Similarities in the self organised critical characteristics between radon and m-hz em disturbances during a very seismic period in Greece

A. A. Fotopoulosb, E. Petrakia, E. M. Vlamakisb, X. A. Argyrioub, N. N. Chatzisavvasb, T. J. Sevvosb, A. Zisosc, C. Nomicosd,

A. Louizie, J. Stonhama, P. H. Yannakopoulosb, D. Nikolopoulosc

a Brunel University, Dept. of Engineering and Design, UK

b Department of Computer Systems Engineering, Technological Educational Institute of Piraeus, Greece

c Department of Physics, Chemistry and Material Science, Technological Educational Institute of Piraeus, Greece d Department of Electronics, Technological Educational

Institution of Athens, Greece e Medical Physics Department, Medical School, University of

Athens, Greece

eRA-7

International

Scientific

Conference

http://env-hum-comp-res.teipir.gr

Address the analogous behavior of the MHz

electromagnetic signals to soil radon

Analysis of both signals with multivariate

statistics, fractal evolving techniques and

Detrended Fluctuation Analysis (DFA)

Radon (222Rn) is a radioactive gas which is present in porous materials, underground and surface waters. It has been used as a trace gas in several studies of Earth, hydro-geology and atmosphere, because of its ability to travel to comparatively long distances and the efficiency of detecting it at very low levels.

Well established criteria have been published for the identification, both of the radon precursors (Cicerone et. al,2009; Ghosh et. al.,2009) and of the precursors of the electromagnetic radiation in the ULF-kHz- MHz range (Eftaxias et. al., 2009; Eftaxias et. al., 2010). According to the earthquake classification of Hayakawa and Hobarra (2010), radon may be considered as a short-term earthquake predictor.

A station for the surveillance of soil radon has been installed in Peloponnese, Ileia Prefecture in South West Greece.

More than 600 Earthquakes of M>4,0 have been occurred in the last Century in Ileia

Radon in soil is monitored by Alpha Guard (AG) Genitron Ltd. via a properly designed unit(Soil Gas Unit, Genitron Ltd.) and accompanying equipment (Genitron, 1997)

Atmospheric pressure (AP), relative humidity (RH) and temperature (T) are continuously monitored as well

EM signals are continuously monitored by a telemetric network which consists of twelve stations (Nomikos and Vallianatos, 1998).

MHz EM radiation is detected by bipolar antennas synchronised in the 41MHz and 46MHz frequencies.

Stations are equipped with novel data-loggers designed adequately for the collection of data of the EM Network (Koulouras et al., 2005).

Fractal evolution of the EM

signals of Vamos station, 41

MHz signal, day 45, year 2008.

i) Time evolution of the

spectral exponent b

(𝑺 (𝒇 )=𝒂⋅𝒇−𝒃)

ii) Spectral exponent log(a) ,

iii) Square of the Spearman's

correlation coefficient

iv) Scalogram of the DWT

respectively.

Spearman correlation coefficient takes

values very close to 1, i.e., the fit to the

power-law is excellent. This is a strong

indicator of the fractal character of the

underlying processes and structures

(Eftaxias et al.,2010).

• Power law beta values in the range

1,5<b<2 indicate anti-persistency and

values above 2 (b>2) persistency

• Switching between persistency and

anti-persistency identifies the long

memory of the system

Vamos Station 46 MHz EM signal days 48-51 year 2008 Neapoli Station 46 MHz EM signal days 75-78 year 2008

High power-law-beta-values presented a very

peculiar increase, as high as 4.

Long-range temporal correlations indicate

strong system memory.

Each value correlates to its long-term history in

fractal manner

Anomalies detected in radon concentrations in 2008 3 & 2

months before 6,5 Earthquake of 6/8/2008

For the power law spectrum 𝑺 (𝒇 )=𝒂⋅𝒇−𝒃 • The area between the two radon

spikes is very critical and presents fractal behaviour (b values above 1,5)

• This low frequency enhancement reveals the predominance of the larger fracture events which is considered as a footprint of the preparation of earthquakes (Eftaxias et al.,2009)

Background noise presents 0<b(t)<1, moving from the the first stage of general disorder to the final stage of general failure presenting stability and self-organisements

Scalogram of the DWT of the 2008 radon signal

Time evolution of the power-law-beta values

Levels of soil radon concentration in 2008

International Scientific Conference eRA-7

Examples of the application of the DWT. (a) Radon 2008 during the five-day

disturbance of the first radon spike (Nikolopoulos et al., 2012). (b) Vamos EM

station, 41 MHz signal, day 45, year 2008. The example corresponds to the

period between the EM bursts which exhibited successive and high values of the

spectral exponent b.

When high frequencies (low negative logarithms) are superimposed on the Power

Spectrum Density, the log-log slope is reduced and, subsequently, the calculated

power-law b-value and the Spearman correlation coefficient.

DFA is a modified root-mean-square analysis of a random walk based on the following concept: a stationary time series with long-range correlations can be integrated.

The measurement of the self-similarity scaling exponent of the integrated series show the long-range correlation properties of the original time series (Peng et al.,1998).

For a given bin size n , the root-mean-square

(rms) fluctuations for this integrated and

detrended signal is calculated:

𝐹 𝑛 =1

𝑁 *𝑦 𝑘 − 𝑛(𝑘)+2𝑁

𝑘=1

Where:

1. i=1,…N a time series of length N

2. k the different time scales

3. y(k) the intergrated signal

4. n the length of each bin

• F(n) is repeated for a broad range of

scales box sizes (n).

• A power-law relation between the

average root-mean square fluctuation

F(n) and the bin size n indicates the

presence of scaling: 𝐹 (𝑛) ∼ 𝑛𝑎 • The scaling exponent α quantifies the

strength of the long-range power-law

correlations in the time series.

Example for the case of the

2008 radon signal. This

figure corresponds to the

period between the two radon

spikes.

The short time scales exhibit

lower slope (α1=1.19), while

the large time scales, higher

(α2=1.55). According to Peng et

al. (1994), these results show

persistent long range power

law correlations.

DFA scatter plot for the

2008 radon time-series.

Exponents a1 and a2

separate radon background

from high power-law-beta

values.

The high power-law-beta

values are characterised by

much larger a1 and a2

DFA scatter plot for the

the EM MHz time-series

of Vamos & Neapoli

Station of EM Telematic

Network.

These DFA values are in

close agreement to the

corresponding values of

the radon background.

Simultaneous appearance of high radon anomalies, high power-law b-values and high power spectral amplitudes, manifests that the wavelet power spectrum can be used as an alternative method for the recognition and visualisation of candidate precursory anomalies in a radon signal.

New MHz EM signals that were derived concurrently to the 2008 radon signal. The signals were analysed with the methods applied to radon. The results indicated analogous behaviour between radon and MHz EM pre-earthquake time-series.

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