Network structure entropy and its dynamical evolution for recurrence networks from earthquake magnitude time series

Min Lin (), Xing Xing Fan, Gang Wang and Gang Zhao
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Min Lin: School of Mathematical Sciences, Ocean University of China
Xing Xing Fan: School of Mathematical Sciences, Ocean University of China
Gang Wang: Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology
Gang Zhao: College of Civil Aviation, NanJing University of Aeronautics and Astronautics

The European Physical Journal B: Condensed Matter and Complex Systems, 2016, vol. 89, issue 5, 1-7

Abstract: Abstract Based on the theory of complex network, we construct a recurrence network for earthquake magnitude time series from California. Network structure entropy and its dynamical evolution of the network is studied. It is found that the network structure entropy of the recurrence network exhibits a peculiar behavior: it stays at a small value before main shock, jumps to a great value at the main shock, and then recovers to normal values gradually. The network structure entropy therefore provides us an approach to characterize main shocks quantitatively.

Keywords: Statistical; and; Nonlinear; Physics (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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DOI: 10.1140/epjb/e2016-70004-0

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