Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis
Recent observation studies have revealed that earthquakes are classified into several different categories. Each category might be characterized by the unique statistical feature in the time series, but the present understanding is still limited due to their non-linear and non-stationary nature. Her...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-04-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2021.656310/full |
| _version_ | 1818936936309456896 |
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| author | Sumanta Kundu Anca Opris Yohei Yukutake Takahiro Hatano |
| author_facet | Sumanta Kundu Anca Opris Yohei Yukutake Takahiro Hatano |
| author_sort | Sumanta Kundu |
| collection | DOAJ |
| description | Recent observation studies have revealed that earthquakes are classified into several different categories. Each category might be characterized by the unique statistical feature in the time series, but the present understanding is still limited due to their non-linear and non-stationary nature. Here we utilize complex network theory to shed new light on the statistical properties of earthquake time series. We investigate two kinds of time series, which are magnitude and inter-event time (IET), for three different categories of earthquakes: regular earthquakes, earthquake swarms, and tectonic tremors. Following the criterion of visibility graph, earthquake time series are mapped into a complex network by considering each seismic event as a node and determining the links. As opposed to the current common belief, it is found that the magnitude time series are not statistically equivalent to random time series. The IET series exhibit correlations similar to fractional Brownian motion for all the categories of earthquakes. Furthermore, we show that the time series of three different categories of earthquakes can be distinguished by the topology of the associated visibility graph. Analysis on the assortativity coefficient also reveals that the swarms are more intermittent than the tremors. |
| first_indexed | 2024-12-20T05:43:59Z |
| format | Article |
| id | doaj.art-2b7cff5d81e9431d958df2d40f3862ce |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-12-20T05:43:59Z |
| publishDate | 2021-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-2b7cff5d81e9431d958df2d40f3862ce2022-12-21T19:51:21ZengFrontiers Media S.A.Frontiers in Physics2296-424X2021-04-01910.3389/fphy.2021.656310656310Extracting Correlations in Earthquake Time Series Using Visibility Graph AnalysisSumanta Kundu0Anca Opris1Yohei Yukutake2Takahiro Hatano3Department of Earth and Space Science, Osaka University, Osaka, JapanDepartment of Earth and Space Science, Osaka University, Osaka, JapanHot Springs Research Institute of Kanagawa Prefecture, Odawara, JapanDepartment of Earth and Space Science, Osaka University, Osaka, JapanRecent observation studies have revealed that earthquakes are classified into several different categories. Each category might be characterized by the unique statistical feature in the time series, but the present understanding is still limited due to their non-linear and non-stationary nature. Here we utilize complex network theory to shed new light on the statistical properties of earthquake time series. We investigate two kinds of time series, which are magnitude and inter-event time (IET), for three different categories of earthquakes: regular earthquakes, earthquake swarms, and tectonic tremors. Following the criterion of visibility graph, earthquake time series are mapped into a complex network by considering each seismic event as a node and determining the links. As opposed to the current common belief, it is found that the magnitude time series are not statistically equivalent to random time series. The IET series exhibit correlations similar to fractional Brownian motion for all the categories of earthquakes. Furthermore, we show that the time series of three different categories of earthquakes can be distinguished by the topology of the associated visibility graph. Analysis on the assortativity coefficient also reveals that the swarms are more intermittent than the tremors.https://www.frontiersin.org/articles/10.3389/fphy.2021.656310/fullearthquakestime series analysisvisibility graphnetworkscomplex system |
| spellingShingle | Sumanta Kundu Anca Opris Yohei Yukutake Takahiro Hatano Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis Frontiers in Physics earthquakes time series analysis visibility graph networks complex system |
| title | Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis |
| title_full | Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis |
| title_fullStr | Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis |
| title_full_unstemmed | Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis |
| title_short | Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis |
| title_sort | extracting correlations in earthquake time series using visibility graph analysis |
| topic | earthquakes time series analysis visibility graph networks complex system |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2021.656310/full |
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