A Greek version of the reserch scientific activities of this topic can be found in odf.pdf  format.

OVERVIEW

The research activities of this field focus on the detection and analysis of disturbances of environmental electromagnetic radiation of the kHz and MHz bands and of radon in soi. The purpose is to investigate the environmental radiation series emitted from the related Geo-systems for hidden spatio-temporal chaotic-fractal and long-memory trends and for traces of self-organised critical (SOC) states. Measurements are conducted continously through a telemetric network of twelve electromagnetic stations and three radon stations. Data are analysed through sliding and gliding time-window Wavelet& Fourier  based Fractal Analysis, Detrended Fluctuation Analysis, Rescaled-Range Analysis, Support Vector Machines, and through analysis of several entropy metrics.

The overall aproach could be considered as a useful tool in delineating the Geo-processes during preparation and evolution of earthquakes and other devastating phenomena. The focus of the following analysis is centered arround the seismical systems and especailly those providing notable pre-earthquake signs of forthcoming events.  

In the following, significant scientific evidence is presented regarding forecasting of earthquakes. The analysis has three parts: (1) The first Part is an outline of the precursos of general failure of Geosytems focusing on the short-term ones; (2) The second part presents the scientific data regarding kH-MHz zdisturbances of the environmental electromagnetic radiation from Geosystems; (3) The last part presents the radon emissions from Geosystems.

PART 1:BRIEF OUTLINE.

Introduction

Natural events like earthquakes, tsunamis and volcanic eruptions are inevitable. What makes these events more dangerous and disastrous is not that they are inevitable but that they are still extremely hard to predict. Therefore, it is one of the major challenges for the world scientific community to find a reliable seismic precursor. The researchers have started efforts towards this direction a lot of decades ago. However, the problem of earthquake prediction remains unsolved. Precursors recorded for certain earthquakes indicate there is evidence that they can be used for forecasting. In case of an earthquake rupture, certain precursory activity can be expected, if the observation is made in the near vicinity of causative fracture. The problem of earthquake prediction consists of consecutive, step-by-step, narrowing of the time interval, space and magnitude ranges, where a strong earthquake should be expected [1]. Five stages of prediction are usually distinguished. The background stage provides maps with the territorial distribution of the maximum possible magnitude and recurrence time of destructive earthquake of different magnitudes. Four subsequent stages, fuzzily divided, include the time prediction; they differ in the characteristic time interval covered by an alarm. These stages are as follows [2]: long-term (10¹ years); intermediate-term (1 year); short-term (10^-1 to 10^-2 years), and immediate-term (10^-3 years or less). Such division into stages is dictated by the character of the process that leads to a strong earthquake and by the needs of earthquake preparedness; the latter comprises an arsenal of safety measures for each stage of prediction [1].According to Hayakawa and Hobara [3] the prediction of earthquakes is classified into three categories: long-term (timescale of 10 to 100 years); intermediate-term (time-scale of 1 to 10 years); short-term. Note, that even in short-term prediction there is no one-to-one correspondence between anomalies in the observations and the earthquake events [4,5]. Although much more difficult than the long-term and intermediate-term predictions, short-term prediction of earthquakes on a time-scale of hours, days or weeks, is believed to be of the highest priority for social demands in seismo-active countries.

The short-term earthquake precursors related with electromagnetic effects are promising tools for earthquake prediction. The subjective study of seismo-electromagnetism refers to electric and magnetic field anomalies [6]observed during seismicity. Various studies have shown that these pre-seismic electromagnetic emissions occur in wide frequency band ranging from few Hz to MHz. Global efforts to predict earthquakes were started about a century ago and peakedduring 1970s. The first scientifically well documented earthquake prediction was made on the basis of temporal and spatial variation of ts/tp relation in Blue mountain Lake, New York on 3rd August, 1973 [7]. Seismologists then successfully predicted the M7.4 Heicheng China earthquake of February 4, 1975 (Cha Chi Yuan), which raised the hopes that it could be possible to make reliable earthquake forecasts. Because of this prediction, an alert was issued within the 24-hour period prior to the main shock, probably preventing a larger number of casualties than the 1328 deaths that actually occurred from this event. However, the failure to predict another devastating earthquake 18 months later, the 1976 M7.8 Tangshan earthquake, was a major setback to the earthquake prediction effort. Casualties from this earthquake numbered in the hundreds of thousands[8]. The seismologists have now narrowed down their studies from long term prediction to short term prediction [9]. The studies carried out in past three decades have given birth to the new field of seismo-electromagnetism. Several research groups all over the world have shown evidences of electromagnetic emissions and anomalies before earthquakes.

Despite the scientific efforts, the preparation and evolution of earthquakes is not delineated yet. A significant reason is that there is restricted knowledge of the fracture mechanisms of the crust [4,10-26]. This is reinforced by the fact that each earthquake is particular and happens in large-scale. Accounting that the fracture of heterogeneous materials is not sufficiently described yet, despite the tremendous up-to-date effort at laboratory, theoretical and numerical level [4], it may be understood why the description of the genesis of earthquakes is still limited[4,10-26]. According to Eftaxias [4] one should expect that the preparatory processes of earthquakes have various facets, which may be potentially observed before the final catastrophe at geological, geochemical, hydrological and environmental scales [4] .

Short-term forecasting of earthquakes-Problems and limitations

The science of short-term earthquake prediction is the study of earthquake precursors. In fact, short-term predictions are typically based on observations of these types of phenomena. Earthquake precursors include serendipitous observations of physical processes ahead of at least some earthquakes [8]. These processes comprise detection of anomalies in electromagnetic fields, fluctuations in ground-water level, gas emissions, surface distortions due to pressure differences , changes in ionospheric parameters etc. [8]. It is important however to note that ULF, kHz and MHz EM anomalies have been detected over periods ranging from a few days to a few hours prior to recent destructive earthquakes that occurred in land or were strong and shallow [10-26]. Finally, it is the subject of seismo-electromagnetism to study electric and magnetic field anomalies observed during seismicity [6]. The related studies have reported pre-seismic electromagnetic emissions in wide frequency bands ranging from 0.001 Hz to MHz.

Two major criteria are significant in identifying earthquake precursors. The first criterion is to recognise credible scientific evidence regarding anomalies observed prior to earthquakes [4]. The successful measurement of some anomalous phenomenon prior to an earthquake usually depends on the luck of having a good scientific experiment operating in an area before, during and after an earthquake. The second criterion for the selection of the earthquake precursors is that there are accepted physical models to explain the existence of the precursor[8].

On the other hand, in material science and in geophysics, it is vital to identify precursors of macroscopic defects or shocks [4]. And this, because fracture induced physical fields allow real-time monitoring of damage evolution in materials during mechanical loading. A stressed rock behaves like a stress-electromagnetic transformer. The crack propagation is the basic mechanism of the failure of the material [20] . In many materials emission of photons, electrons, ions and neutral particles is observed during the formation of new surface features after fracturing, deformation, wearing, peeling etc. [18-22] . Collectively, these emissions are referred as fracto-emissions [20]. The rupture of inter-atomic (ionic) bonds also leads to intense charge separation that is the origin of the electric charge between the micro-crack faces. On the faces of a newly created micro-crack the electric charges constitute an electric dipole or a more complicated system. The motion of a crack has been shown to be governed by a dynamical instability causing oscillations in its velocity and structure on the fracture surface. Experimental evidence indicate that the instability mechanism is that of local branching: a multi-crack state is formed by repetitive, frustrated micro-fracturing events. It is worth mentioning that laboratory experiments show that more intense fracto-emissions are observed during the unstable crack growth [20, 27-30]. Due to the strong wall vibration of cracks in the stage of the micro-branching instability, the fractured material behaves as an efficient electromagnetic emitter. Thus, when a material is strained, electromagnetic emissions in a wide frequency spectrum ranging from Hz to MHz are produced by opening cracks, which can be considered as the so-called precursors of general fracture. These electromagnetic precursors are detectable both at laboratory and geological scale [4,18-30] . In the above sense, it becomes evident that the main tool of the prediction of earthquakes is the monitoring of the micro-fractures, which possibly occur in the focal area before the final break-up, by recording their electromagnetic emissions [4].

Several investigations on earthquake prediction were based on visual observations. Numerous have utilized concepts from the theory of entropy and information [21,24,26,31]. Alternative approximations employed the use of fractal methods, symbolic dynamics, Natural Time, Hurst Exponent and DFA (Detrended Fluctuation Analysis) [4,10-33]. Usually employed entropy metrics were: [4,22,24,26,28] (i) Shannon entropy per letter (ii) Conditional entropy (iii) Entropy of the source (iv) T-entropy (v) Tsallis entropy (vi) Hurst exponent (vii) Fisher Information (viii) Perturbation entropy (ix) Fractal dimension. Pre-seismic EM precursors were investigated in terms of critical phenomena as well [16,19,22].

Most disturbances were analyzed visually. Analysis based on advanced techniques has been reported in some cases. More significant seem to be the Natural Time and Detrended Fluctuation Analysis, the evolution of fractal dimension and Hurst exponent and the temporal changes of various metrics of entropy. The latter techniques investigated in detail, traces of long-memory hidden in pre-earthquake time-series or features of self-organization of the earthquake generating system.

PART 2:ENVIRONMENTAL ELECTROMAGNETIC RADIATION ( kHz - MHz) FROM GEOSYSTEMS

Pre-seismic electromagnetic disturbances in frequency bands

ULF emissions

Beginning from 1964 [85], seismogenic ULF (Ultra Low Frequency) electromagnetic emissions were reported at frequencies lower than 10 Hz [3].Although high frequency components cannot propagate in lithosphere over long distances due to severe attenuation, ULF waves can propagate up to an observation point near the Earth’s surface with small attenuation [3].ULF electromagnetic noise in the atmosphere, variations of ground electric potential and other known phenomena are found to take place before earthquake occurrences [40,41,46,47,75,76,86,87].ULF precursors are mainly electric, however, several studies have investigated magnetic ULF precursors as well [8,86]. Worth to mention is however that there have been published some controversial reports as well regarding earthquake-related ULF-range signals [86].

Regarding the electric ULF precursors, the so-called VAN-method of measuring Seismic Electric Signals (SES) at some days or weeks before earthquake occurrences has been used in Greece [75,76] and Japan [3,88]for earthquake forecasting for more than 20 years. SES are ULF (< 1 Hz) signals. Selectivity is one of the most important SES physical properties [86,89], which refers to the experimental fact that a (sensitive) monitoring station is capable to detect SES only from a restricted number of seismic areas. This means that a certain site is sensitive only to SES from some specific focal area(s). These properties can not be explained by a homogeneous medium [56,58,90]. A map showing the seismic areas that emit SES detectable (for earthquakes above a magnitude threshold) at a given station is called “selectivity map of this station” [88,91]. The remarkable property of SES is that it can be recorded at sensitive sites which are a hundred or more kilometres from the epicenter. Varotsos and Lazaridou [89]published four criteria according to which true SES can be discriminated from magnetotelluric (MT) variations and from anthropogenic disturbances. The application of these criteria requires the simultaneous operation of short electric dipoles (e.g. with lengths L lying between 50m and 200m) and long dipoles. These allow discrimination of true SES from artificial signals emitted from distances of the order of several kilometres.

The empirical dependence of SES amplitude E (mV/m) on earthquake magnitude M looks as [90]where a and b are empirical constants. The value of b depends on the azimuth of epicentre reckoned from observation station and the ‘‘sensitivity’’ of station. In other words, the parameter b is not universal. Discussion on the VAN-method has divided the scientific community into two: one supporting [87] it and the other rejecting [8].

Either for SES or magnetic pre-seismic ULF signals, three are the mechanisms that have been proposed as potential models, summarized by Cicerone et al. [8] as follows::

(a) The first mechanism is the so-called magneto-hydrodynamic (MHD) effect [92]. For this mechanism, the flow of an electrically conducting fluid in the presence of a magnetic field generates a secondary induced field. The MHD equation is derived from Maxwell’s equations.The induced magnetic field B_i is given by where is a magnetic Reynolds number analogous to the hydrodynamic Reynolds number, the latter defining the relative importance of the convective and diffusive terms, while B is the primary magnetic field.

(b) The second mechanism is the so-called piezomagnetic effect [93]. For this mechanism, a secondary magnetic field is induced due to a change in magnetization in ferromagnetic rocks in response to an applied stress.

(c) The third mechanism is the electrokinetic effect [94,95]. The electrokinetic effect results from the flow of electric currents in the earth, in the presence of an electrified interface at solid–liquid boundaries. These electric currents in turn produce magnetic fields.

More specifically, hypotheses of piezo-stimulated current and current generated by charged dislocations have been proposed by Varotsos et al. [96]. Some theories are based on the electrokinetic hypothesis [97]. The electrokinetic currents can be observed in water-saturated media with fluid-filled channels [98.99]. The walls of pores and cracks in a solid body generally adsorb cations from the liquid. Moving along the channel, the liquid carries ions of opposite sign, and thus produces an extrinsic electric current, Surkov et al.[100],in order to model electrokinetic current parameters, supposed that an earthquake hypocenter is surrounded by water-saturated porous rocks with fluid-filled pore channels. The pre-earthquake stage is accompanied by appearances of a number of fresh cracks in the vicinity of hypocenter. Such a zone is called fracture zone. The scale of the fracture zone may be varied from hundreds of metres up to several kilometres. Feder[101] assumed that the pore space in the fracture zone exhibits fractal structure. Apparently, most of the fresh cracks are closed when formed. Because of the pressure release due to cracking, they are under lower pressure, so that water from uncracked outer region can penetrate into them as soon as a network of connected channels or fractal clusters is formed. The closed fresh cracks may be regarded as the sink of water from surrounding higher pressure areas. Surkov et al. [100]supposed that the porosity n and permeability of rocks, after the cluster formation, decreases from the center of the fracture zone towards the periphery by a certain law. The percolation threshold is exceeded in the internal area with typical size L. It means that the permeability tends to zero outside this zone. Actually, there is a finite permeability due to the fact that crustal rocks contain a wide range of small cracks that can be connected. Further there is interest in conductivity of the rock rather than its permeability. The conductivity of the surrounding space is also non-zero due to both the bulk and surface conductivities of the small fluid-filled cracks and conductivity of the rocks itself. Surkov et al. [100] supposed that these conductivities can be neglected in comparison with that of the fluid-filled cracks, which are formed in the fracture zone, i.e. the conductivity outside the fracture zone is negligible. It means that the value is rather related to the percolation threshold for conductivity due to the fresh fluid-filled cracks. It should be emphasized that a variety of the crack sizes can be described only in the framework of rather complicated percolation theory. Surkov et al. [100] restricted the analysis by a simple percolation theory without of account of the crack/channel size distribution. Then the fractal properties near the threshold can be determined [101].

HF- emissions

KHz band

A way to investigate transient phenomena is to analyze a sequence of distinct time windows of short duration into the detected pre-seismic time series. The aim is to discover a clear difference of dynamical characteristics as the catastrophic event is approaching. In order to develop a quantitative identification of kHz EM precursors, the concepts of entropy and tools from information theory are used in order to identify statistical patterns [4,12-31]. It is expected that a significant change of a statistical pattern represents a deviation from normal behavior, revealing the presence of an anomaly. Symbolic dynamics provide a rigorous way of looking at "real" dynamics. First, a symbolic analysis [4,22,23,26,28,102-105] of experimental data is attempted, in terms of Shannon n-block entropy, Shannon n-block entropy per letter, conditional entropy, entropy of the source and T-entropy. It is well-known that Shannon entropy works best in dealing with systems composed of subsystems, which can access all the available phase space and which are either independent or interact via short-range forces. However, a central property of the earthquake preparation process is the possible occurrence of coherent large-scale collective behavior with a very rich structure, resulting from repeated non-linear interactions among the constituents of the system. Consequently, non-extensive Tsallis entropy is an appropriate tool for investigating the launch of a kHz EM precursors [21,22,31,66]. It has been shown [4,16,19,22,55] that the techniques based on critical dynamics discriminate also clearly the recorded kHz EM anomalies from the background: they are characterized by a significantly lower complexity (or higher organization). The analysis with Approximate Entropy verified the results of symbolic dynamics [21,22,67]. On the other hand, the fractal spectral analysis [4,13,14,17,20,21,25,27-30,48,49,70,106] offers additional information concerning signal/noise discrimination mainly due to two facts. First, it shows that the candidate kHz precursor follows the fractional Brownian motion (fBm)-model while, on the contrary, the background follows the 1/f-noise model. Second, it implies that the candidate kHz precursor has persistent behavior [4,13,14,17,20,21]. The existence of persistency in the candidate precursor is confirmed by R/S analysis, while the conclusion that the anomaly follows the persistent fBm-model is verified by Detrended Fluctuation Analysis.

The abrupt simultaneous appearance of both high organization and persistency in a launched kHz anomaly implies that the underlying fracto-electromagnetic process is governed by a positive feedback mechanism [4,13,14,17,20,21,25,27-30]. Such a mechanism is consistent with the anomaly's being a candidate precursor. Of course, such an analysis cannot establish, independently, the precursory value of a certain anomaly. Much remains to be done to tackle precursors systematically. It is a difficult task to rebate two events separated in time, such as a candidate kHz EM precursor and the ensuing earthquake. It remains to be established whether different approaches could provide additional information that would allow one to accept the seismogenic origin of the recorded kHz EM anomalies and link these to a crucial stage of earthquake generation, i.e., the kHz EM anomalies are associated with the fracture of asperities that are distributed along the fault sustaining the system.

MHz band

It has been shown that the MHz EM precursors present strong anti-persistent behavior [4,10-26]. This behaviour indicates an underlying non-linear feedback of the system that "kicks" the crack-opening rate away from extremes [20]. This anti-persistent behavior is similar to the one found in systems which undergo a continuous phase transition at equilibrium [4,16,19,22,55]. Heterogeneity could account for the appearance of a stationary-like behavior in the anti-persistent part of the pre-fracture MHz EM time series. A published statistical method of analysis of critical fluctuations has shown that the detected precursory MHz anomalies, could be described in analogy to a continuous thermal phase transition. More specifically, it has been shown [4,16,19,22,55] that an underlying strong critical behavior is consistent to a criterion: the majority of trajectories in the properly defined laminar region carry out information about the underlying criticality. The MHz EM precursors follow this criterion.

A thermal second-order phase transition is associated with a "symmetry breaking" [4,16,19,22,55] . To gain insight into the catastrophic character of the fracture phenomena, the evolution of the "symmetry breaking" with time has been elucidated for non-equilibrium-irreversible processes. The analysis showed that the system is gradually driven out of equilibrium. Through this the time was estimated beyond which the process which generates the pre-seismic MHz EM emission could continue only as non-equilibrium instability. More precisely, the analysis revealed the following significant issues: (i) The critical epoch (critical window) during which the short-range correlations evolve to long-range, (ii) The epoch of the "symmetry breaking" occurrence, (iii) The integration of the "symmetry breaking". It is generally accepted that the terminal phase of the earthquake preparation process is accompanied by significant increase in localization and directionality. It is hence important to distinguish characteristic epochs in the evolution of precursory MHz EM activity and to link these to the equivalent last stages in the earthquake preparation process. Tracing of "symmetry-breaking" may signalize that the micro-fracture propagation has finished in the heterogeneous component of the focal area, which surrounds the backbone of the strong asperities on the fault plane: the rupture has been obstructed at the boundary of the backbone of strong asperities: The "siege" of asperities has already been started [20].

It is important to mention that MHz radiation precedes kHz both at the large (geophysical) and at the small (laboratory) scale [4,10-26]. Attention should be given to the fact that the time lags between the pre-earthquake EM anomalies and the impeding earthquakes are different among the MHz and the kHz precursors. This remarkable asynchronous emergence of the MHz and the kHz precursors indicates that they refer to different stages of the earthquake preparation process [4]. A significant issue for science is to attempt associations between the numerous detectable EM observations, that appear one after the other, to the consecutive processes within the Earth's crust [4,10-26].

It has been clarified that the emergence of a MHz EM anomaly is a necessary but not a sufficient condition for the earthquake occurrence [4,10-26]. Indeed, although numerous MHz EM anomalies have been detected with clear strong, critical and anti-persistent behavior, these were not combined with the occurrence of a significant earthquake. Noticeably is that any possible relations of these anomalies should be excluded if associated to magnetic storm activity, solar flare activity, or, man-made electromagnetic sources.

PART 3: RADON EMISSIONS FROM GEOSYSTEM

Radon (²²² Rn) is a naturally occurring radioactive noble gas directly produced by the radioactive decay of ²²⁶ Ra. ²²² Rn, as its parent nucleus, is present in soil, rocks, building materials, underground and surface waters (Nazaroff and Nero, 1988; UNSCEAR, 2005). Whereas in fluids all generated radon atoms are diluted, in porous media and fragmented rock only a percentage of radon emanates, enters pore’s volume and dissolves into pore's fluid (Nazaroff and Nero, 1988; Nikolopoulos and Louizi, 2008). Once there, a macroscopic transport is possible, either by molecular diffusion or convection (Khayrat et al., 2001). This transport can be implemented through interconnected pores and aquifers (Ghosh et al., 2009; Hakl et al., 2003; Soonawala and Telford, 1980). When the pores are saturated, radon is dissolved in water and is transported by it (Andrews and Wood, 1972). This radon's transportation can be implemented also through fluid flow (Ghosh et al., 2009). Through these processes radon can travel to short, medium or long distances reaching water aquifers and air (Nikolopoulos et al., 2012) . It finally dilutes to atmosphere directly from soil or through release from its aqueous phase (Nazaroff and Nero, 1988; Nikolopoulos and Louizi, 2008). Various factors affect the whole process, such as soil's permeability, temperature gradients and pressure differences (Nazaroff and Nero, 1988; UNSCEAR, 2005) . Radon is very important from radiological point of view, since it accounts for more than half of the natural exposure of the general public. It is well known that among natural radioactivity (not man-made), the most dominant component is radon and, therefore, it is the major contributor to the effective dose equivalent.

Radon has been used as trace gas in several studies of Earth, hydro-geology and atmosphere, because of its ability to travel to comparatively long distances from its parent mineral and the efficiency of detecting it at very low levels (Richon et al., 2007) . Noteworthy variations of radon and progeny have been observed in geothermal fields (e.g. Whitehead et al., 2007), thermal spas (e.g. Nikolopoulos and Vogiannis, 2008), active faults (e.g. Al-Tamimi and Abumura, 2001; King, 1985, 1978; Tansi et al., 2005; Walia et al., 2009), soil experiments (e.g. Zafrir et al., 2009), volcanic processes (e.g Immè et al., 2006, 2005; Morelli et al., 2006) and seismotectonic environments(e.g. Cicerone et al., 2009; Chyi et al., 2005; Ghosh et al., 2009; Ghosh et al., 2012, Kuo et al., 2009; Majumdar, 2004; Nikolopoulos et al., 2012; Petraki et al., 2013,.2013b; Singh M et al.,1991; Singh S. et al., 2010; Zafrir et al., 2009). Due to its importance, radon monitoring has become a continuously growing study area in the search of premonitory signals prior to earthquakes. Anomalous radon variations have been observed prior to earthquakes in groundwater, soil gas, atmosphere and thermal spas (Cicerone et al., 2009; Choubey et al., 2009; Chyi et al. 2005, Erees et al., 2007; Ghosh et al., 2009; Singh M. et al.1991; Singh S. et al. 2010; Yasuoka et al. 2006). Seismological data have shown that radon concentrations are characterised by wide fluctuations, peaks and downturns (Ghosh et al., 2009). Connections have also been attempted between earthquake- relating parameters (e.g. magnitude, precursory time, epicentral distance) and time-series characteristics (e.g. range, duration, number of radon anomalies) (Cicerone et al., 2009; Ghosh et al., 2009; MorgoCampero and Fleischer 1979; Rikitake, 1987).Remarkable are however the divergences in the published results. For example, reported p recursory times range from 3 months to some days prior to the earthquake event, whilst epicentral distances, between 10 and 100 km (please see e.g. Cicerone et al., 2009 and Ghosh et al., 2009 and references therein) . It is also noteworthy that, most precursory signals are derived with passive techniques which integrate radon concentrations over long time intervals (at least >1-4 weeks), i.e., they provide coarse time-series estimations. Limited are the reported precursory signals with a ctive techniques which enable high radon detection rates (between 1 and 1/60) and provide fine radon signals (Cicerone et al., 2009 ; Ghosh et al., 2009; Richon et al, 2008; Walia et al, 2009) Further parameters affect radon-earthquake estimations. For instance, radon concentration levels are affected by geological and geophysical conditions, seasonal variations and atmospheric changes such as rainfall and barometric pressure alterations (please see e.g. Nazaroff and Nero, 1988; UNSCEAR, 2005). For this reason such time-series data are presented in parallel to radon precursory signals ( Cicerone et al., 2009 ; Ghosh et al., 2009) . Still more, most radon-earthquake associations are based on earthquakes of small or intermediate magnitudes. This restricts the estimations more, since, up-to-date there seems not to exist, not only for the mild, but even for the intense earthquakes, a universal model to serve as a signature of a specific forthcoming seismic event (Eftaxias et al., 2010;2009a;2009b;2008;2007).

Regarding modelling, available m odels propose explanations in terms of strain changes within the earth's crust during preparation of earthquakes (Dobrovolsky et al., 1979; Ghosh et al., 2009; MorgoCampero and Fleischer 1979; Rikitake, 1987). It is the displacement of rock mass under tectonic stress that opens up various pathways and exposes new surfaces when cracks open. The stress-strain developed within the earth's crust before earthquakes leads to changes in gas transportation from the deep earth to surface (Thomas, 1988). As a result, unusual quantities of radon emerge out of the pores and fractures of the rocks on the surface. Due to the seismic activity, changes in underground fluid flow may also render anomalous changes in concentration of radon and its progeny (Steinitz et al., 2006). Under the so called compression model, small changes in velocity of gas into or out of the ground, causes signi ficant changes in radon concentration at shallow depths, as changes in gas flow disturb the strong radon concentration gradient that exists between soil and atmosphere. A slight compression of pore's volume, causes gas to flow out of soil resulting to increase of radon levels. Similarly, when pore's volume increases, gas flows into soil from atmosphere. Thus, increased radon concentration occurs in the region of compression and radon concentration decreases in the region of dilation. As small changes in gas flow velocity causes signifi cant change in radon concentration, soil radon monitoring is an important way to detect the changes in compression or dilation associated with an earthquake event.

An important recent paper Ghosh et al., 2012), reported pre-earthquake fractal characteristics of a three year radon signal through the method of multifractal detrended fluctuation analysis. Two other recent reports revealed fractal self organised critical (SOC) characteristics of radon disorders prior to significant earthquakes in Greece(Nikolopoulos et al., 2012; Petraki et al., 2013, Nikolopoulos et al., 2014;Nikolopoulos et al., 2015). The methods of wavelet spectral fractal analysis and detrended fluctuation analysis showed that these disorders exhibited self-affine persistent-antipersistent behaviour similar to those of the pre-seismic electromagnetic disturbances of the MHz range. These methods, on the other hand, did not trace any fractal patterns in radon background. Antipersistent chaotic regimes have also been observed in variations of radon in atmosphere(Radolic et al., 2005)  and soil(Planinic et al., 2004). Another recent paper(Petraki et al., 2013b), revealed pre-earthquake features hidden in radon time-series through the methods of rescaled-range, roughness-length, variogram, fractal dimension and block entropy.

According toHayakawa and Hobara(2010), radon can be considered as a short-term earthquake precursor. Nevertheless, no universal model exists to serve as pre-earthquake signature(Eftaxias, 2010,Eftaxias et al., 2008). Moreover, there is no definite rule to link any kind of pre-earthquake anomaly to a specific forthcoming seismic event, either if this is intense or mild (Eftaxias, 2010; Eftaxias et al., 2008). For these reasons, despite the fairly abundant circumstantial evidence, the scientific community still debates the precursory value of premonitory anomalies detected prior to earthquakes(Eftaxias, 2010). On the other hand, well established criteria exist to identify pre-earthquake patterns hidden in time-series, which are based on the concepts of fractality, self-organisation, non-extensivity and entropy(Eftaxias, 2010; Eftaxias et al., 2009; Eftaxias et al., 2008; Eftaxias et al., 2007). Especially according to Eftaxias, 2010, certain questions still remain: (i) How can a certain observation be recognised as pre-seismic? (ii) How can an individual precursor be linked to a distinctive stage of an earthquake preparation process? (iii) How can certain precursory symptoms in anomalous observations be identified so as to indicate that the occurrence of an earthquake is unavoidable? The above issues clearly indicate that radon monitoring in soil is very important from geological point of view.

REFERENCES

PART 1

[1] Keilis-Borok VI, Soloviev AA (2003) Nonlinear Dynamics of the Lithosphere and Earthquake Prediction, Springer, Heidelberg, 348 p.

[2] Keilis-Borok V (2002) Εarthquake Prediction: State-of-the-Art and Emerging Possibilities. Annual Review of Earth and Planetary Sciences 30:1–33 .

[3] Hayakawa M, Hobara Y (2010) Current status of seismo-electromagnetics for short-term earthquake prediction. Geomatics, Natural Hazards and Risk1:115-155.

[4] Eftaxias K (2010) Footprints of non-extensive Tsallis statistics, self-affinity and universality in the preparation of the L'Aquila earthquake hidden in a pre-seismic EM emission.Physica A 389: 133-140 .

[5] Nikolopoulos D, Petraki E, Marousaki A, Potirakis S, Koulouras G, et al. (2012) Environmental monitoring of radon in soil during a very seismically active period occurred in South West Greece. Journal of Environmental Monitoring, 14: 564-578.

[6] Molchanov A, Kopytenko A, Voronov M, Kopytenko A, Matiashviali G, et al. (1992) Results of ULF magnetic field measurements near the epicenters of the Spitak (Ms= 6.9) and Loma-Prieta (Ms= 7.1) earthquakes: comparative analysis. Geophysical Research Letters 19: 1495-1498.

[7] Aggarwal P, Sykes R, Simpson W, Richards G (1975) Spatial and temporal variations in ts/tp and in P wave residuals at Blue Mountain Lake, New York: Application to earthquake prediction. Journal of Geophysical Research 80:718-732.

[8] Cicerone R, Ebel J, Britton J (2009) A systematic compilation of earthquake precursors, Tectonophysics 476: 371-396.

[9] Parvaiz K, Sharad T, Azad M, Purushottam B, Purohit P, et al. Scientific efforts in the direction of successful earthquake prediction, International Journal of Geomatics and Geosciences 1: 669-677 .

[10] Eftaxias K, Kapiris P, Polygiannakis J, Bogris N, Kopanas J, et al. (2001) Signature of pending earthquake from electromagnetic anomalies. Geophysical Research Letters 29: 3321–3324.

[11] Hadjicontis V, Mavromatou C, Eftaxias K (2002) Preseismic earth’s field anomalies recorded at Lesvos station, north-eastern Aegean. Acta Geophysica Polonica 50: 151-158.

[12] Eftaxias K, Kapiris P, Dologlou E, Kopanas J, Bogris N, et al. (2002) EM anomalies before the Kozani earthquake: a study of their behavior through laboratory experiments. Geophysical Research Letters 29: 69-1-69-4.

[13] Kapiris P, Polygiannakis J, Peratzakis A, Nomicos K, Eftaxias K (2002) VHF-electromagnetic evidence of the underlying pre-seismic critical stage. Earth Planets Space, 54: 1237–1246.

[14] Kapiris P, Eftaxias K, Nomikos K, Polygiannakis J, Dologlou E, et al. (2003) Evolving towards a critical point: A possible electromagnetic way in which the critical regime is reached as the rupture approaches. Nonlinear Processes in Geophysics 10: 511–524.

[15] Kapiris P, Eftaxias K, Chelidze T (2004) Electromagnetic Signature of Prefracture Criticality in Heterogeneous Media. Physical Review Letters 92: 065702-1/065702-4.

[16] Contoyiannis Y, Kapiris P, Eftaxias K (2005) Monitoring of a preseismic phase from its electromagnetic precursors. Physical Review E 71: 066123(1-14).

[17] Eftaxias K, Kapiris P, Balasis G, Peratzakis A, Karamanos K, et al. (2006) Unified approach to catastrophic events: from the normal state to geological or biological shock in terms of spectral fractal and nonlinear analysis. Natural Hazards and Earth System Sciences 6: 205-228.

[18] Eftaxias K, Panin V, Deryugin Y (2007) Evolution-EM signals before earthquakes in terms of meso-mechanics and complexity. Tectonophysics 431: 273–300.

[19] Contoyiannis Y, Eftaxias K (2008) Tsallis and Levy statistics in the preparation of an earthquake. Nonlinear Processes in Geophysics 15: 379–388.

[20] Eftaxias K, Contoyiannis Y, Balasis G, Karamanos K, Kopanas J, et al. (2008) Evidence of fractional-Brownian-motion-type asperity model for earthquake generation in candidate pre-seismic electromagnetic emissions. Natural Hazards and Earth System Sciences 8: 657-669.

[21] Eftaxias K, Athanasopoulou L, Balasis G, Kalimeri M, Nikolopoulos S, et al. (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.Natural Hazards and Earth System Sciences 9: 1953-1971.

[22] Eftaxias K, Balasis G, Contoyiannis Y, Papadimitriou C, Kalimeri M, et al.(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. Natural Hazards and Earth System Sciences, 10: 275–294.

[23] Minadakis G, Potirakis S, Nomicos C, Eftaxias K (2012) Linking electromagnetic precursors with earthquake dynamics: An approach based on non-extensive fragment and self-affine asperity models. Physica A 391: 2232–2244.

[24] Potirakis S, Minadakis G, Eftaxias K (2012) Analysis of electromagnetic pre-seismic emissions using Fisher information and Tsallis entropy. Physica A 391: 300-306.

[25] Balasis G, Mandea M (2007) Can electromagnetic disturbances related to the recent great earthquakes be detected by satellite magnetometers? Tectonophysics 431: 173–195.

[26] Balasis G , Daglis I , Papadimitriou C , Kalimeri M , Anastasiadis A , et al. (2008) Dynamical complexity in D_st time series using non-extensive Tsallis entropy. Geophysical Research Letters 35: L14102 (1-6).

[27] Petraki E, Nikolopoulos D, Fotopoulos A, Panagiotaras D, Koulouras G, et al. (2013) Self-organised critical features in soil radon and MHz electromagnetic disturbances: Results from environmental monitoring in Greece. Applied Radiation and Isotopes 72: 39–53.

[28] Petraki E, Nikolopoulos D, Fotopoulos A, Panagiotaras D, Nomicos C, et al. (2013) Long-range memory patterns in variations of environmental radon in soil. Analytical Methods 5: 4010-4020.

[29] Nikolopoulos D, Petraki E, Vogiannis E, Chaldeos Y, Giannakopoulos P, et al. (2014) Traces of self-organisation and long-range memory in variations of environmental radon in soil: Comparative results from monitoring in Lesvos Island and Ileia (Greece).Journal of Radioanalytical and Nuclear Chemistry 299: 203-219.

[30] Petraki E, Nikolopoulos D, Chaldeos Y, Coulouras G, Nomicos C, et al. (2014) Fractal evolution of MHz electromagnetic signals prior to earthquakes: results collected in Greece during 2009 . Geomatics, Natural Hazards and Risk (in press).

[31] Kalimeri M, Papadimitriou C, Balasis G, Eftaxias K (2008) Dynamical complexity detection in pre-seismic emissions using non-additive Tsallis entropy.Physica A 387: 1161-1172.

[32] Varotsos P, Sarlis N, Skordas E (2011) Scale-specific order parameter fluctuations of seismicity in natural time before main shocks.EPL(Europhysics Letters) 96 : 59002-p1/59002-p6.

[33] Sarlis N, Skordas E, Varotsos P, Nagao T, Kamogawa M, et al. (2013) Minimum of the order parameter fluctuations of seismicity before major earthquakes in Japan. Proceedings of the National Academy of the United States of America 110: 13734-13738.

[34] Warwick J, Stoker C, Meyer T (1982) Radio Emission Associated With Rock Fracture: Possible Application to the Great Chilean Earthquake of May 22, 1960. Journal of Geophysical Research 87: 2851-2859.

[35] Smith B, Johnston M (1976) A tectonomagnetic effect observed before a magnitude 5.2 earthquake near Hollister, California. Journal of Geophysical Research 81: 3556-3560.

[36] ZhaoY, Qian F (1994) Geoelectric precursors to strong earthquakes in China. Tectonophysics 233: 99-113.

[37] Wallace R, Teng T (1980) Prediction of the Sungpan-Pingwu earthquakes, 1976.Bulletin of the Seismological Society of America 70: 1199-1223.

[38] Gokhberg M, Morgounov V, Yoshino T, Tomizawa I (1982) Experimental Measurement of Electromagnetic Emissions Possibly Related to Earthquakes in Japan. Journal of Geophysical Research 87: 7824-7828.

[39] Gershenzon N, Gokhberg M (1993) On the origin of electrotelluric disturbances prior to an earthquake in Kalamata, Greece. Tectonophysics 224: 169-174.

[40] Kopytenko Y, Matiashviali T, Voronov P, Kopytenko E, Molchanov O (1993) Detection of ultra-low-frequency emissions connected with the Spitak earthquake and its aftershock activity, based on geomagnetic pulsations data at Dusheti and Vardzia observatories. Physics of the Earth and Planetary Interiors 77: 85-95.

[41] Fraser-SmithA , Bernardi A, McGill P, Ladd M, Helliwell R, Viilard O (1990) Low-frequency magnetic field measurements near the epicenter of the Ms 7.1 Loma Prieta earthquake. Geophysical Research Letters 17: 1465-1468.

[42] Serebryakova O, Bilichenko S, Chmyrev V, Parrot M, Rauch J, et al. (1992) Electromagnetic ELF radiation from earthquake regions as observed by low-attitude satellites.Geophysical Research Letters, 19: 91-94.

[43] Dea J, Hansen P, Boerner W (1993) Long-term EMF background noise measurements, the existence of window regions and applications to earthquake precursor emission studies. Physics of the Earth and Planetary Interiors 77: 109-125.

[44] Shalimov S, Gokhberg M (1998) Lithosphere-ionosphere coupling mechanism and its application to the earthquake in Iran on June 20, 1990.A review of ionospheric measurements and basic assumptions. Physics of the Earth and Planetary Interiors 105: 211–218.

[45] Ondoh T (1998) Ionospheric disturbances associated with great earthquake of Hokkaido southwest coast, Japan of July 12, 1993. Physics of the Earth and Planetary Interiors 105: 261-269.

[46] Hayakawa M, Kawate R, Molchanov O, Yumoto K (1996) Results of ultra-low-frequency magnetic field measurements during the Guam earthquake of 8 August 1993.Geophysical Research Letters 23: 241-244.

[47] Hayakawa M, Ito T (1999) Fractal analysis of ULF geomagnetic data associated with the Guam earthquake on August 8, 1993.Geophysical Research Letters 26: 2797-2800.

[48] Smirnova, N, Hayakawa M (2007) Fractal characteristics of the ground-observed ULF emissions in relation to geomagnetic and seismic activities. Journal of Atmospheric and Solar-Terrestrial Physics 69: 1833–1841.

[49] Hayakawa M, Ida Y, Gotoh K (2005) Multifractal analysis for the ULF geomagnetic data during the Guam earthquake. Electromagnetic Compatibility and Electromagnetic Ecology, 2005.Proccedings of the IEEE 6^th International Symposium on 21-24 June 2005:239 – 243.

[50] Fujinawa Y, Takahashi K (1998) Electromagnetic radiations associated with major earthquakes. Physics of the Earth and Planetary Interiors 105: 249-259.

[51] Enomoto Y, Tsutsumi A, Fujinawa Y, Kasahara M, Hashimoto H (1997) Candidate precursors: pulse-like geoelectric signals possibly related to recent seismic activity in Japan. Geophysical Journal International 131: 485-494.

[52] Maeda K, Tokimasa N (1996) Decametric radiation at the time of the Hyogo-ken Nanbu earthquake near Kobe in 1995.Geophysical Research Letters 23: 2433-2436.

[53] Bernard P, Pinettes P, Hatzidimitriou P, Scordilis E, Veis G, et al. (1997) From precursors to prediction: a few recent cases from Greece. Geophysical Journal International 131: 467-477.

[55] Contoyiannis Y, Diakonos F., Kapiris P, Peratzakis A, Eftaxias K (2004) Intermittent dynamics of critical pre-seismic electromagnetic fluctuations. Physics and Chemistry of the Earth 29:397-408.

[56] Varotsos P, Sarlis N, Eftaxias K, Lazaridou M, Bogris N, et al. (1999) Prediction of the 6.6 Grevena-Kozani Earthquake of May 13, 1995. Physics and Chemistry of the Earth 24: 115-121.

[57] Varotsos P, Sarlis N, Skordas E (2003) Electric Fields that ‘‘Arrive’’ before the Time Derivative of the Magnetic Field prior to Major Earthquakes. Physical Review Letters 91:148501-1/148501-4.

[58] Varotsos P, Sarlis N, Skordas E, Lazaridou M (2007) Electric pulses some minutes before earthquake occurrences. Applied Physics Letters 90: 064104-1/064104-3.

[59] Hayakawa M, Ohta K, Maekawa S, Yamauchi T, Ida Y, et al. (2006) Electromagnetic precursors to the 2004 Mid Niigata Prefecture earthquake. Physics and Chemistry of the Earth 31: 351-364.

[60] Yonaiguchi N, Ida Y, Hayakawa M, Masuda S (2007) Fractal analysis for VHF electromagnetic noises and the identification of preseismic signature of an earthquake. Journal of Atmospheric and Solar-Terrestrial Physics 69: 1825-1832.

[61] Biagi P, Ermini A, Kingsley S (2001) Disturbances in LF Radio Signals and the Umbria-Marche (Italy) Seismic Sequence in 1997-1998. Physics and Chemistry of the Earth (C) 26:755-759.

[62] Karakelian D, Klemperer S, Fraser-Smith A, Thompson G (2002) Ultra-low frequency electromagnetic measurements associated with the 1998 Mw 5.1 San Juan Bautista, California earthquake and implications for mechanisms of electromagnetic earthquake precursors. Tectonophysics 359:65–79.

[63] Nikolopoulos S, Kapiris P, Karamanos K, Eftaxias K (2004) A unified approach of catastrophic events. Natural Hazards and Earth System Sciences 4: 615–631.

[64] Kapiris P, Nomicos K, Antonopoulos G, Polygiannakis J, Karamanos K, et al. (2005) Distinguished seismological and electromagnetic features of the impending global failure: Did the 7/9/1999 M5.9 Athens earthquake come with a warning? Earth Planets Space 57: 215–230.

[65] Karamanos K, Peratzakis A, Kapiris P, Nikolopoulos S, Kopanas J, et al. (2005) Extracting preseismic electromagnetic signatures in terms of symbolic dynamics. Nonlinear Processes in Geophysics 12: 835–848.

[66] Potirakis S, Minadakis G, Nomicos C, Eftaxias K (2011) A multidisciplinary analysis for traces of the last state of earthquake generation in preseismic electromagnetic emissions. Natural Hazards and Earth System Science 11: 2859–2879.

[67] Potirakis S, Minadakis G, Eftaxias K (2013) Relation between seismicity and pre-earthquake electromagnetic emissions in terms of energy, information and entropy content. Natural Hazards and Earth System Sciences 12: 1179-1183.

[68] Chuo Y, Liu J, Pulinets S, Chen Y (2002) The ionospheric perturbations prior to the Chi-Chi and Chia-Yi earthquakes. Journal of Geodynamics 33: 509–517.

[69] Ruzhin Y, Nomicos C (2005) Radio VHF precursors of earthquakes. Natural Hazards 40: 573-583.

[70] Gotoh K, Hayakawa M, Smirnova N, Hattori K (2004) Fractal analysis of seismogenic ULF emissions. Physics and Chemistry of the Earth 29: 419–424.

[71] Smirnova N, Hayakawa M, Gotoh K (2004) Precursory behavior of fractal characteristics of the ULF electromagnetic fields in seismic active zones before strong earthquakes. Physics and Chemistry of the Earth 29: 445–451.

[72] Saroso S, Hattori K, Ishikawa H, Ida Y, Shirogane R, et al. (2009) ULF geomagnetic anomalous changes possibly associated with 2004–2005 Sumatra earthquakes. Physics and Chemistry of the Earth 34:343-349.

[73] Parrot M (1994) Statistical study of ELFNLF emissions recorded by a low-altitude satellite during seismic events. Journal of Geophysical Research 99: 23-339-23.

[74] Yoshino T, Tomizawa I, Sugimoto T (1992) Results of statistical analysis of low-frequency seismogenic EM emissions as precursors to earthquakes and volcanic eruptions. Physics of the Earth and Planetary Interiors 77:21-31.

[75] Varotsos P, Alexopoulos K (1984) Physical properties of the variations of the electric field of the earth preceding earthquakes, I. Tectonophysics 110: 73-98.

[76] Varotsos P, Alexopoulos K (1984) Physical properties of the variations of the electric field of the earth preceding earthquakes, II, determination of epicenter and magnitude. Tectonophysics 110:99-125.

[77] Bella F, Biagi P, Caputo M, Cozzi E, Della Monica G, et al. (1998) Field strength variations of LF radio waves prior to earthquakes in central Italy. Physics of the Earth and Planetary Interiors 105:279–286.

[78] Vallianatos F, Nomikos K (1998) Seismogenic Radio-emissions as Earthquake Precursors in Greece. Physics and Chemistry of the Earth 23 (9-10):953-957.

[79] Yépez, E, Angulo-Brown, F, Peralta, JA, Pavía CG, González-Santos G (1995). Electric field patterns as seismic precursors. Geophysical Research Letters 22(22):3087-3090

[80] Liu JY, Chen YI, Pulinets SA, Tsai YB, Chuo YJ (2000) Seismo-ionospheric signatures prior to M>6.0 Taiwan earthquakes. Geophysical Research Letters 27(19):3113-3116.

[81] Enescu BD, Enescu D, Constantin AP (1999) The use of electromagnetic data for short-term prediction of Vrancea (Romania) earthquakes: Preliminary data . Earth Planets Space 51 (10):1099–1117.

[82] Stavrakas I, Clarke M, Koulouras G, Stavrakakis G, Nomicos C (2007) Study of directivity effect on electromagnetic emissions in the HF band as earthquake precursors: Preliminary results on field observations. Tectonophysics 431 (1-4): 263–271.

[83] Fujiwara H, Kamogawa M, Ikeda M., Liu JY, Sakata H, et al. (2004) Atmospheric anomalies observed during earthquake occurrencesGeophysical Research Letters 31 (17): L17110, 1-4.

[84] Varotsos PA (2006) Recent Seismic Electric Signals (SES) activities in Greece. Acta Geophysica 54 (2):158-164.

[85] Moore GW (1964) Magnetic disturbances preceding the 1964 Alaska earthquake. Nature 203:508-509.

[86] Shrivastava A (2014) Are pre-seismic ULF electromagnetic emissions considered as a reliable diagnostics for earthquake prediction? Current Science 107(4): 596-60.

[87] Uyeda S (2000) In defense of VAN’s earthquake predictions, Eos, Transactions, American Geophysical Union 81:3-6.

[88] Uyeda S, Nagao T., Kamogawa M (2009) Short-term earthquake prediction: current status of seismo-electromagnetics. Tectonophysics 470:205–213.

[89] P. Varotsos and M. Lazaridou, Latest aspects of earthquake prediction in Greece based on Seismic Electric Signals. I, Tectonophysics 188, 321−347, 1991.

[90] Varotsos P, Sarlis N, Lazaridou M, Bogris N (1996) Statistical evaluation of earthquake prediction results.Comments on the success rate and alarm rate. Acta Geophysica Polonica 44( 4): 329-347.

[91] Varotsos P, Alexopoulos K, Lazaridou M (1993) Latest aspects of earthquake prediction in Greece based on Seismic Electric Signals II. Tectonophysics 224:1-37.

[92] Draganov AB, Inan US, Taranenko Yu N (1991) ULF magnetic signatures at the Earth's surface due to ground water flow: a possible precursor to earthquakes. Geophysical Research Letters 18:1127–1130.

[93] Sasai Y (1991) Tectonomagnetic modeling on the basis of the linear piezomagnetic effect. Bulletin of the Earthquake Research Institute The Univeristy of Tokyo 66:585–722.

[94] Fitterman DV (1978) Electrokinetic and magnetic anomalies associated with dilatant regions in a layered Earth. Journal of Geophysical Research 83:5923–5928.

[95] Fitterman DV (1979) Theory of electrokinetic–magnetic anomalies in a faulted half-space. J. Geophys. Res. 84, 6031–6040.

[96] Varotsos P, Alexopoulos K, Nomicos K, Lazaridou M (1986) Earthquake prediction and electric signals, Nature 322:120.

[97] Dobrovolsky IP, Zubkov SI, Miachkin VI (1979) Estimation of the Size of Earthquake Preparation Zones, Pure and Applied Geophysics 117:1025-1044

[98] MizutaniH, Ishido T (1976) A new interpretation of magnetic field variation associated with the Matsushiro earthquakes. Journal of Geomagnetism and Geoelectricity 28:179-188.

[99] Jouniaux L, Pozzi JP (1995) Streaming potential and permeability of saturated sandstones under triaxial stress: - consequences for electrotelluric anomalies prior to earthquakes. Journal of Geophysical Research 100(B6):10197-10209.

[100] Surkov V, Uyeda S, Tanaka H, Hayakawa M (2002) Fractal properties of medium and seismoelectric phenomena. Journal of Geodynamics 33 (4-5):477-487.

[101] Feder J (1988) Fractals. DOI: 10.1007/978-1-4899-2124-6, Print ISBN: 978-1-4899-2126-0 Online ISBN: 978-1-4899-2124-6.

[102] Nicolis G, Gaspard P (1994) Toward a probabilistic approach to complex systems.Chaos, Solitons and Fractals 4 (1):41-57.

[103] Karamanos K, Nicolis G (1999) Symbolic dynamics and entropy analysis of Feigenbaum limit sets. Chaos, Solitons and Fractals 10 (7):1135-1150.

[104] Karamanos K (2001) Entropy analysis of substitutive sequences revisited. Journal of Physics A: Mathematical and General 34 (43): 9231-9241.

[105] Karamanos K, Dakopoulos D, Aloupis K, Peratzakis A, Athanasopoulou L, et al. (2006) Preseismic electromagnetic signals in terms of complexity. Physical Review E 74: 016104/1-016104/21.

[106] Smirnova N, Hayakawa M. (2007) Fractal characteristics of the ground-observed ULF emissions in relation to geomagnetic and seismic activities . Journal of Atmospheric and Solar-Terrestrial Physics 69 (15):1833-1841.

PART 2

Al-Tamimi MH, Abumura K (2001) Radiat Meas 34: 397

Barbosa S, Zafrir H, Malik U, Piatibratova O (2008) Geophys Res Lett 35: L1410235

Cicerone R, Ebel J, Britton J (2009) Tectonophysics 476: 371

Chyi L, Quick T, Yang T, Chen C (2005) TAO 16(4): 763

Choubey V, Kumar N, Arora B (2009) Sci Total Environ 407:77

Corbett D, Burnett W, Cable P, Clark S (1997) J Hydrol 203:209

Dobrovolsky I, Zubkov S, Miachkin V (1979) Pure Appl Geophys117(5): 1025

Eftaxias, K. (2010).Physica A, 389(1), 133-140.

Eftaxias, K., Contoyiannis, Y., Balasis, G., Karamanos, K., Kopanas, J., Antonopoulos, G., Koulouras, G., Nomicos, C. (2008).Natural Hazards and Earth System Sciences, 8(4), 657-669

Eftaxias, K., Athanasopoulou, L., Balasis, G., Kalimeri, M., Nikolopoulos, Contoyiannis, Y., Kopanas, J., S., Antonopoulos, G., Nomicos, C. (2009).Natural Hazards and Earth System Sciences, 9(6), 1953-1971.

Eftaxias, K., Panin, V., Deryugin, Y. (2007). Tectonophysics, 431(1-4), 273–300

Erees F, Aytas S, Sac M, Yener G, Salk M (2007) Radiat Meas42: 80

Ghosh D, Deb A, Sengupta R (2009) J Appl Geophys 187: 245

Ghosh D, Deb A, Dutta S, Sengupta R (2012) Fractals 20(01):33

Hayakawa, M. & Hobara, Y. (2010).Geomatics, Natural Hazards and Risk, 1(2), 115-155.

Imme´ G, Delf SL, Nigro SL, Morelli D, Patane´ G (2005) Ann Geophys 48(1): 65

Imme´ G, Delf SL, Nigro SL, Morelli D, Patane´ G (2006) Radiat Meas 41: 241

King C (1985) Pure Appl Geophys 122(2–4): 340

Kuo T, Lin C, Fan K, Chang G, Lewis C, Han Y, Wu Y, Chen W, Tsai C (2009) Radiat Meas 44: 295

Majumdar K (2004) Curr Sci India 86(9): 1288

Morelli D, Martino SD, Imme´ G, Delfa SL, Nigro SL, Patane´ G (2006) Radiat Meas 41: 721

Mogro-Campero A, Fleischer R (1979) Search for long-distance migration of subsurface radon. US Department of Energy, Washington

Nazaroff W, Nero A (1988) Radon and its decay products in indoor air. Wiley, New York

Nikolopoulos D, Vogiannis E (2008) Sci Total Environ 405: 36

Nikolopoulos, D., Petraki, E., Marousaki, A., Potirakis, S., Koulouras, G., Nomicos, C., Panagiotaras, D., Stonham, J. & Louizi, A. (2012) Greece. Journal of Environmental Monitoring, 14(2), 564.

Nikolopoulos, D., Petraki, E., Vogiannis, E., Chaldeos, Y., Giannakopoulos, P., Kottou, S., Nomicos, C., Stonham,J. (2014). Journal of Radioanalytical and Nuclear Chemistry, 299(1), 203-219.

Nikolopoulos D , Petraki E, Nomicos C, Koulouras G, Kottou S, Yannakopoulos PH, J Earth Sci Clim Change (2015), 6(1):1-10

Petraki, E., Nikolopoulos, D., Fotopoulos, A., Panagiotaras, D., Koulouras, G., Zisos, A., Nomicos, C., Louizi, A., Stonham, J. (2013).Applied Radiation and Isotopes, 72, 39–53.

Petraki, E., Nikolopoulos, D., Fotopoulos, A., Panagiotaras, D., Nomicos, C., Yannakopoulos, P., Kottou, S., Zisos, A., Louizi, A., Stonham, J. (2013). Analytical Methods, 5(16), 4010-4020.

Petraki, E., Nikolopoulos, D., Chaldeos, Y.,Coulouras, G., Nomicos, C., Yannakopoulos, P., Kottou, S., Stonham, J.(2014). Geomatics, Natural Hazards and Risk

Richon P, Bernard P, Labed V, Sabroux J, Beneito A, Lucius D, Abbad S, Robe M (2007) Radiat Meas 42: 87

Rikitake T (1987) Tectonophysics 136(3-4): 265

Singh M, Ramola R, Singh B, Singh S, Virk H (1991) Nucl Tracks Radiat Meas 19(1-4): 417

Singh S, Kumar A, Singh BB, Mahajan S, Kumar V, Dhar S (2010) Terr Atmos Ocean Sci 21(4): 685

Tansi C, Tallarico A, Iovine G, Gallo MF, Falcone G (2005) Tectonophysics 396: 181

Yasuoka Y, Igarashi G, Ishikawa T, Tokonami S, Shinogi M (2006) Appl Geochem 21: 1064

UNSCEAR (United Nations Scientific Committee on the Effects of Atomic Radiation)(2008) Sources and effects of ionizing radiation UNSCEAR, New York

Vaupotic´ J (2008) Sci Total Environ 393(1): 27

Walia V, Yang T, Hong W, Lin S, Fu C, Wen K, Chen C (2009) Appl Radiat Isot 67: 1855

Whitehead N, Barry B, Ditchburn R, Morris C, Stewart M (2007) J Environ Radioactiv 92(1): 16