Panayiotis A. Varotsos
Curriculum Vitae
(including the VAN earthquake prediction method)

(Last updated: 04-02-2014)


Born on November 28, 1947 at Patras, Greece. PhD in Physics, University of Athens (1973), Dozent, University of Athens (1977). Since 1986, Professor of Solid State Physics at the Department of Physics, University of Athens.

Academic Positions held- Additional Professional Experience


Main Research Interests


Main Scientific Results

These have been achieved in the following research fields:

(1) Thermodynamics

A rigorous foundation of the thermodynamic parameters that govern the formation and/or migration of defects in solids has been made in collaboration with Professor Kessar Alexopoulos and led to a model that interconnect them with bulk properties. This has been made in a series of several tens of publications mainly in Physical Review B during the '70s and the beginning of '80s. The main results have been compiled in the monograph (that was favourably reviewed in Physics Today, November 1987, pp95-96): P. Varotsos and K. Alexopoulos, Thermodynamics of Point Defects and their relation with the bulk properties (North Holland, 1986). [Times cited: several hundreds]

(2) Application of Thermodynamics and Statistical Physics to Earthquake Prediction (The VAN earthquake prediction method)

The presence of aliovalent impurities in ionic solids results in the formation of extrinsic defects (e.g. vacancies, interstitials) a significant portion of which are placed near the impurities, thus forming electric dipoles relaxation time depends on pressure (stress). Varotsos and Alexopoulos showed that when the pressure reaches a critical value, a cooperative orientation of these dipoles may occur, which results in the emission of a transient electric signal. This may happen before an earthquake (critical point) since the stress gradually increases in the focal region before the rupture. Along this direction, a detailed experimentation started in Greece in 1981, which showed (P. Varotsos and K. Alexopoulos, Tectonophysics 110, 73-98, 1984; Tectonophysics 110, 99-125, 1984) that actually transient variations of the electric field of the Earth are observed before the occurrence of earthquakes. These signals are termed Seismic Electric Signals, SES (of the so called VAN earthquake prediction method; VAN comes from the initials of Varotsos, Alexopoulos and Nomikos). The results have been published in a series of more than 130 papers during 1984-2013 in refereed journals and arouse a great interest in the international scientific community as it is evident from the following facts: (i) They have been repeatedly commented on by widespread journals like Nature, Science, Proceedings of the National Academy of Sciences USA (PNAS), Physics Today, New Scientist etc. (ii) A meeting, under the title “A critical review of VAN” was held jointly by the International Council of Scientific Unions (ICSU) and the Royal Society at the Society’s London premises on 11-12 May 1995 (“A Critical Review of VAN”, Earthquake prediction from seismic electric signals, ed. Sir J. Lighthill, World Scientific, Singapore, 1996) (iii) A special issue of Geophysical Research Letters (Vol. 23, No.11, May 27, 1996) was solely focused on our results under the title “Debate on VAN”. The results of the 25 year period 1981-2005 have been compiled in the monograph: “P. Varotsos, The Physics of Seismic Electric Signals (TerraPub, Tokyo, 2005)”.

The main properties of SES could be summarized as follows (P. Varotsos and K. Alexopoulos, Tectonophysics 110, 73-98, 1984; Tectonophysics 110, 99-125, 1984): First, the SES amplitude is interrelated with the magnitude of the impending earthquake. This interrelation is in fact a power-law which corroborates that the approach of a system to a critical point (second order phase transition) is accompanied by fractal structure, thus being in accordance with the original SES generation mechanism proposed by Varotsos and Alexopoulos. Second, SES cannot be observed at all points of the Earth's surface but only at certain points called “sensitive points”. Each sensitive station enables the collection of SES only from a restricted number of seismic areas (“selectivity effect”). A map showing the seismic areas that emit SES detectable at a given station is called “selectivity map of this station”. This allows the determination of the epicenter of an impending earthquake (e.g. P. Varotsos and M. Lazaridou, Tectonophysics 188, 321-347, 1991; P. Varotsos, K. Alexopoulos and M. Lazaridou Tectonophysics 224, 1-37, 1993). Third, at epicentral distances of the order of 100km, the SES electric field precedes markedly (~1s) the time-derivative of the relevant magnetic field variations [P. Varotsos et al., Phys. Rev. Lett 91, 148501 (2003)]. This, which has been commented on by Physics World-Web of Science of the Institute of Physics (IoP, UK, February 2004), finds applications in the determination of the epicenter of the impending earthquake as well as in the distinction of true SES from “noise” emitted from manmade sources. The physical properties of SES can be theoretically explained, if we take into account the aforementioned SES generation mechanism together with the existence of inhomogeneities in the Solid Earth’s Crust (for a review see “P. Varotsos, The Physics of Seismic Electric Signals (TerraPub, Tokyo, 2005)”). The SES collection from the real time VAN telemetric network (nowadays consists of 9 measuring stations) enables the estimation of the three parameters: time (see also below the methodology of natural time), epicenter and magnitude of the impending mainshock. These predictions, when the expected magnitude is 6 units or larger, are submitted for publication in scientific journals before the earthquake occurrence.

(3) Proposal of a new Time Domain

A new concept of time, termed natural time, was introduced by P. Varotsos, N. Sarlis and E. Skordas, Practica of Athens Academy 76, 294-321, 2001; Phys. Rev. E 66, 011902, 2002. This was followed by a sequence of papers published mainly in Physical Review and Physical Review Letters.

In particular, it has been found that novel dynamical features hidden behind time series in complex systems can emerge upon analyzing them in the new time domain of natural time, which conforms to the desire to reduce uncertainty and extract signal information as much as possible [Phys. Rev. Lett. 94, 170601 (2005)]. The analysis in natural time enables the study of the dynamical evolution of a complex system and identifies when the system approaches a critical point. Hence, natural time plays a key role in predicting impending catastrophic events in general. Relevant examples of data analysis in this new time domain have been presented in a large variety of fields including Medicine, Biology, Earth Sciences and Physics:

First, in Cardiology, natural time analysis of electrocardiograms: Sudden cardiac death (SCD) is a frequent cause of death and may occur even if the electrocardiogram seems to be strikingly similar to that of a healthy individual. Upon employing, however, the entropy defined in natural time, SCD can be clearly distinguished from the truly healthy individuals [P. Varotsos et al., Phys. Rev. E 70, 011106 (2004); Phys. Rev. E 71, 011110 (2005)]. This finding has been commented on by New Scientist (3 April 2004). In addition, when considering the entropy change under time reversal, not only the SCD risk can be identified, but also an estimate of the time of the impending cardiac arrest can be provided [P. Varotsos et al., Appl. Phys. Lett. 91, 064106 (2007)].

Second, in Earth Sciences, the SES exhibit scale invariance over five orders of magnitude, which agrees with the original proposal that SES are governed by critical dynamics. The natural time analysis also showed that all the measured SES are characterized by very strong memory and fall on a universal curve [P. Varotsos et al., Practica of Athens Academy 76, 294-321 (2001); Phys. Rev. E 66, 011902 (2002)]. As for the SES distinction from similar looking “noise”, this is achieved upon employing modern tools of Statistical Physics (detrended fluctuation analysis, wavelet analysis etc.), but applied to the natural time domain [P. Varotsos et al., Phys. Rev. E 67, 021109 (2003); Phys. Rev. E 68, 031106 (2003)].

Third, in Seismology, natural time enables the determination of the occurrence time of an impending major earthquake since, as mentioned, it can identify when a complex system approaches a critical point. Since the detection of an SES activity signifies that the system enters in the critical regime, the small earthquakes that occur after the SES detection are analyzed in natural time. It was found that the variance (κ1) of natural time χ, i.e., κ1=<χ2>-<χ>2, becomes equal to 0.070 (which manifests the approach to the critical point) a few hours to one week before the main shock. This has been ascertained to date for several main shocks in Greece [P. Varotsos et al., Phys. Rev. E 72, 041103 (2005); Phys. Rev. E 73, 031114 (2006)] and Japan [S. Uyeda et al., J. Geophys. Res. 114, B02310 (2009)], including the prediction of three major earthquakes that occurred in Greece during 2008. For example, the 6.9 earthquake on 14 February 2008, which is the strongest earthquake that occurred in Greece during the last 30 years, was publicly announced as imminent on February 10, 2008 along with the identification of its epicenter and magnitude. This arouse a considerable international interest, e.g., see the two recent articles by Japanese scientists in EOS Transactions of the American Geophysical Union [S. Uyeda and M. Kamogawa, EOS 89, No. 39, p.363, 23 September 2008; EOS 91, No. 18, p.162, 4 May 2010]

Fourth, in Statistical Physics, upon employing natural time analysis, a striking similarity of fluctuations in equilibrium critical phenomena and non-equilibrium systems emerges. In particular, it was found [P. Varotsos et al., Phys. Rev. E 72, 041103 (2005)] that the scaled distributions fall on the same curve, which exhibits over four orders of magnitude features similar to those in several equilibrium critical phenomena (e.g., two-dimensional Ising model) and in non-equilibrium systems (e.g., three dimensional turbulent flow). After introducing an order parameter for seismicity, which is the quantity κ1 mentioned above, it was also shown that the worldwide seismicity, as well as that of the San Andreas fault system (California) and Japan fall on the same curve. Furthermore, the b-value (»1) in the Gutenberg-Richter law for earthquakes [stating that the number N (>M) of earthquakes larger than M is given by (N>M) ~ 10-bM] results from natural time analysis by applying the Maximum Entropy Principle. A similar law holds for solar flares etc. [P. Varotsos et al., Phys. Rev. E 74, 021123 (2006)].

Fifth, in Condensed Matter Physics, the time series of the avalanches of the penetration of magnetic flux into thin films of type II superconductors, when analyzed in natural time, were found to obey conditions (as far as the variance and the entropy is concerned) consistent with critical dynamics [N. Sarlis et al., Phys. Rev. B 73, 054504 (2006)]. The same holds for other systems that exhibit the so called self organized criticality (SOC), e.g., the avalanches in a three dimensional pile of rice getting progressively closer to the critical state.

Sixth, in the Physics of Complex Systems, a challenging point when analyzing data from such systems that exhibit scale invariant structure is the following: In several systems, this non trivial structure points to long range temporal correlations; in other words, the self-similarity results from the process’ memory only (for example, the fractional Brownian motion). Alternatively, the self-similarity may solely result from the process’ increments infinite variance (heavy tails in their distribution). In general, however, the self-similarity may result from both these origins. It has been shown [P. Varotsos et al., Phys. Rev. E 74, 021123 (2006); N. Sarlis et al., Phys. Rev. E 80, 022102 (2009)] that the identification of the origins of self-similarity can be achieved by employing natural time analysis. For example, the original earthquake data exhibit both origins of self-similarity (i.e., temporal and magnitude correlations), while the self-similarity of SES solely comes from infinitely ranged temporal correlations (very strong memory) [P. Varotsos et al., Phys. Rev. E 73, 031114 (2006)].

(4) Results from measurements in Japan

Several papers have reported SES observations before large earthquakes in Japan [e.g., S. Uyeda et al., Proc. Natl. Acad. Sci. USA 97, 4561-4566 (2000); Proc. Natl. Acad. Sci. USA 99, 7352-7355 (2002); see also Orihara et al., Proc. Natl. Acad. Sci. USA 109, 19125-19128 (2012), which has been highlighted by the journal under the title «Seismic electrical signals precede earthquakes at Kozu-shima Island Japan». In addition, Japanese workers have applied natural time analysis to SES activities observed in Japan as well as to their subsequent seismicity. They obtained results [Uyeda et al., J. Geophys. Res. 114, B02310 (2009)] which were in striking agreement with those found in Greece.

An additional fact showing the physical interconnection of SES with seismicity has been recently uncovered, as follows:

Almost a decade ago, Japanese scientists reported [Uyeda et al., Proc. Natl. Acad. Sci. USA 99, 7352-7355 (2002)] that almost 2 months before the onset of the volcanic-seismic swarm activity in the Izu Island region Japan they measured a pronounced SES activity. This swarm was then characterized by Japan Meteorological Agency as being the largest earthquake swarm ever recorded in Japan.

Recently, Varotsos et al. [Tectonophysics 589, 116-125 (2013)] demonstrated that a characteristic change of seismicity occurred upon the initiation of the aforementioned SES activity. In particular, the fluctuations of the order parameter κ1 of seismicity simultaneously exhibited a distinct minimum. This is the first time in the literature that well before the occurrence of major earthquakes characteristic changes were found to appear almost simultaneously in two independent datasets of different geophysical observables, i.e., geoelectrical data and seismicity. In addition, Varotsos et al., showed that these two phenomena are also linked in space.

Subsequently, Sarlis et al. (2013) [Proc. Natl. Acad. Sci. USA 110, 13734-13738 (2013)] proceeded to the analysis in natural time of the Japan seismic catalog from 1 January 1984 to 11 March 2011, the day of the super giant Tohoku earthquake of magnitude Mw9.0. They found that the fluctuations of the order parameter of seismicity exhibited distinct minima a few months before all the shallow earthquakes of magnitude 7.6 or larger that occurred during this 27 year period in Japanese area. Among the minima, the minimum before the Mw 9.0 Tohoku earthquake observed on ~5 January 2011 was the deepest. This, by considering the aforementioned findings, Varotsos et al (2013), reflects that a very strong SES activity should have been initiated on the same date. Consequently, anomalous magnetic field changes accompanying the electric field variations of this SES activity should also initiate on this date, i.e., 5 January 2011. This has been strikingly verified by an independent study of Chinese and Japanese scientists [Xu et al., J. Asian Earth Sci. 77, 59-65 (2013)] who reported that anomalous magnetic field variations initiated at a station around 135 km from the Tohoku earthquake epicenter on ~4 January and lasted almost 10 days. It was also found [Skordas and Sarlis, J. Asian Earth Sci. 80, 161-164 (2014)] that the κ1 values of seismicity computed after this date converged to κ1= 0:07 at 08:40 LT on 10 March 2011, i.e., the day before the Tohoku earthquake occurrence, thus signaling the impending risk.