Ionosonde data and crustal earthquakes with magnitude M≥6.0
observed in Greece during the 2003–2015 period were examined to
check if the relationships obtained earlier between precursory
ionospheric anomalies and earthquakes in Japan and central Italy are
also valid for Greek earthquakes. The ionospheric anomalies are
identified on the observed variations of the sporadic E-layer
parameters (h′Es, foEs) and
foF2 at the ionospheric station of Athens. The corresponding
empirical relationships between the seismo-ionospheric disturbances
and the earthquake magnitude and the epicentral distance are
obtained and found to be similar to those previously published for
other case studies.
The large lead times found for the ionospheric anomalies occurrence
may confirm a rather long earthquake preparation period. The
possibility of using the relationships obtained for earthquake
prediction is finally discussed.
Ionosphere (Ionospheric disturbances)Introduction
Earthquakes (EQs) are some of the most energetic phenomena occurring within
the Earth (e.g. Bolt, 1999), with potential coupling with atmosphere and
ionosphere not only at the moment of the largest energy release due to the
main fault rupture, but even during the long-term process of its preparation
(e.g. Freund, 2000; Hayakawa and Molchanov, 2002; Pulinets and Boyarchuk,
2004; De Santis et al., 2015). Pre-earthquake ionospheric anomalies can be
registered 1–2 months in advance (middle-term precursors) as well as with
lead times from some hours up to 1 day (short-term precursors; Gufeld and
Gusev, 1998). Ground-based facilities, such as ionosonde stations, magnetic
observatories and GPS receivers can monitor various parameters used to detect ionospheric anomalies, such as F2-layer critical frequency
(foF2), total electron content (TEC), electron temperature (Te) at
F2-layer heights, magnetic pulsations and low-frequency radio signals (see,
e.g.,
Hayakawa et al., 1999; Strakhov and Liperovsky, 1999; Bortnik et al., 2008;
Ondoh, 2009; Trigunait et al., 2004; Hobara and Parrot, 2005; Liu et al.,
2006; Maekawa et al., 2006; Sharma et al., 2006; Ondoh and Hayakawa, 2006;
Dabas et al., 2007; Oikonomou et al., 2016).
In particular, variations of the ionospheric parameters observed by
ground-based ionosondes, F2-layer critical frequency (foF2) and
parameters of sporadic E layer (Es), are here considered. Variations of
foF2 during seismo-active periods have been considered in many
papers, as foF2 observations are usually available from ground-based
ionosondes.
Hobara and Parrot (2005) analysed foF2 variations recorded by
ionosonde stations in the Asian longitudinal sector for an isolated and very
powerful Hachinohe earthquake (M=8.3). A foF2 decrease was
registered in the vicinity of the epicentre but not further than
1500 km apart. In that case a pronounced ionospheric reaction to the
event was registered.
Liu et al. (2006) analysed the association between foF2 and 184
EQs with M>5.0, which took place during 1994–1999 in the Taiwan area.
They observed a decrease of 25 % in foF2 within 5 days of
the EQ. Generally, this effect increases with the EQ magnitude and decreases
with the distance from the epicentre to the ionospheric station, as expected
from a lithospheric source. However, only the EQs with M>5.4 whose
epicentres were within 150 km of the ionospheric
station had a significant chance to result in a pronounced foF2
decrease.
Dabas et al. (2007) investigated the variations of foF2 at
low latitudes in relation with the occurrence of 11 major EQs with M≥6.
They observed unusual perturbations in the foF2 values from 1 to
25 days before the earthquake occurrence.
Xu et al. (2015) analysed the so-called Q-disturbances, i.e. variations
of foF2 during quiet periods along three solar cycles (1978–2008).
They found that positive Q-disturbances, probably related to EQs occurred
predominately in the daytime, especially in the local afternoon sector.
The occurrence probability and the frequency increase in the
semi-transparency range of the sporadic Es have been considered by Silina
et al. (2001). Ondoh (2009) and Ondoh and Hayakawa (2006) observed an
anomalous foEs increase on some Japanese ionosonde stations close in
time to a strong earthquake with M=7.2.
Any attempt at obtaining a quantitative relationship for seismo-ionospheric
precursors should be considered an important step towards the
understanding of physical mechanism of such relationships. For instance, Liu
et al. (2006) found the expressions describing the probability of the EQ
to result in a >25 % foF2 decrease with the magnitude and the
distance between the EQ epicentre and the ionospheric station.
The approach proposed by Korsunova and Khegai (2006, 2008) and successively
developed by Perrone et al. (2010) may be considered an improved attempt to
obtain a quantitative relationship for seismo-ionospheric anomalies. The main
idea is based on the results of a theoretical analysis by Kim et al. (1993,
1994), who showed that the electric field above the preparation zone of
future earthquakes can penetrate into the ionosphere to form a dense sporadic
layer Es at 120–140 km height above the earthquake preparation
zone. The preparation zone is defined by the formula of
Dobrovolsky
et al. (1979), ρ≤100.43M, where ρ is the radius in kilometres of
the supposed circular preparation zone and M is the magnitude. This formula
was obtained from a theoretical model of deformation before the fault rupture
causing a large earthquake and confirmed by different kinds of ground
geophysical observations in the crust and in the lower atmosphere.
Regarding the pre-EQ ionospheric anomalies we detected, the basic
feature of this mechanism could be the formation of a high Es layer due
to a penetrating electric field caused by the EQ preparation.
A distinguishing feature of our analysis is the multi-parameter
approach, which takes into account the variations of three parameters
simultaneously in Es and regular F2 layers (Korsunova and Khegay,
2008; Perrone et al., 2010; Villalobos et al., 2016).
Perrone et al. (2010) have considered all crustal M>5.0 earthquakes and a
hypocentral depth <50km, and with the ionospheric observatory
inside the preparation zone. The long-living (Δt∼2–3 h)
sporadic Es layers revealed and used in the analysis occurred at heights
of ≥10km higher than normal Es for corresponding geophysical
conditions. Their formation is accompanied by an increase in two other
ionospheric parameters (blanketing frequency of Es layer, fbEs, critical
frequency of F2 layer, foF2). It has been shown that the deviations
of ionospheric parameters from the background level can be related to the
magnitude and the epicentral distance of the corresponding earthquake.
The dependence obtained relates the lead time ΔT between the
observed ionospheric anomalies and the earthquake occurrence with the
magnitude of the earthquake and the epicentral distance.
More recently, Carter et al. (2013) studied the seismo-ionospheric anomalies
related to the M9 Tohoku earthquake. They analysed the variations of the
ionospheric parameters foF2, foEs, and h′Es as observed at three
Japanese stations. In this study, h′Es was found to deviate
by no more than 10 km. Thus, they analysed only the variations of
foF2 and foEs. They found anomalies that could be related
to the earthquake as well as others that could not be related to any
seismic activity.
The aim of the present paper is to check whether the method which was
already applied for powerful (M>6.5) crustal Japanese EQs
(Korsunova and Khegay, 2008) and for central Italian moderate (M≤5.8) EQs (Perrone et al., 2010) works in the case of Greek
earthquakes.
In the next section, we will present the data analysis; Sect. 3 shows
the results, while Sect. 4 assesses the quality of the method for
forecast purposes. Section 5 discusses the results, while Sect. 6
reports the conclusions.
Data analysis
Hourly observations from the ionospheric station of Athens
(38.0∘ N, 23.5∘ E) were used in our analysis. We
confined our analysis to the shallow (hypocentral depth <50km) and great magnitude (M≥6.0) earthquakes which
took place in the zone nearest to the epicentre (R≤350km) from Athens in the period 2003–2015 (Table 1 and Fig. 1). For
all these earthquakes, Athens is located in the preparation zone
according to the formula by Dobrovolsky et al. (1979).
Epicentres of major earthquakes in Greece and surrounding
area (M≥6, depth <50km) between January 2003 and
December 2015 and the ionosonde location.
List of the shallow earthquakes (hypocentral depth <50km)
with magnitude M≥6.0 registered in Greece during 2003–2015.
Geographical coordinates of the earthquake epicentres (from United States Geological Survey USGS, 2017) and
the distance (R) from the Athens ionosonde are also given. EQs
with (∗) have not been analysed because are too distant from
the Athens ionosonde station (R>350km).
Table 1 also shows the distance R from the ionosonde station to the
epicentre calculated along the great cycle path. The correction of R when
such distance is propagated to the E-region heights is <2km for
the selected events and it can be ignored. The epicentres are located at
a distance 90–500 km from Athens.
However, it was found that there is a limitation in distance after
which we could not find any reliable ionospheric anomalies (Korsunova
and Khegai, 2008; Perrone et al., 2010). In our case, we did not
analyse the more distant EQs from the Athens ionosonde station,
i.e. those with R>350km (EQs indicated by an asterisk in
Table 1). Therefore the case studies analysed were reduced to 10 EQs.
It should be stressed that the analysis of the EQ ionospheric
anomalies in the nearest ionosonde to the epicentre zone is most
important from a practical point of view. In the next paragraph, we will
explain the procedure.
According to Korsunova and Khegai (2008) and Perrone et al. (2010), the
deviations in h′Es, fbEs and foF2 ionospheric parameters
should simultaneously satisfy the ionospheric anomaly selection criteria.
Unfortunately, the fbEs parameter is obtained by manual scaling of the
ionograms and currently very few ionospheric observatories provide such
a parameter. In such cases, instead of using the fbEs parameter, we use
foEs, which is scaled automatically.
The occurrence of abnormally high Es layer for 2–3 h is considered
necessary to identify anomalies. The h′Es
height should exceed the corresponding background values by ≥10km. An increase in foEs and foF2 also for
2–3 h during the same day soon after the h′Es increase is
considered sufficient to identify ionospheric anomalies. The critical frequency foEs excess over the
background value should be not less than 20 %. Electron concentration in
the F2 layer is subjected to large and irregular variations; however, an
increase in foF2 by ≥10 % over the background level also
for 2–3 h after the increases in h′Es and foEs should
take place.
Ionospheric data analysis comprises some steps.
First, the background h′Es, foEs, foF2 variations
are specified. They characterize quiet-time diurnal variations for the
years and months analysed. The 27-day running medians calculated over all
quiet (Ap ≤15) days are used as the background values. Then, absolute
Δh′Es=h′Es-(h′Es)med, ΔfoEs=foEs-(foEs)med, ΔfoF2 =foF2 - (foF2)med and relative ΔfoEs/(foEs)med, ΔfoF2/(foF2)med deviations are
calculated for every hour of each day. Finally, we look for the
ionospheric anomaly in the 4 months preceding the earthquake. Middle-term
earthquake precursors may occur some months in advance (Korsunova and Khegai,
2006; Hao et al., 2000).
Particular attention in the data analysis has to be paid to the
fact that the coupling of the ionosphere with the magnetosphere makes
the former highly sensitive to the variations of the external field
modulated by the solar activity, masking other effects such as the
coupling with the lithosphere we are investigate here. Specifically,
while the magnetosphere is influenced by the solar wind parameters and
by the strength and orientation of the interplanetary magnetic field
(IMF), the ionospheric behaviour is principally modulated by the level
of solar and geomagnetic activity (Cander, 2016). The ionospheric
parameters are variable and affected from above (solar extreme ultraviolet,
EUV; magnetospheric and dynamo electric fields; changing thermospheric
circulation and neutral composition; travelling atmospheric
disturbances, TADs; etc.) and from below (planetary and gravity waves,
neutral gas vertical motion and eddy diffusion changing thermospheric
neutral composition, tropospheric electric fields not necessarily
related to seismic processes). This leads to the fact that ionospheric
behaviour is hard to predict, in terms of occurrence and determination
of the causes behind the formation of the ionospheric irregularities
in space and time.
Thus, we have to exclude ionospheric anomalies that could originate
from external forcing. To this aim, detailed information about the
physical state of the entire Sun–Earth system is required and,
consequently, several geophysical indices are monitored. Two
geomagnetic indices have been used in this analysis to characterize
the geomagnetic activity:
ap index: a 3 h index, available since
1932. It is expressed in nanotesla and derived from another index, the Kp,
which is an almost logarithmic scale with respect to the field
variations and it is based on the geomagnetic field data from 11
observatories located at mid- and high latitudes. This index is
commonly used in ionospheric studies, especially in
ionospheric forecasting models and long-term studies (Perrone
and De Franceschi, 1998). In this work, we considered the Ap index
that is the daily average of the corresponding eight ap values of
the day
(ftp://ftp.ngdc.noaa.gov/STP/GEOMAGNETIC_DATA/INDICES/KP_AP).
AE index: a 1 min index, available since
1957. It is computed using a network of auroral observatories and it
is introduced to characterize the auroral zone, where the
fluctuations of the magnetic field are much stronger than at mid- and low
latitudes. In the ionospheric studies, it is used to check if local
ionospheric variations are due to an increase in auroral activity
that could influence the mid- and low-latitude ionosphere (Perrone and De
Franceschi, 1998). In this work, we considered the 1 h mean of AE
(http://wdc.kugi.kyoto-u.ac.jp/aedir/).
In this study, the ionospheric anomaly associated with an
earthquake must occur under magnetically quiet conditions, which are
defined as
Daily geomagnetic index Ap ≤15.
The auroral electrojet index AE: in summer and equinox this should
be ≤100 nT and in winter ≤200 nT for the
previous 6 h. Different limits applied to AE index are due to
different meridional thermospheric circulation in different
seasons. Meridional daytime wind at middle latitudes is mainly
equatorward in summer and equinox, easing the perturbation to
penetrate from the auroral zone to middle latitudes, while in winter
the meridional circulation during daytime is mostly poleward, constraining
the perturbation at high latitudes (Buonsanto and Witasse, 1999;
Prölss and von Zahn, 1977; Prölss, 1993; Field et al.,
1998).
We considered 6 h before the occurrence of an ionospheric anomaly because
TADs related to upsurges of auroral activity can reach middle latitudes and
perturb foF2. Under the average TAD velocity at F2-region heights of
500–600 ms-1 (Bruinsma and Forbes, 2010; Mikhailov and Perrone,
2009; Mikhailov et al., 2012), the arrival of a TAD at middle latitudes is
expected in 2–3 h.
Sometimes EQs follow each other with a small time interval (see 3a, 3b
in Table 1), and it may be problematic to correlate a particular
ionospheric anomaly with a single, corresponding earthquake. In such
cases, the following rule is used: under approximately equal epicentre
distances, an ionospheric anomaly for an earthquake with larger
magnitude occurs earlier and produces larger deviations in
h′Es (Korsunova and Khegai, 2006).
Table 2 gives the ionospheric anomalies found with the corresponding
parameters and the related earthquakes.
Identified ionospheric anomalies and corresponding EQs (the EQ
order no. is the same used in Table 1). Daily Ap indices are given as well.
nDate of theionospheric anomalyTime UT Δh′EsδfoEsδfoF2ApDate and no. of the EQhh : mm UTM106.11.200710:00–12:00210.620.17014.02.2008 (no. 3)10:096.9210.12.200708:00–10:00240.490.19914.02.2008 (no. 4)12:086.5330.01.200813:00–14:00210.220.10220.02.2008 (no. 5)18:276.2430.12.201306:00–07:00180.280.10526.01.2014 (no. 11)13:556.1511.02.201407:00–10:00470.310.10524.05.2014 (no. 13)09:256.9
For the EQ that took place on 8 June 2008 (EQ no. 6, Table 1), no
ionospheric anomalies were found. For the other remaining four EQs, nos. 1,
10, 12 and 15, the ionospheric anomalies were found but they
did not occur during quiet magnetic conditions (see criteria 1 and 2 given
earlier).
As an example of valid pre-EQ anomaly, Δh′Es,
δfoEs and δfoF2 variations along with 3 h
ap for the EQ occurred on 24 May 2014 (EQ no. 13) are given in Fig. 2.
The ionospheric anomaly for the 24.05.14 EQ using observed
Δh′Es, δfoEs and δfoF2 variations (arrows). Three-hour
ap indices are given in lower panel.
Results
The applied method uses three ionospheric ionosonde parameters
simultaneously: 2–3 h splashes in Δh′Es,
δfoEs and δfoF2 above the corresponding
thresholds should take place within 1 day.
There are relationships for middle-term ionospheric anomalies related to lead time ΔT, which is the time in advance between the
ionospheric anomaly and the EQ occurrence, with the EQ magnitude M
and the epicentre distance R (Sidorin, 1992;
Korsunova and Khegai, 2006; Perrone et al., 2010).
Such relationships are given in Fig. 3 (black squares) for the events
listed in Table 2. The upper panel gives the dependence for (the
decimal logarithm of) lead time on the EQ magnitude. The middle panel
gives the same dependence, but for the product (ΔT×R),
while the lower panel gives the dependence for Δh′Es
on the earthquake magnitude M. The dependencies are statistically
significant at the 99 % level for the first two relationships and at the
97.5 % for the third confidence level, according to Fisher
criterion.
The observed dependencies for (from top to bottom)
log(ΔT×R), logΔT and log(Δ
h′Es) on the EQ magnitude. Correlation coefficients (r) of
the points from the regression line are given. Each dash line is the
best linear fit, while the continuous lines are those corresponding
to ± 1 standard deviation (SD).
The relationships obtained for log(ΔT), log(ΔT×R) and log(Δh′Es) are
log(ΔT)=0.81M-3.53;log(ΔT×R)=0.84M-1.33;log(Δh′Es)=0.28M-0.41.
The standard errors associated with the regressions (1–3)
coefficients are given in Table 3.
Coefficients of the regressions (1–3) and their standard errors.
Due to the small statistics size (only five events), the uncertainty in the
coefficients is rather large. However, the relationships obtained are
similar to those obtained in previous research. In particular,
Korsunova and Khegai (2006), for 33 powerful earthquakes with M≥6
that occurred in the region of Kokubunji station in 1985–2000,
obtained the following relationship:
logΔT×R=1.14M-4.72
Perrone et al. (2010) for moderate central Italian earthquakes obtained the
following.
logΔT=1.09M-4.897logΔT×R=0.886M-1.626logΔh′Es=0.672M-2.422
Ground observations of various geophysical parameters for a number of
earthquakes with magnitude in the range 4–8 (Sidorin, 1992) resulted
in the following dependence.
logΔT×R=0.72M-0.72
A qualitative agreement is seen for Eqs. (1)–(6), obtained using both
ground and ionospheric precursors. The similarity of the relationships that
were obtained in different parts of the world seems to confirm the uniformity
of the solid earth and ionosphere coupling processes during the EQ
preparation period.
Another aspect of earthquake prediction is the number of false cases in the
same period of study, i.e. all years from 2003 to 2015. For this purpose, the
Δh′Es, δfoEs and δfoF2
deviations were calculated for every hour of all days and all months of
these years. The calculated values were then analysed for ionospheric
anomalies in accordance with our criteria. The list of all false cases, i.e.
those cases for which we revealed the ionospheric anomalies during quiet
magnetic conditions according to our criteria but no EQs occurred, is given
in Table 4 along with M, ΔT, and R values calculated using the
relationships (1–3). We have not listed the ionospheric anomalies from which
we obtained these empirical relationships (Table 2).
Revealed false ionospheric anomalies for quiet periods
between 2003 and 2015 along with calculated M, ΔT, R values
using the relationships (1–3). Only expected events with M≥6.0
are listed.
n/nDate of the falseTime UTΔh′EsδfoEsδfoF2MΔTRionospheric anomalydayskm105.11.200310:00–14:00190.540.196.021240227.11.200313:00–16:00180.270.236.021240422.03.200513:00–14:00270.250.106.666248528.01.200606:00–10:00250.750.266.554246607.08.200813:00–15:00240.500.106.445249703.03.200911:00–12:00320.270.196.9115255817.08.200913:00–15:00400.820.27.2200262910.12.201308:00–09:00350.250.107.0138257
Only the characteristics of strong (M≥6.0) expected events are
listed in Table 4. The analysis undertaken has shown that false cases
do exist in the years analysed. They are not found to be numerous, but
their number is comparable to the number of real earthquakes (Table 2)
and they are not distinguished from the previous ionospheric anomalies
(Table 2).
Forecast possibilities of the method
An important result of our analysis is the provision of quantitative
expressions (1–3) relating the EQ magnitude and the epicentre
distance with observed h′Es variations for the Greek
region. In principle, such expressions could be used for prediction
purposes to determine the magnitude M and lead time ΔT of
a future earthquake.
However, large uncertainty (due to small statistics) of the regression
coefficients (Table 3) does not allow us to make such a prediction
with acceptable accuracy.
Dependences similar to (1–3) would only make practical sense if the
probability of false cases is not high. A special analysis has been
undertaken to clarify this question.
Confusion matrix
Receiver operating characteristic (ROC) analysis has been performed
in terms of a confusion matrix, giving the discrete joint sample
distribution of forecasts and observations in terms of cell counts
(e.g. Fawcett, 2006). It is very useful to perform an objective
validation of the method and a comparison with respect to a random
system.
We checked our ionospheric anomalies 4 months before all EQ
occurrences so we could see if a possible precursory anomaly
could exist in a cell of 4 months. For dichotomous categorical forecasts,
having only two possible outcomes (yes or no), the (2×2)
confusion matrix of Table 5 can be defined.
The parameters indicated in Table 5 are the following:
a is the number of the ionospheric anomalies followed by an
earthquake: true cases;
b is the number of ionospheric anomalies that do not
correspond to earthquakes: the number of false alarms;
d is the number of cases where there were no ionospheric anomalies followed by an
earthquake: the number of misses;
c is the number of forecasts with no events corresponding to no
events observed: the number of correct rejections.
Forecast quality for this (2×2) binary situation can be
assessed using a surprisingly large number of different measures, detailed in the following.
The hit rate (H) is the number of EQs preceded by an anomaly, so it
is an indication of the predictability of the events: the closer to 1, the better the
value. In our case,
H=aa+d=510=0.5.
The false alarms (F) are the number of anomalies without a following
earthquake: the closer to 0, the better the value. In our case,
F=bb+c=935=0.26. In some cases, the false alarm
could be due to a non-seismic source responsible for the ionospheric
anomaly. It is possible to reduce the number of false alarms
using other observables in a multi-parametric integrated approach
(e.g. De Santis et al., 2015).
Obviously, the best method provides both a high H and a low F.
A non-optimal case would be for alarms every time before impending
earthquakes but at the price of an increased amount of false
alarms, and
so the prediction system fails.
The accuracy, Acc, which is given by (a+c)/e, is a global
evaluation of the method and is a comparison of the success
(anomaly + earthquake or no anomaly + no seismic activity)
with respect to the total number of cases analysed. The closer its value to
1, the better the method. In our case,
Acc=a+ce=2539=0.69.
Gain, G, and R score (e.g. Aki, 1981) are some evaluation quality
factors that are an indication of the improvement of the method with
respect to a random system. The more positive the G, the better the
method. The R score can assume real (positive, null or negative)
values. If R score is negative the method is worse than a random
system; R score =0 means that the method behaves as a random guess;
a positive value means that the method is better than a random
system. R score equal to -1 is a total fail, while R score equal
to 1 is a perfect (ideal) method of prediction.
G=h×ea+b=1.61
The R score can be evaluated in two ways:
R=aa+b-dc+d=0.2,R′=H-F=0.24.
The accuracy is near 70 %, although the hit rate is not above 50 %,
and the false alarm rate is 26 %. R score is positive, indicating
a good forecast method.
The global evaluation of the method by these factors is quite
positive. The results provided by the method of EQ forecast based on
ionosonde data are clearly better than a random guess system.
Ionospheric anomalies during an increase in auroral
activity
It is interesting to note that even some of the ionospheric anomalies
that have been previously discarded because of the stringent limits
imposed by the magnetic indices could have some forecast value.
Figure 4 gives, together with the previous validated
ionospheric anomalies, also those (empty squares) with Ap <20, but
with an AE larger than the imposed limits, which could be linked to
the other four EQs (Table 6). Details on these discarded cases are given
in Table 6, but it should be stressed that the number of ionospheric
anomalies not related to earthquakes increases during a disturbed
period: from 2003 to 2015, we find 36 of this type of ionospheric
anomaly, and only 4 of them could be related to earthquakes.
The observed dependencies for log(ΔT), log(ΔT×R) and log(Δh′Es) on the EQ magnitude. Correlation
coefficients (r) of the points from the regression line are
given. Each dash line is the best linear fit, while the continuous
lines are those corresponding to ± 1 standard deviation
(SD). Empty squares represent those discarded anomalies
characterized by an AE larger than the imposed limits.
Revealed ionospheric anomalies during disturbed magnetic conditions
(AE larger than the imposed limits) and corresponding earthquakes, daily Ap
indices are given as well.
n/nDate of theTime UTΔh′EsδfoEsδfoF2ApDate of theUTMionospheric anomalyearthquakehour118.07.200318:00–23:00151.340.101514.08.200305:146.2227.08.201315:00–16:00170.630.151612.10.201313:116.6312.01.201411:00–12:00110.200.10703.02.201403:086.0406.09.201512:00–14:00160.20.111317.11.201507:106.5Discussion
The analysis undertaken has shown that the approach proposed by Korsunova and
Khegai (2006, 2008) and Perrone et al. (2010) can be applied to earthquakes
with M>6.0 taking place in Greece in the vicinity of Athens (i.e. well
within the Dobrovolsky strain radius ρ). The simultaneous deviations in
Δh′Es, δfoEs and
δfoF2 above the corresponding thresholds for 2–3 h
following each other within 1 day can be related by logarithmic dependences
with the EQ magnitude and the epicentre distance. Despite the few available
cases, the dependences obtained (1–3) for log(ΔT), log(ΔT×R) and log(Δh′Es) vs. the EQ magnitude
are statistically significant at the 99 % confidence level.
A theory of the earthquake preparation taking into account the
electromagnetic processes in the lithosphere–atmosphere–ionosphere system
(e.g. Kim et al., 1994; Pulinets et al., 1998; Sorokin et al., 2006) suggests
the formation of a sporadic E layer with large electron concentration at the
heights of 120–140 km above the earthquake preparation zone.
Long-living (Δt≈2–3 h), sporadic Es layers revealed
and used in our analysis occur at heights of ≥10km higher than
normal Es for corresponding geophysical conditions. Their formation is
accompanied by an increase in foEs and foF2.
In this work, we demonstrate that the method previously applied to
Japanese and central Italian EQs is also valid for Greek EQs. The
ionospheric anomalies found with the Athens ionosonde can be
considered middle-term seismic precursors.
Our method to identify ionospheric anomalies uses three parameters
simultaneously (Δh′Es being the main parameter), and this
is the principal difference with other methods based on just one ionospheric
parameter, for instance, foF2 (e.g. Liu et al., 2006; Dabas
et al., 2007). Although the authors attempted to avoid
periods with higher geomagnetic activity in their analyses, foF2
is a very variable parameter, affected both from above (e.g. by solar EUV,
magnetospheric and dynamo electric fields, changing thermospheric
circulation and neutral composition, TADs etc.; Bremer et al., 2009; Kutiev
et al., 2013; Mikhailov and Perrone, 2015) and from below (e.g. planetary
and gravity waves, neutral gas vertical motion and eddy diffusion changing
thermospheric neutral composition, tropospheric electric fields not necessary
related to seismic processes). Therefore, besides the geomagnetic activity
effects, there are many other sources of foF2 variations. The
morphology of the F2-layer perturbations not related to geomagnetic activity
(so-called Q-disturbances) can be found in Mikhailov et al. (2004) and
Depueva et al. (2005). The equatorial and low-latitude F2 region considered by
Dabas et al. (2007) is strongly affected by electric fields, which exhibit
large variability even under geomagnetically quiet conditions (e.g. counter
electrojet). Therefore, foF2 is a very “inconvenient” ionospheric
parameter for the role of an earthquake precursor, if used alone without
other parameters. For this reason, in our method foF2 is considered
to be a parameter whose variations are taken into account only along with Es
parameter variations, but 2–3 h deviations in ΔfoF2
above the background level should take place as well.
In this paper, along with daily Ap, we have used an additional
geomagnetic activity index of auroral activity, AE, to define quiet
geomagnetic conditions. This was used because upsurges of auroral
activity can launch TADs which might perturb mid-latitude F2 layer,
without be reflected in the anomalous daily Ap index.
We conclude with some words concerning the possibility of using this method in an
operational environment to find reliable earthquake precursors. We have shown
that the statistical reliability of the regression coefficients is not very
high and prevents us from making any quantitative forecasts. Another problem
of real forecast is the non-zero probability of false alarms: although they
are not numerous (Tables 4 and 5), they exhibit the same features as the real
ones so they cannot be distinguished in any real time prediction scheme.
On the other hand, our method can be used as an independent and
original contribution within a more integrated system of earthquake
prediction, where many other EQ-sensitive physical parameters are
considered as well (e.g. De Santis et al., 2015).
Conclusions
The results of our analysis may be summarized as follows.
The method earlier used for Japanese and central Italian earthquakes (Korsunova
and Khegay, 2008, and Perrone et al., 2010, respectively) was shown to be
applicable also for M≥6 Greek earthquakes. It is based on observed
simultaneous deviations in Δh′Es, δfoEs/δfbEs and δfoF2 above the
corresponding thresholds for 2–3 h following each other within 1 day. An
observed set of such deviations in the ionospheric parameters is considered
to be a middle-term ionospheric anomaly.
The ionospheric anomalies that occur during a period of increased
auroral activity (i.e. with high values of AE index) should not be
considered in the analysis: this reduces the possibility of
contaminating the results with external sources. However, we have
also showed that some of them could be potential EQ precursors, but
before extending the analysis to increased auroral activity periods,
more investigation is needed.
The observed ionospheric anomalies resulted in the relationship relating the lead time ΔT with the
earthquake magnitude M and the epicentre distance R. The relationship
obtained is statistically significant at the 99 % confidence level and it
looks similar to those obtained earlier for Japanese and central Italian
earthquakes. The relationship indicates the process of spreading the
disturbance from the epicentre towards periphery during the earthquake
preparation process (Perrone et al., 2010).
There are ionospheric anomalies not related to earthquakes. They are
not numerous but they are comparable with those potentially associated with the
seismic events and, even more importantly, they cannot be uniquely
distinguished from the ionospheric anomalies linked to the
earthquakes.
A systematic statistical analysis of 13 years of ionosonde data from
the Athens station has been undertaken, and the final result is that the
method has great potential for use in EQ
prediction, especially when it is integrated with other appropriate
independent data (De Santis et al., 2015).
Data of the Athens digisonde are available in the
Near-Earth Space Data Infrastructure for e-Science (ESPAS) portal,
https://www.espas-fp7.eu/portal/ (last access: 15 October 2016).
The authors declare that they have no conflict of
interest.
Acknowledgements
This work has been developed in the framework of the SAFE (Swarm for
Earthquake study) Project, funded by the European Space Agency
(contract no. 4000116832/15/NL/MP) under the STSE (Support To Science
Element) Swarm + Innovation Program, and coordinated by the INGV
– Istituto Nazionale di Geofisica e Vulcanologia.
Data of the Athens digisonde are available in the Near-Earth Space
Data Infrastructure for e-Science (ESPAS) portal,
http://www.espas-fp7.eu. The ESPAS project has received
funding from the European Union's Seventh Framework Programme for
research, technological development and demonstration under grant
agreement no. 283676. The topical editor, Ana G. Elias, thanks Jan
Laštovička and one anonymous referee for help in evaluating this
paper.
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