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Statistical analysis of automatically detected ion density variations
recorded by DEMETER and their relation to seismic activity

Michel Parrot

Laboratoire de Physique et Chimie de l’Environnement et de l’Espace, Université d’Orléans, CNRS, Orléans, France

ANNALS OF GEOPHYSICS, 55, 1, 2012; doi: 10.4401/5270

ABSTRACT

Many examples of ionospheric perturbations observed during large seismic
events were recorded by the low-altitude satellite DEMETER. However,
there are also ionospheric variations without seismic activity. The present
study is devoted to a statistical analysis of the night-time ion density
variations. Software was implemented to detect variations in the data
before earthquakes world-wide. Earthquakes with magnitudes >4.8 were
selected and classified according to their magnitudes, depths and locations
(land, close to the coast, or below the sea). For each earthquake, an
automatic search for ion density variations was conducted from 15 days
before the earthquake, when the track of the satellite orbit was at less than
1,500 km from the earthquake epicenter. The result of this first step
provided the variations relative to the background in the vicinity of the
epicenter for each 15 days before each earthquake. In the second step,
comparisons were carried out between the largest variations over the 15
days and the earthquake magnitudes. The statistical analysis is based on
calculation of the median values as a function of the various seismic
parameters (magnitude, depth, location). A comparison was also carried
out with two other databases, where on the one hand, the locations of the
epicenters were randomly modified, and on the other hand, the longitudes
of the epicenters were shifted. The results show that the intensities of the
ionospheric perturbations are larger prior to the earthquakes than prior to
random events, and that the perturbations increase with the earthquake
magnitudes.

1. Introduction
A substantial number of effects in the Earth’s

atmosphere and the ionosphere that are possibly associated
with earthquakes have been revealed over the past 20 years.
Ionospheric phenomena and their correlation with seismic
events have been examined in numerous experimental
studies. The seismo-electromagnetic effects include:
electromagnetic emissions in a large frequency range,
perturbations of ionospheric layers, anomalies in the records
of very low frequency (VLF) transmitter signals, night
airglow observations, and heating. The hypotheses of the
generation mechanisms of precursors to earthquakes are

mainly related to: wave production by rock compression,
diffusion of water in the epicentral area, and redistribution
of electric charges at the surface of the Earth and then in the
Earth’s atmospheric system.

These hypotheses and modeling have already been
published, and they can be found for example in monographs
published by Hayakawa and Fujinawa [1994], Hayakawa
[1999] and Hayakawa and Molchanov [2002], in books by
Gokhberg et al. [1995] and Pulinets and Boyarchuk [2004], in
special issues of journals [Parrot and Johnston 1989, 1993,
Hayakawa 2002, Hayakawa et al. 2004], and in reviews by
Molchanov [1993], Hayakawa [1997], Freund et al. [2006],
Tronin [2006], Ouzounov et al. [2007], Pulinets [2009] and
Harrison et al. [2010], and in references therein.

The DEMETER (Detection of Electro-Magnetic
Emissions Transmitted from Earthquake Regions) satellite
was the first satellite with a complete payload that was
specifically dedicated to this scientific objective [Parrot
2006]. With its data, many ionospheric perturbations have
been observed in relation to earthquakes, and examples
can be found in Parrot et al. [2006], Ouyang et al. [2008],
Zhu et al. [2008], Zeng et al. [2009], Zhang et al. [2009a,b,c,
2010a,b,c), Akhoondzadeh et al. [2010], Bankov et al.
[2010], He et al. [2010], Yufei et al. [2010], Zlotnicki et al.
[2010] and Píša et al. [2011].

However, it needs to be kept in mind that all of the
measured parameters also show variations in the absence
of seismic activity, as the mid-latitude and equatorial
ionosphere is affected by a number of other sources of
perturbations, and primarily by solar activity. Therefore,
only a statistical study covering many events will be able to
show the general behavior of these ionospheric
perturbations, and will help us to define a signature of
ionospheric perturbations prior to earthquakes. This is
possible with the DEMETER data, because the lifetime of
the mission is more than 6 years.

The DEMETER payload is briefly described in Section
2. In Section 3 the events that occurred before the main

Special Issue: EARTHQUAKE PRECURSORS

149

Article history
Received july 4, 2011; accepted September 26, 2011.
Subject classification:
Ionosphere, Satellite, Earthquake interactions and probability.

shocks of earthquakes are shown. The statistical analysis and
the results are presented in Section 4. Discussion and
conclusions are provided in Section 5.

2. The DEMETER satellite
DEMETER is a low-altitude satellite (710 km) that was

launched in June 2004 into a polar and circular orbit. It can
measure the electromagnetic waves and plasma parameters
all around the globe, except in the auroral zones [Parrot
2006]. The altitude of the satellite was decreased to 660 km
in December 2005. The scientific mission of DEMETER
came to an end in December 2010. Due to technical reasons,
the data were only recorded at invariant latitudes of less than
65˚. The orbit of DEMETER is nearly sun-synchronous and
the up-going half-orbits correspond to night-time (22.30 LT),
whereas the down-going half-orbits correspond to day-time
(10.30 LT). As nearly sun-synchronous, this means that
everyday the satellite did not return to exactly above the
same point, but to above the same area (it could be at more
than 1,000 km from the point it flew over in the previous
days). The payload of DEMETER included an instrument
known as a thermal plasma analyzer (IAP; Instrument
Analyseur de Plasma). The aim of the IAP was to measure
the main parameters of the thermal ion population; i.e. the
densities of the major ionospheric ions, H+, He+ and O+,
their temperature, and the ion flow velocity from the Earth’s
frame of reference. The IAP provided ion densities with a 4-
s time resolution. Details of the IAP experiment can be found
in Berthelier et al. [2006].

3. Examples of ionospheric perturbations
Figures 1 and 2 show a remarkable event that was

recorded by DEMETER before the M 8 Samoa earthquake
that occurred on September 29, 2009, at 17.48.11 UT (location
15.51˚S, 187.97˚E). Figure 1 corresponds to data recorded on
September 22, 2009 (7 days before the earthquake) whereas
data in Figure 2 have been recorded on September 28, 2009 (1
day before the earthquake). Both of these Figures have the
same presentation. The top panels in Figures 1 and 2 show
the O+ ion density, and the bottom panels show the
earthquake occurrences along the satellite orbit. These
bottom panels also show the satellite closest approach to past
and future earthquake epicenters that are within 2,000 km of
the DEMETER orbit. The Y-axis represents the distances D
between the epicenters and the satellite, from 750 km up to
2,000 km. The symbols are filled green squares for post-
seismic events and filled red triangles for pre-seismic events.
The color scale on the right of these bottom panels represents
the time interval between the earthquakes and the
DEMETER orbit, with color gradation from >30 days down
to a 0-6 h interval. The symbol sizes correspond to
earthquakes of magnitudes 5-6, 6-7, and >7. The red triangles
indicate the closest approach to the Samoa earthquake and
the many aftershocks, with their elongated positions
indicating that the DEMETER orbit was almost parallel to
the rupture fault. The density presents a clear fluctuation
when the satellite arrives above the future epicenter. Other
ionospheric perturbations for this earthquake were published
by Akhoondzadeh et al. [2010]. By examining the three

150

PARROT

Figure 1. Data recorded on September 22, 2010, between 09.29.00 UT and 09.35.00 UT, 7 days before a powerful earthquake in the Samoa Islands. Top
panel, ion density, where due to the scale, only the density of O+ ions is seen, as the majority. Bottom panel, distance and magnitudes of forthcoming
earthquakes, as a function of time. Red triangles, future main shocks and aftershocks; green symbols, past earthquakes (see text for details). Below the
panels, details of the observations (during night-time along the rupture zone of the earthquakes).

151

preceding months, they showed that these perturbations were
very uncommon in this area at that time.

Many other ionospheric perturbations were recorded by
DEMETER, and even for earthquakes of smaller

magnitudes. These were generally observed a few hours
before the moderate earthquakes. An example is given in
Figure 3. All of these observations were performed during
the night-time.

DENSITY VARIATIONS WITH SEISMIC ACTIVITY

Figure 2.Data recorded on September 28, 2010, between 09.14.00 UT and 09.20.00 UT, 1 day before a powerful earthquake in the Samoa Islands. See Figure
1 for details.

Figure 3. Data recorded on November 27, 2007, between 11:05:00 UT and 11:11:00 UT, a few tens of minutes before a M 6.6 earthquake, which occurred
at 11:50:02 UT (latitude, –11.02˚S; longitude, 162.17˚E; depth, 41.6 km). See Figure 1 for details.

4. The statistical analysis
It must be said that the variations in the ionospheric

parameters are not only due to earthquakes. There are
numerous possibilities of ionospheric perturbations that can
come from other sources (solar activity, acoustic gravity
waves, traveling ionospheric disturbances, plasma dynamics,
and large meteorological phenomena). In the examples that
have been given in the past, great care was taken to show
that these ionospheric variations that are associated with
seismic activity are uncommon at these locations and at the
times when they were observed.

On the other hand, with satellite data, we do not find
ionospheric perturbations for all earthquakes. This might be
due to the crust composition and soil configuration.
However, we also do not expect to have continuous
ionospheric perturbations, and with a single satellite we are
‘above’ a given future epicenter for only 3 min per day (night-
time half-orbit). Here, the term ‘above’ refers to a distance of
less than 1,500 km.

For these two above-mentioned reasons, we preferred
to search for possible influences of seismic activity on the
ionosphere using a statistical analysis. Automatic software to
detect density fluctuations was developed. The inputs are the
earthquake list and the DEMETER data. Earthquakes with
magnitudes >4.8 were selected, and for each of these, we
associated a parameter related to the position of the
epicenter: below the sea or inland, and close to the coast.
During the period considered (August 2004 to October 2009),
there were 17,366 such earthquakes. The software searched
for the data of the orbits that were close to the epicenters
(less than 1,500 km) between 0 and 15 days before each of
the earthquakes. It then retains one night-time half-orbit per
day, whereby if there were two orbits, it retains the closest
one. It is possible that for a given earthquake there was no
half-orbit for that given day because the data were not
recorded or because the half-orbit was too far away. To detect
variations, the software considers the DEMETER ion density
data over the 3 min around the closest approach to the future
epicenter of an earthquake. The data are smoothed and
variations (crests or troughs) are searched using change in
the derivative sign.

At the end, the software produces a final database with
the results. For each earthquake, the first line of the database
gives the information about its date, time, latitude,
longitude, magnitude, depth and position. The following 15
lines give the information on the data recorded along the
half-orbits. The second line relates to the 24 h before the
earthquake, then the third line to the –48 h to –24 h period,
and so on. The information provided includes the half-orbit
number, the date and time where the half-orbit was closest to
the epicenter, the date, time and position when the
automatic software detected any variation in the ion
densities, and parameter A, which gives the percentage of

these variations relative to the background. The software
could also detect no variations.

To evaluate the results of the automatic detection
software working with the earthquake list, we considered
two other lists with random data. First, we took the list of
the earthquakes and randomly changed their latitudes and
longitudes (keeping the same times). The resulting database
after the software application is called RAND1. Secondly, we
took the same list of earthquakes and only shifted their
longitudes 25˚ to the west (keeping the same latitudes and
times). After the software application, this third database is
called RAND2. This shift of the longitude is because, on the
one hand, most of the earthquakes were concentrated
around the equator, and on the other hand, it is known that
during the night-time, natural occurrences of ionospheric
perturbations are also more concentrated around the
equator. Then with RAND2 we have a database that fits
more with the reality concerning the positions of the
earthquakes and the possible ionospheric perturbations due
to usual geophysical activities.

To reduce the effects of solar activity, we eliminated the
data when the kp index was >3+. We also do not take into
account the aftershock data when the time of the aftershock
is too close to the time of the main shock, in order not to
mix pre-seismic and post-seismic effects. It is known that at
the exact time of an earthquake, you can have propagation
of an acoustic gravity wave that can perturb the ionosphere
[see for example, Garcia et al. 2005]. Then, if N is the number
of days between the main shock and the aftershock, and if N
<15, then we have only considered the aftershock data for
N–1 days before this aftershock.

This automatic software to detect variations is certainly
not perfect, because the shapes of any ionospheric
perturbations might be very different. We have examples
with very sharp peaks or very smooth peaks in the ion
density. However, we ran the software in the same way on
the three lists to search for ionospheric perturbations and to
produce three databases (earthquakes, RAND1 and RAND2).

The largest values of A were selected in each data bases
(earthquake and random) from the 15 days before each
earthquake (each event for the random databases). A
distinction was made regarding the positions of the
earthquakes (all epicenters, with epicenters below the sea,
with epicenters inland or close to the coast). To check whether
the ionospheric perturbations depend on the earthquake
magnitudes, the results are displayed as functions of several
magnitude intervals ([4.8, 5.0], ]5.0, 5.5], ]5.5, 6.0], and ]6.0,
9.0]). This selection of intervals was designed to have enough
events in each case for the statistical analysis. Even if it has no
meaning, this was also done for the random databases, to have
several draws in these databases. The characteristics of the
statistical distributions of these largest values of A were
evaluated using the kurtosis and the skewness parameters. It

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153

appears that the kurtosis is always much less than 3 and the
skewness is not close to 0 (see Tables 1 and 2). This means that
the distributions of these data do not follow a normal law and
that it is better to use a median value than a mean value of
parameter A. Table 1 gives the results obtained with the
RAND1 and RAND2 databases. As expected, larger
perturbations (median values) are observed with the RAND2
database, and in the following, all of the comparisons are
carried out with this RAND2 database. To obtain a reference
median value, 20 draws were made in the RAND2 database
(varying the false magnitudes and depths). All of these median
values were averaged and a standard deviation was calculated.
Table 1 gives the events that occurred everywhere, although

similar calculations were carried out for events below the sea
and for inland events. For the RAND2 database, the final
averaged median values are 7.82 ± 0.61 (all events), 8.21 ± 1.23
(events below the sea), and 7.46 ± 0.34 (inland events). These
values can be compared with the median values calculated
from the real earthquake database, which are given in Table 2.
This Table 2 gives for each magnitude interval and for each
epicenter position, the median values of each distribution. It
can be seen that the maximum perturbations increase with
the magnitude, as was expected, and that they are more
intense for earthquakes that occurred below the sea. These
results are significant if they are larger than the average
median value for random events plus the variance (8.43 for all
events, 9.44 for sea events, 7.80 for inland events). This is true
for powerful earthquakes (Table 2, last column) and all inland
earthquakes.

The influence of the depth was checked for powerful
earthquakes (]6.0, 9.0]), and these results are shown in Table
3. Table 3 can be compared with the last column of Table 2
(identical values in the tables are just by chance). As
expected, the perturbations are not so important for deep
earthquakes (>40 km).

5. Discussion and conclusions
A few examples of ionospheric perturbations prior to

seismic activity have been presented in this study. These
perturbations occur in close vicinity of the earthquake
epicenters, and a few hours to a few days before the shocks.
However, many other phenomena can perturb the
ionosphere, and as people might doubt the relationships
between the ionosphere and the Earth’s crust despite the
various mechanisms briefly mentioned in the Introduction,
a statistical analysis was performed while considering a huge
number of events.

These statistics show that ionospheric perturbations are
more important close to the epicenters of future inland

DENSITY VARIATIONS WITH SEISMIC ACTIVITY

Statistic Magnitude range

]4.8, 5.0] ]5.0, 5.5] ]5.5, 6.0] ]6.0, 9.0]

RAND1

Number of events 7576 4347 1259 570

Median 7.49 7.52 7.89 7.12

Kurtosis 1.548 1.565 1.669 2.456

Skewness 1.145 1.160 1.124 1.373

RAND2

Number of events 6218 3460 987 415

Median 7.68 7.77 8.03 7.19

Kurtosis 0.719 0.843 0.805 0.346

Skewness 0.931 0.966 0.897 0.878

Table 1. Statistics relating to the maximum intensities A of perturbations
(see text for explanations) according to the RAND1 and RAND2 random
databases with events occurring everywhere.

Statistic Magnitude

]4.8, 5.0] ]5.0, 5.5] ]5.5, 6.0] ]6.0, 9.0]

All earthquakes

Number of earthquakes 6332 3506 1032 420

Median 8.16 8.23 8.47 9.27

Kurtosis 0.437 0.598 0.003 -0.032

Skewness 0.812 0.809 0.686 0.696

Sea earthquakes

Number of earthquakes 1734 1084 328 98

Median 8.66 8.69 8.33 10.48

Kurtosis 0.628 0.457 0.021 -0.582

Skewness 0.830 0.777 0.650 0.474

Inland earthquakes

Number of earthquakes 4598 2422 704 322

Median 7.94 7.93 8.53 8.96

Kurtosis 0.373 0.656 -0.086 0.201

Skewness 0.812 0.809 0.687 0.774

Table 2. Statistics relating to the maximum intensities A of perturbations
for the earthquake database (see text for explanations). The results are
given as functions of the earthquake epicentre positions and magnitudes.

Statistic Depth

≤40 km >40 km
All earthquakes

Number of earthquakes 313 107

Median 9.35 8.64

Sea earthquakes

Number of earthquakes 75 23

Median 10.84 8.64

Inland earthquakes

Number of earthquakes 238 84

Median 8.96 8.84

Table 3. Statistics relating to the maximum intensities A of the
perturbations for powerful earthquakes (]6.0, 9.0]) as a function of their
locations and depths.

earthquakes than prior to events at random positions. It also
indicates that the intensities of these perturbations is more
important when the magnitudes of the earthquakes are
greater.

One surprising result is that there are more intense
perturbations for earthquakes with an epicenter below the
sea. However, the comparison with the random database
indicates that this variation is inside the normal fluctuation
range (mean ± standard deviation). This can be linked to a
possible mechanism of generation of these natural
ionospheric perturbations. For instance, it is known that the
electrical conductivity is larger above the sea.

Finally, deep earthquakes (>40 km) do not produce
ionospheric perturbations. The presented statistical analysis
agrees with previous studies that have reported local
increases in particle densities in time and positions close to
forthcoming earthquakes [see for example, Liu et al. 2009,
Kon et al. 2011].

Acknowledgements. This study was supported by the Centre
National d’Etudes Spatiales. It is based on observations with the plasma
analyzer on DEMETER. The author thanks J.J. Berthelier, the PI of this
instrument, for the use of the data, and Pauline Le Maire for help in data
handling. The study leading to these results also received funding from
the European Community Seventh Framework Programme (FP7/2007-
2013), under grant agreement N° 262005.

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*Corresponding author: Michel Parrot
Université d’Orléans, CNRS, Laboratoire de Physique et Chimie de
l’Environnement et de l’Espace, Orléans, France;
e-mail: mparrot@cnrs-orleans.fr

DENSITY VARIATIONS WITH SEISMIC ACTIVITY