eRA-7       International         Scientific                 A. A. Fotopoulosb, E. Petrakia, E. M. Vlamakisb, X. A. Argyri...
 Address  the analogous behavior of the MHz  electromagnetic signals to soil radon Analysis of both signals with multiva...
   Radon (222Rn) is a radioactive gas which is present in    porous materials, underground and surface waters. It    has ...
   A station for the surveillance    of soil radon has been    installed in Peloponnese,    Ileia Prefecture in South    ...
 EM signals are continuously  monitored by a telemetric  network which consists of  twelve stations (Nomikos and  Vallian...
Fractal evolution of the EMsignals of Vamos station, 41MHz signal, day 45, year 2008. i) Time evolution of the    spectral...
Vamos Station 46 MHz EM signal days 48-51 year 2008           Neapoli Station 46 MHz EM signal days 75-78 year 2008  High ...
Background noise presents Scalogram of the DWT      0<b(t)<1, moving from the the firstof the 2008 radon signal   stage of...
Examples of the application of the DWT. (a) Radon 2008 during the five-daydisturbance of the first radon spike (Nikolopoul...
   DFA is a modified root-       For a given bin size n , the root-mean-square                                  (rms) flu...
Example for the case of the       DFA scatter plot for the      DFA scatter plot for the2008 radon signal. This           ...
 Simultaneous appearance of high radon  anomalies, high power-law b-values and high  power spectral amplitudes, manifests...
   Peng,C.-K., Hausdor, J.M., Havlin, S., Mietus, J.E., Stanley, H.E., Goldberger,A.L.,    1998. Multiple-time scales ana...
   Eftaxias, K., Balasis, G., Contoyiannis, Y., Papadimitriou, C., Kalimeri, M.,    Athanasopoulou, L., Nikolopoulos, S.,...
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Similarities in the self organised critical characteristics between radon and m-hz em disturbances during a very seismic period in Greece

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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.

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

  1. 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, Greecehttp://env-hum-comp-res.teipir.gr
  2. 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. 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. 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. 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. 6. Fractal evolution of the EMsignals of Vamos station, 41MHz 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 Spearmans correlation coefficientiv) 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 systemSpearman correlation coefficient takesvalues very close to 1, i.e., the fit to thepower-law is excellent. This is a strongindicator of the fractal character of theunderlying processes and structures(Eftaxias et al.,2010).
  7. 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 inpeculiar increase, as high strong system memory. fractal manner as 4.
  8. 8. Background noise presents Scalogram of the DWT 0<b(t)<1, moving from the the firstof 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 ofconcentration in 2008 6/8/2008 International Scientific Conference eRA-7
  9. 9. Examples of the application of the DWT. (a) Radon 2008 during the five-daydisturbance of the first radon spike (Nikolopoulos et al., 2012). (b) Vamos EMstation, 41 MHz signal, day 45, year 2008. The example corresponds to theperiod between the EM bursts which exhibited successive and high values of thespectral exponent b.When high frequencies (low negative logarithms) are superimposed on the PowerSpectrum Density, the log-log slope is reduced and, subsequently, the calculatedpower-law b-value and the Spearman correlation coefficient.
  10. 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. 11. Example for the case of the DFA scatter plot for the DFA scatter plot for the2008 radon signal. This 2008 radon time-series. the EM MHz time-seriesfigure corresponds to the Exponents a1 and a2 of Vamos & Neapoliperiod between the two radon separate radon background Station of EM Telematicspikes.The short time scales exhibit from high power-law-beta Network.lower slope (α1=1.19), while values. These DFA values are inthe 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 ofal. (1994), these results show much larger a1 and a2 the radon background.persistent long range powerlaw correlations.
  12. 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.
  13. 13.  Peng,C.-K., Hausdor, J.M., Havlin, S., Mietus, J.E., Stanley, H.E., Goldberger,A.L., 1998. Multiple-time scales analysis of physiological time series under neural control. Physica A 249, 491–500. Kapiris, P.G., Eftaxias, K.A., Nomikos, K.D., Polygiannakis, J., Dologlou, E., Balasis, G. T., Bogris, N.G., Peratzakis, A.S., Hadjicontis, V.E., 2003. Evolving towards a critical point: A possible electromagnetic way in which the critical regime is reached as the rupture approaches. Nonlinear Proc. Geoph. 10, 1-14. Koulouras, G., Kontakos,K., Stavrakas, I., Stonham,J., Nomicos, C., 2005.A new data storage system designed for an embedded compact flash based data acquisition system.IEEE Circ. Dev. Mag. July/August 27-34. Nomikos, K., Vallianatos, F., 1998. Electromagnetic variations associated with the seismicity of the frontal Hellenic arc, Geol. Carpath. 49, 57–60. Smirnova, N.A., Hayakawa, M., 2007. Fractal characteristics of the ground- observed ULF emissions in relation to geomagnetic and seismic activities. J. Atmos. Sol.-Ter. Phy. 69,1833-1841. Cicerone, R.D., Ebel, J.E., Britton, J., 2009. A systematic compilation of earthquake precursors. Tectonophysics. 476, 371-396. Eftaxias, K., Balasis, G., Contoyiannis, Y., Papadimitriou, C., Kalimeri, M., Athanasopoulou, L., Nikolopoulos, S., Kopanas, J. , Antonopoulos, G., Nomicos, C., 2010. Unfolding the procedure of characterizing recorded ultra low frequency, kHZ and MHz electromagnetic anomalies prior to the L’Aquila earthquake as pre- seismic ones – Part 2. Nat. Hazard. Earth Sys. 10, 275–94.
  14. 14.  Eftaxias, K., Balasis, G., Contoyiannis, Y., Papadimitriou, C., Kalimeri, M., Athanasopoulou, L., Nikolopoulos, S., Kopanas, J. , Antonopoulos, G., Nomicos, C., 2009. Unfolding the procedure of characterizing recorded ultra low frequency, kHZ and MHz electromagnetic anomalies prior to the L’Aquila earthquake as pre- seismic ones – Part 1, Nat. Hazard. Earth Sys. 9, 1953–1971. Contoyiannis Y.F., Kapiris P.G., Eftaxias K.A., 2005. Monitoring of a preseismic phase from its electromagnetic precursors. Phys. Rev. E 71, 066123-1-066123-14. Ghosh, D., Deb, A., Dutta, S., Sengupta, R., 2012. Multifractality of radon concentration fluctuation in earthquake related signal. Fractals. 20 (01), 33. Erees, F.S., Aytas, S., Sac, M.M., Yener, G., Salk, M., 2007. Radon concentrations in thermalwaters related to seismic events along faults in the Denizli Basin,Western Turkey. Radiat. Meas. 42, 80-86. Hayakawa, M., Hobara, Y., 2010. Current status of seismo-electromagnetics for short-term earthquake prediction. Geomatics, Natural Hazards and Risk 1(2), 115- 155. Kapiris, P., Polygiannakis, J., Peratzakis, A., Nomicos, K., Eftaxias, K., 2002. VHFelectromagnetic evidence of the underlying pre-seismic critical stage. Earth Planets Space 54, 1237–1246.
  15. 15. http://env-hum-comp-res.teipir.gr/

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