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In the present paper, we analyze the fractal structures in magnitude time series for a set of unprecedented sample extracted from the National Earthquake Information Center (NEIC) catalog corresponding to 12 Circum-Pacific subduction zones from Chile to Kermadec. For this end, we used the classical Rescaled Range ($R/S$) analysis for estimating the long-term persistence signature derived from scaling parameter so-called Hurst exponent, $H$. As a result, we measured the referred exponent and obtained all values of $H>0.5$, indicating that a long-term memory effect exists. The main contribution of our paper, we found a possible fractal relationship between $H$ and the $b_{s}(q)$-index which emerges from nonextensive Gutenberg-Richter law as a function of the asperity, i.e., we show that the values of $H$ can be associated with the mechanism which controls the abundance of magnitude and, therefore, the level of activity of earthquakes. Finally, we concluded that dynamics associated with fragment-asperity interactions can be emphasized as a self-affine fractal phenomenon.
Geometrical properties of landscapes result from the geological processes that have acted through time. The quantitative analysis of natural relief represents an objective form of aiding in the visual interpretation of landscapes, as studies on coast
Nonlinear time series analysis aims at understanding the dynamics of stochastic or chaotic processes. In recent years, quite a few methods have been proposed to transform a single time series to a complex network so that the dynamics of the process c
We study the betweenness centrality of fractal and non-fractal scale-free network models as well as real networks. We show that the correlation between degree and betweenness centrality $C$ of nodes is much weaker in fractal network models compared t
We propose a new type of earthquake precursor based on the analysis of correlation dynamics between geophysical signals of different nature. The precursor is found using a two-parameter cross-correlation function introduced within the framework of fl
This study analyzes friendships in online social networks involving geographic distance with a geo-referenced Twitter dataset, which provides the exact distance between corresponding users. We start by introducing a strong definition of friend on Twi