Abstract This paper addresses issues of self-organised-critical behaviour of soil-radon and MHz-electromagnetic disorders during intense seismic activity in SW Greece. A significant radon signal is re-analysed for environmental influences with FFT and multivariate statistics. Self-organisation of signals is investigated via fractal evolving techniques and detrended fluctuation analysis. New lengthy radon data are presented and analysed accordingly. These did not present self-similarities. Similar analysis applied to new important concurrent MHz-electromagnetic signals revealed analogous behaviour to radon. The signals precursory value is discussed.

1. eRA-7
International
Scientific A. A. Fotopoulosb, E. Petrakia, E. M. Vlamakisb, X. A. Argyrioub,
Conference 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
http://env-hum-comp-res.teipir.gr

2.  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)

3.  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.

4.  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  Atmospheric pressure
Alpha Guard (AG) Genitron (AP), relative humidity
Ltd. via a properly designed (RH) and temperature (T)
unit(Soil Gas Unit, Genitron are continuously
Ltd.) and accompanying monitored as well
equipment (Genitron, 1997)

5.  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).

6. 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.
• 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
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).

7. 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-
Long-range temporal Each value correlates to
values presented a very
correlations indicate its long-term history in
peculiar increase, as high
strong system memory. fractal manner
as 4.

8. Background noise presents
Scalogram of the DWT 0<b(t)<1, moving from the the first
of the 2008 radon signal stage of general disorder to the
final stage of general failure
presenting stability and self-
organisements
For the power law spectrum
𝑺 (𝒇 )=𝒂⋅𝒇−𝒃
• The area between the two radon
spikes is very critical and presents
Time evolution of the fractal behaviour (b values above 1,5)
power-law-beta values • 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)
Anomalies detected in radon
concentrations in 2008 3 & 2
Levels of soil radon months before 6,5 Earthquake of
concentration in 2008 6/8/2008
International Scientific Conference eRA-7

9. 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.

10.  DFA is a modified root- For a given bin size n , the root-mean-square
(rms) fluctuations for this integrated and
mean-square analysis of a detrended signal is calculated:
random walk based on the 𝑁
following concept: a 1
𝐹 𝑛 = *𝑦 𝑘 − 𝑛(𝑘)+2
𝑁
stationary time series with 𝑘=1
long-range correlations Where:
can be integrated. 1. i=1,…N a time series of length N
2. k the different time scales
 The measurement of the 3. y(k) the intergrated signal
self-similarity scaling 4. n the length of each bin
exponent of the • F(n) is repeated for a broad range of
integrated series show the scales box sizes (n).
long-range correlation • A power-law relation between the
average root-mean square fluctuation
properties of the original F(n) and the bin size n indicates the
time series (Peng et presence of scaling: 𝐹 (𝑛) ∼ 𝑛 𝑎
al.,1998). • The scaling exponent α quantifies the
strength of the long-range power-law
correlations in the time series.

11. Example for the case of the DFA scatter plot for the DFA scatter plot for the
2008 radon signal. This 2008 radon time-series. the EM MHz time-series
figure corresponds to the Exponents a1 and a2 of Vamos & Neapoli
period between the two radon
separate radon background Station of EM Telematic
spikes.
The short time scales exhibit from high power-law-beta Network.
lower slope (α1=1.19), while values. These DFA values are in
the large time scales, higher The high power-law-beta close agreement to the
(α2=1.55). According to Peng et values are characterised by corresponding values of
al. (1994), these results show much larger a1 and a2 the radon background.
persistent long range power
law correlations.

12.  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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