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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| Published in: | Entropy (Basel, Switzerland) Vol. 27; no. 2; p. 178 |
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| Abstract | 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 (k) versus magnitude (M) graph (k-M 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 k-M 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. |
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| AbstractList | 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 (k) versus magnitude (M) graph (k-M 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 k-M 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. 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 (k) versus magnitude (M) graph (k-M 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 k-M 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.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 (k) versus magnitude (M) graph (k-M 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 k-M 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. 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 ( ) versus magnitude ( ) graph ( 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 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. |
| Author | Solis-Montufar, Eric Eduardo Zamora-Justo, José Alberto Aguilar-Molina, Ana María Muñoz-Diosdado, Alejandro |
| AuthorAffiliation | Unidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Mexico City 07340, Mexico; amunozdi@ipn.mx (A.M.-D.); anafrom@hotmail.com (A.M.A.-M.); eric.montufar@gmail.com (E.E.S.-M.) |
| AuthorAffiliation_xml | – name: Unidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Mexico City 07340, Mexico; amunozdi@ipn.mx (A.M.-D.); anafrom@hotmail.com (A.M.A.-M.); eric.montufar@gmail.com (E.E.S.-M.) |
| Author_xml | – sequence: 1 givenname: Alejandro orcidid: 0000-0001-9872-8304 surname: Muñoz-Diosdado fullname: Muñoz-Diosdado, Alejandro – sequence: 2 givenname: Ana María orcidid: 0000-0003-0367-2366 surname: Aguilar-Molina fullname: Aguilar-Molina, Ana María – sequence: 3 givenname: Eric Eduardo orcidid: 0000-0002-3903-5724 surname: Solis-Montufar fullname: Solis-Montufar, Eric Eduardo – sequence: 4 givenname: José Alberto orcidid: 0000-0002-2946-3899 surname: Zamora-Justo fullname: Zamora-Justo, José Alberto |
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| Cites_doi | 10.1029/2004GL020892 10.1007/s00024-017-1617-8 10.3390/e25040677 10.1038/30918 10.1016/j.neuroimage.2017.05.047 10.1103/PhysRevLett.96.238701 10.1063/5.0007480 10.1140/epjb/e2013-40762-2 10.1007/978-1-4757-5426-1 10.1103/RevModPhys.74.47 10.1029/JB094iB11p15635 10.3390/e25050773 10.1016/j.physrep.2012.01.007 10.1209/0295-5075/97/50002 10.1016/j.physa.2013.08.078 10.1029/2023JB026811 10.1073/pnas.0709247105 10.1103/PhysRevLett.84.4006 10.1103/PhysRevLett.59.381 10.1016/j.physa.2014.08.048 10.3389/fphys.2016.00044 10.1007/s00024-018-1947-1 10.1016/0040-1951(90)90298-M 10.1209/0295-5075/103/50004 10.1103/PhysRevLett.68.1244 10.1371/journal.pone.0106233 10.1007/978-3-642-35139-6_18 10.1016/j.physa.2012.05.049 10.1029/2004JB003209 10.1093/acprof:oso/9780199206650.003.0001 10.1016/j.physrep.2006.11.001 10.1038/355071a0 10.1029/92JB00427 10.21767/2172-0479.100079 10.1103/PhysRevLett.88.178501 |
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| Keywords | seismicity series visibility graph algorithm complex networks spring-block model seismicity from California |
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| SubjectTerms | Algorithms Clustering complex networks Connectivity Earthquakes Graph theory Heart rate Regression analysis Seismic activity Seismic response Seismicity seismicity from California seismicity series spring-block model Time series Trends Visibility visibility graph algorithm |
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| Title | Analysis of Aftershocks from California and Synthetic Series by Using Visibility Graph Algorithm |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/40003175 https://www.proquest.com/docview/3170908320 https://www.proquest.com/docview/3171378068 https://pubmed.ncbi.nlm.nih.gov/PMC11853820 https://doaj.org/article/4795e57bc0694d65be91e48e450fd162 |
| Volume | 27 |
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