Flicker-noise spectroscopy: a new approach to investigate the time dynamics of geoelectrical signals measured in seismic areas

Analysis of EEG signal by flicker-noise spectroscopy: identification of right-/left-hand movement imagination

“…The FNS is a time-series analysis method that introduces parameters characterizing the components of stochastic signals in different frequency ranges [59]. The method has found numerous applications; among others it is worth mentioning about the use of FNS to the parameterization of images produced by the atomic force microscopy (AFM) [51], analysis of geological signals measured in seismic areas [15, 49], determination of electric breakdowns precursors in thin porous silicon films [39], analysis of electric potential fluctuations in electromembrane systems [58] or monitoring cutting processes and development of stability maps for materials [29]. The FNS method was also successfully applied to some problems in a medical data processing.…”

Is It Possible to Predict Strong Earthquakes?

“…The peaks were observed 52-54 days prior to the major seismic event, which is agreement with our previous results for the variation of groundwater chlorine-ion concentration where the peaks were observed in the range from 50 to 70 days at GK-1 and GK-44 boreholes prior to the major earthquake of October 8, 2001 (Ryabinin at al., 2011). Large peaks in the nonstationarity factor before strong earthquakes were also observed for other types of data, including geoelectrical signals (Telesca et al, 2004), electrochemical potentials (Descherevsky et al, 2003), and ultra-low-frequency electromagnetic transmissions (Hayakawa and Timashev, 2006). This implies that local reorganizations in the geophysical system of a particular seismic region may be related to a buildup for impending earthquakes.…”

“…We assume that a large earthquake may be preceded by a reconfiguration of a geophysical system on different time and space scales, which manifests itself in qualitative changes of various signals within relatively short time intervals. Our previous studies performed using flicker-noise spectroscopy (FNS), a phenomenological framework for extracting information from time series with stochastically varying components Polyakov, 2007, 2008;Timashev, 2007;Timashev et al, 2010a), show that the peak values in FNS nonstationarity factors, which correspond to the time moments of major rearrangements (within relatively short time intervals) of a complex geophysical system (preceding a future strong earthquake), may be considered as "precursors" of the upcoming earthquakes (Descherevsky et al, 2003;Telesca et al, 2004;Vstovsky et al, 2005;Hayakawa and Timashev, 2006;Ida et al, 2007;Ryabinin et al, 2011). These studies demonstrate that the timing of the peaks in FNS nonstationarity factors with respect to strong earthquakes may dramatically vary depending on the nature of the signals under study.…”

What Information Is Hidden in Chaotic Signals of Biological Systems?

“…In this paper we demonstrate that the practical problems related to revealing the informative essence of various chaotic signals could be resolved by introducing a new image of information hidden in chaotic signals. This image is presented in Flicker-Noise Spectroscopy (FNS) [1][2][3]. According to this phenomenological approach, the main information hidden in chaotic signals is provided by sequences of distinguishing types of irregularities -spikes, jumps, and discontinuities of derivatives of different orders, at all space-time hierarchical levels of systems.…”

“…The introduced decomposition V G (t) = V R (t) + V F (t) makes it possible to find new characteristic features of the studied signals. The V R (t) term is obtain by using a relaxing ("diffusive") procedure [2][3] for the total set of the initial time series readings. By this smoothing procedure, the "evolution of the dynamical variable" of the chaotic series is a realization of minimum high frequency information in the V R (t) component.…”