: S-EPMC6115410 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Non-parametric detection of atmospheric radon concentration anomalies related to earthquakes.

Iwata Daichi D   Nagahama Hiroyuki H   Muto Jun J   Yasuoka Yumi Y  

Scientific reports 20180829 1


Anomalous phenomena related to earthquakes have been studied to aid in the forecasting of large earthquakes. Radon (<sup>222</sup>Rn) concentration changes are known to be one of those phenomena. Many studies have quantified radon anomalies to identify physical aspects of radon emanations related to earthquakes. Here, we apply singular spectrum transformation, non-parametric analysis to estimate change points in time series, to atmospheric radon concentration. From 10 years of data from continuo  ...[more]

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