Analysis of Aftershocks from California and Synthetic Series by Using Visibility Graph Algorithm
The use of the Visibility Graph Algorithm (VGA) has proven to be a valuable tool for analyzing both real and synthetic seismicity series. Specifically, VGA transforms time series into a network representation in which structural properties such as node connectivity, clustering, and community structu...
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2025-02-01
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| author | Alejandro Muñoz-Diosdado Ana María Aguilar-Molina Eric Eduardo Solis-Montufar José Alberto Zamora-Justo |
| author_facet | Alejandro Muñoz-Diosdado Ana María Aguilar-Molina Eric Eduardo Solis-Montufar José Alberto Zamora-Justo |
| author_sort | Alejandro Muñoz-Diosdado |
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| description | The use of the Visibility Graph Algorithm (VGA) has proven to be a valuable tool for analyzing both real and synthetic seismicity series. Specifically, VGA transforms time series into a network representation in which structural properties such as node connectivity, clustering, and community structure can be quantitatively measured, thereby revealing underlying correlations and dynamics that may remain hidden in traditional linear or spectral analyses. The time series transformation into complex networks with VGA provides a new approach to analyze seismic dynamics, allowing scientists to extract trends and behaviors that may not be possible by classical time-series analysis. On the other hand, many studies attempt to find viable trends in order to identify preparation mechanisms prior to a strong earthquake or to analyze the aftershocks. In this work, the seismic activity of Southern California Earthquake was analyzed focusing only on the significant earthquakes. For this purpose, seismic series preceding and following each earthquake were constructed using a windowing method with different overlaps and the slope of the connectivity (<i>k</i>) versus magnitude (<i>M</i>) graph (<i>k-M</i> slope) and the average degree were computed from the mapped complex networks. The results revealed a significant decrease in these parameters after the earthquake, due to the contribution of the aftershocks from the main event. Interestingly, the study was extended to synthetic seismicity series and the same behavior was observed for both <i>k-M</i> slope and average degree. This finding suggests that the spring-block model reproduces a relaxation mechanism following a large-magnitude event like those of real seismic aftershocks. However, this conclusion contrasts with conclusions drawn by other researchers. These results highlight the utility of VGA in studying events that precede and follow major earthquakes. This technique may be used to extract some useful trends in seismicity, which could eventually be employed for a deeper understanding and possible forecasting of seismic behavior. |
| format | Article |
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| institution | DOAJ |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-4795e57bc0694d65be91e48e450fd1622025-08-20T02:44:32ZengMDPI AGEntropy1099-43002025-02-0127217810.3390/e27020178Analysis of Aftershocks from California and Synthetic Series by Using Visibility Graph AlgorithmAlejandro Muñoz-Diosdado0Ana María Aguilar-Molina1Eric Eduardo Solis-Montufar2José Alberto Zamora-Justo3Unidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Mexico City 07340, MexicoUnidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Mexico City 07340, MexicoUnidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Mexico City 07340, MexicoUnidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Mexico City 07340, MexicoThe use of the Visibility Graph Algorithm (VGA) has proven to be a valuable tool for analyzing both real and synthetic seismicity series. Specifically, VGA transforms time series into a network representation in which structural properties such as node connectivity, clustering, and community structure can be quantitatively measured, thereby revealing underlying correlations and dynamics that may remain hidden in traditional linear or spectral analyses. The time series transformation into complex networks with VGA provides a new approach to analyze seismic dynamics, allowing scientists to extract trends and behaviors that may not be possible by classical time-series analysis. On the other hand, many studies attempt to find viable trends in order to identify preparation mechanisms prior to a strong earthquake or to analyze the aftershocks. In this work, the seismic activity of Southern California Earthquake was analyzed focusing only on the significant earthquakes. For this purpose, seismic series preceding and following each earthquake were constructed using a windowing method with different overlaps and the slope of the connectivity (<i>k</i>) versus magnitude (<i>M</i>) graph (<i>k-M</i> slope) and the average degree were computed from the mapped complex networks. The results revealed a significant decrease in these parameters after the earthquake, due to the contribution of the aftershocks from the main event. Interestingly, the study was extended to synthetic seismicity series and the same behavior was observed for both <i>k-M</i> slope and average degree. This finding suggests that the spring-block model reproduces a relaxation mechanism following a large-magnitude event like those of real seismic aftershocks. However, this conclusion contrasts with conclusions drawn by other researchers. These results highlight the utility of VGA in studying events that precede and follow major earthquakes. This technique may be used to extract some useful trends in seismicity, which could eventually be employed for a deeper understanding and possible forecasting of seismic behavior.https://www.mdpi.com/1099-4300/27/2/178seismicity seriesseismicity from Californiaspring-block modelvisibility graph algorithmcomplex networks |
| spellingShingle | Alejandro Muñoz-Diosdado Ana María Aguilar-Molina Eric Eduardo Solis-Montufar José Alberto Zamora-Justo Analysis of Aftershocks from California and Synthetic Series by Using Visibility Graph Algorithm Entropy seismicity series seismicity from California spring-block model visibility graph algorithm complex networks |
| title | Analysis of Aftershocks from California and Synthetic Series by Using Visibility Graph Algorithm |
| title_full | Analysis of Aftershocks from California and Synthetic Series by Using Visibility Graph Algorithm |
| title_fullStr | Analysis of Aftershocks from California and Synthetic Series by Using Visibility Graph Algorithm |
| title_full_unstemmed | Analysis of Aftershocks from California and Synthetic Series by Using Visibility Graph Algorithm |
| title_short | Analysis of Aftershocks from California and Synthetic Series by Using Visibility Graph Algorithm |
| title_sort | analysis of aftershocks from california and synthetic series by using visibility graph algorithm |
| topic | seismicity series seismicity from California spring-block model visibility graph algorithm complex networks |
| url | https://www.mdpi.com/1099-4300/27/2/178 |
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