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This study models seismic time series as a multiplex temporal network, revealing it better captures earthquake activity than single-layer networks. This approach helps identify high-seismicity regions and understand earthquake physics.

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Area of Science:

  • Geophysics
  • Complex Networks
  • Time Series Analysis

Background:

  • Seismic time series are often mapped to complex networks using geographical cells as nodes.
  • Previous methods primarily used single-layer network representations for earthquake data.

Purpose of the Study:

  • To map seismic time series to a temporal multiplex network.
  • To analyze the evolution of network structure using eigenvector centrality.
  • To improve the representation of earthquake activity compared to single-layer models.

Main Methods:

  • Representing seismic time series as a temporal multiplex network.
  • Applying eigenvector centrality to characterize network structure evolution.
  • Generalizing previous single-layer earthquake network models.

Main Results:

  • The multiplex network representation more effectively captures seismic activity than single-layer networks.
  • Eigenvector centrality analysis successfully identified high seismological activity zones in Iran and California.
  • Temporal network modeling offers new insights into earthquake physics.

Conclusions:

  • Multiplex temporal networks provide a superior framework for analyzing seismic data.
  • Network centrality analysis is a viable tool for identifying seismic hotspots.
  • This temporal modeling approach advances the understanding of earthquake dynamics.