Eur. Phys. J. B (2016) 89: 131
https://doi.org/10.1140/epjb/e2016-70004-0

Regular Article

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

¹ School of Mathematical Sciences, Ocean University of China, Qingdao 266100, P.R. China
² Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266061, P.R. China
³ College of Civil Aviation, NanJing University of Aeronautics and Astronautics, Nanjing 211106, P.R. China
⁴ Faculty of Management Engineering, Huaiyin Institute of Technology, Huai’an 223003, P.R. China

^a e-mail: linminouc@163.com

Received: 4 January 2016
Received in final form: 29 March 2016
Published online: 23 May 2016

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.