Entropy of geoelectrical time series in the natural time domain

Abstract

Abstract. Seismic electric signals (SES) have been considered precursors of strong earthquakes, and, recently, their dynamics have been investigated within the Natural Time Domain (NTD) (Varotsos et al., 2004). In this paper we apply the NTD approach and the chaotic map signal analysis to two geoelectric time series recorded in a seismically very active area of Mexico, where two strong earthquakes, M = 6.6 and M = 7.4, occurred on 24 October 1993 and 14 September 1995, respectively. The low frequency geoelectric signals measured display periods with dichotomic behavior. Our findings point out to an increase of the correlation degree of the geoelectric signals before the occurrence of strong earthquakes; furthermore, the power spectrum and entropy in NTD are in good agreement with the results published in literature. Our results were validated by the analysis of a chaotic map simulated time series, which revealed the typical characteristics of artificial noise.

Entropy in Natural Time and the Associated Complexity Measures

“…Relations (14) have been also found to be valid [60] for long duration SES activities, such the one depicted in Figure 1e, although a more complicated analysis is necessary for this case (see Section 5). It is important to mention that S together with κ 1 have been also found to obey Equation (5) and Inequality (14) for the electric field variations that were recorded at Mexico [61] and preceded the magnitude M = 6.6 earthquake on 24 October 1993 and the M = 7.4 of the Guerrero earthquake on 14 September 1995.…”

Investigations into the Correlation Properties of Membrane Electroporation-Induced Inward Currents: Prediction of Pore Formation

“…Detrended fluctuation analysis (DFA) is a scaling analysis that has proven to be successful in determining the correlation properties of non-stationary time series derived from biological, physical, and social systems [7][8][9][10][11]. It uses the scaling exponent a to represent the correlation properties of a time series and permits detection of statistical persistence behavior embedded in non-stationary time series.…”

On the Motivation and Foundation of Natural Time Analysis: Useful Remarks

“…Examples of data analysis in this time domain carried out by a multitude of authors (e.g., Vargas et al, 2015, Flores-Márquez et al, 2014Ramirez-Rojas et al, 2011, Rundle et al, 2012Holliday et al, 2006) have appeared in diverse fields, including Biology, Cardiology, Condensed Matter Physics, Environmental Sciences, Geophysics, Physics of Complex Systems, Statistical Physics, and Seismology. Several of these applications have been compiled in a monograph by Varotsos et al (2011a), where the foundations of natural time analysis have been also explained in detail by providing the necessary mathematical background in each step.…”