Download URSI-Türkiye-2006 Bildirilerinin Yazım Kuralları

URSI-TÜRK YE’2014 VII. Bilimsel Kongresi, 28-30 A ustos 2014, ELAZI
STATISTICAL STUDY on RELATIONSHIP BETWEEN
EARTHQUAKES and IONOSPHERIC F2 REGION CRITICAL
FREQUENCIES
Tuba Karaboga, Murat Canyilmaz*, Osman Ozcan
Mus Alparslan University
Department of Physics
Mus
[email protected], [email protected],
*Firat University
Department of Physics
Beytepe, Elazig
[email protected]
Özet: Bu çal mada, Japonya’ da 2004-2008 y llar süresince depremlerden önce iyonkürenin F2 bölgesi kritik
frekans n de imi (foF2) istatistiksel olarak incelenmi tir. Tüm depremler için iyosonda datalar deprem
haz rl k alan n içinde bulunan Kokubunji istasyonundan (35.71oN, 139.49oE) al nd ve foF2 datalar na
Dalgalanma metodu uyguland . Belirlenen zaman süresince depremlerden önce foF2’de anormal de imler
oldu u gözlenmi tir. Bu de imler iyonkürede haberciler olarak göz önünde bulundurulabilir ve k sa vadede
deprem tahmini için kullan labilirler.
Abstract: In this study, variations of the ionospheric F2 region critical frequency (foF2) have been investigated
statistically before earthquakes happened during 2004-2008 periods in Japan area. Ionosonde data was taken
from Kokubunji station (35.71oN, 139.49oE) which is in the earthquake preparation zone for all earthquakes and
Fluctuation method is applied to the foF2 data. It is observed that there are anomalous variations on the foF2
before earthquakes during the specified time periods. These variations can be regarded as ionospheric
precursors and may be used for short term earthquake prediction.
1. Introduction
It is well known that ionosphere plays a unique role in the Earth's environment because of strong coupling
processes to regions below and above it [1]. There are many reports of possible links between ionospheric
phenomena and events, e.g., earthquakes, volcanic explosions and severe weather, solar and geomagnetic
activities. [2]. It is recently recognized that seismic activities caused variations not only lithosphere, but also
atmosphere and ionosphere [3-8]. This is called “Lithosphere-Atmosphere-Ionosphere Coupling” [9]. These
variations of lithosphere–atmosphere-ionosphere parameters occurred prior earthquakes are considered as
earthquake precursors [3, 7]
Earthquakes as geophysical phenomena involve processes which are irregular, non-linear and complicated
such that simple solutions might not relate the earthquake parameters to reasonably correct prediction.
Therefore, it is necessary to establish rather reliable methods to closely investigate the earthquake process and its
relevant parameters within speci c precursors [10]. During the last 20 years, to predict earthquakes accurately
many researchers have developed new earthquake monitoring and prediction methods [9, 11-13].
This paper presents a statistical study of foF2 variations for earthquake precursors as follows: Earthquake and
foF2 data and fluctuation method are described section 2, the results of methods are given in section 3 and
discussed in section 4.
2. Material and Method
The size of modified area in ionosphere is of the same order of magnitude as the size of the earthquake
preparation area on the ground surface [11]. The radius of earthquake preparation zone ( ) was estimated by
Dobrovolsky and was calculated for each earthquake by using following formula [11, 14].
(1)
URSI-TÜRK YE’2014 VII. Bilimsel Kongresi, 28-30 A ustos 2014, ELAZI
Here M is the observed magnitude in Richter scale. In the present study we investigated 4 earthquakes which had
magnitudes with M 6.0 and depth (D) < 40 km. In Table 1, earthquake information (their onset date and time,
epicenter latitude/longitude, focal depth and distance from earthquake preparation zone) is given.
Table1. Characteristics of earthquakes which are studied in this work.
S.N.
1
2
3
4
Location Name
Date
Time
Near East Coast of
Honshu
Far East Coast of
Honshu
Near West Coast of
Honshu
08.05.2008
01:45:19
30.05.2004
05:56:12
25.03.2007
09:41:57
Hokkaido
06.12.2004
23:15:11
Epicenter
36.14 0N
141.540E
34.2560N
141.3850E
37.2810N
136.6020E
42.9070N
145.2000E
Magnitude
(M)
Depth
(D)
Radius Of
Earthquake
Preparation Zone
( )
Distance
From
Station
6.8
36.6
839.46
191.09
6.5
38.1
623.73
236.54
6.7
5
760.33
311.98
6.8
35
839.46
394.170
In this work, hourly values of foF2 data have been obtained from ionosonde station Kokubunji (35,7 oN,
139,49oE). For every earthquake Kokubunji ionosonde station is inside the earthquake preparation zone.
Earthquakes and ionosonde stations’ location can be seen in Figure1 (LT=UT+9.0).
Figure1. Visual exhibition of earthquakes by numbers and Kokubunji (K) ionosonde station.
2.1 Describing of Method
In this technique, to identify anomalous variations, we have computed hourly mean values of foF2 and found the
residue as follows:
(2)
Where
and
are the value of foF2 and mean at a time t on a current day over the period respectively.
is the residual of
. In this section, we have considered just negative value of
. Because
is essential for variations triggered by seismic activities [6, 15].
(3)
Where Ns and Ne are the times of starting and ending the time. We have normalized fluctuation by (standard
deviation over the whole period). The results of methods given in Tablo1 by the numbers are shown in Figure2
with the same order.
URSI-TÜRK YE’2014 VII. Bilimsel Kongresi, 28-30 A ustos 2014, ELAZI
3. Results and Discussions
Figure2. Fluctuation analysis (FN-Fluctuation normalization) for foF2 (dotted line). Dashed line is 2 (UBUpper bound).
This method used for nighttime VLF/LF signal in previously studies by Maekawa et al. 2006, Hayakawa, 2007,
Muto et al., 2009 and found
exceeded the corresponding standard deviation. We used F2 region critical
frequency over 24 hour and found similar results. For all earthquakes, it can be seen that ionospheric foF2
exceeded during the specified time periods. Especially there were big variations in second earthquake. It wasn’t
found relationship between amplitude of variations and magnitude of earthquake or depth.
4. Conclusion
In this study, we have analyzed statistically ionospheric F2 region critical frequency before four earthquakes in
Japan and we have found outstanding precursory variations before earthquakes. The results we found in this
study show that there is a relationship between lithosphere and ionosphere existing before earthquakes and
earthquakes can excite atmospheric and ionospheric perturbations. Earthquake precursors are difficult to identify
because of the complexity of the earth’s crust, various types of the earthquake mechanisms and the absence of
widespread geophysical, geochemical and ionospheric data and observing in most regions, as well as there are a
lot of factors affected on day to day variability on ionosphere like especially geomagnetic and solar activities.
Moreover, mechanism of seismo-ionospheric coupling is still not fully understood. To make short-term
earthquake prediction more accurately, further investigation is required together with other areas like geophysics,
geomagnetism, geochemistry, seismology, and atmospheric physics and integration of different kinds of
precursors from different experiment.
Acknowledgements: The authors thank The USGS Earthquake Hazards Program and Japan National
Institute of Information and Communications Technology and National Geophysical Data Center (NGDS),
NOAA satellite and information service for providing earthquakes and foF2 data.
References
[1].
[2].
[3].
[4].
[5].
[6].
Rishbeth, H. and M. Mendillo, Patterns of F2-layer variability. Journal of Atmospheric and SolarTerrestrial Physics, 2001. 63(15): p. 1661-1680.
Rishbeth, H., F-region links with the lower atmosphere? Journal of atmospheric and solar-terrestrial
physics, 2006. 68(3): p. 469-478.
Akhoondzadeh, M., M. Parrot, and M. Saradjian, Electron and ion density variations before strong
earthquakes (M> 6.0) using DEMETER and GPS data. Natural Hazards & Earth System Sciences,
2010. 10(1).
Yao, Y., et al., Analysis of ionospheric anomalies before the 2011 M w 9.0 Japan earthquake. Chinese
Science Bulletin, 2012. 57(5): p. 500-510.
Panda, G., et al., Study of Ionospheric Perturbations during Turkey–Central Earthquake of December
20, 2007. J. Ind. Geophys. Union (April 2010), 2010. 14(2): p. 133-138.
Maekawa, S., et al. A statistical study on the effect of earthquakes on the ionosphere, based on the
subionospheric LF propagation data in Japan. in Annales Geophysicae. 2006. Copernicus GmbH.
URSI-TÜRK YE’2014 VII. Bilimsel Kongresi, 28-30 A ustos 2014, ELAZI
[7].
[8].
[9].
[10].
[11].
[12].
[13].
[14].
[15].
Liu, J., et al., Seismo-ionospheric signatures prior to M 6.0 Taiwan earthquakes. Geophysical research
letters, 2000. 27(19): p. 3113-3116.
Hayakawa, M., et al., On the correlation between ionospheric perturbations as detected by
subionospheric VLF/LF signals and earthquakes as characterized by seismic intensity. Journal of
Atmospheric and Solar-Terrestrial Physics, 2010. 72(13): p. 982-987.
Horie, T., et al., The wave-like structures of ionospheric perturbation associated with Sumatra
earthquake of 26 December 2004, as revealed from VLF observation in Japan of NWC signals. Journal
of atmospheric and solar-terrestrial physics, 2007. 69(9): p. 1021-1028.
Akhoondzadeh, M. and M.R. Saradjian, TEC variations analysis concerning Haiti (January 12, 2010)
and Samoa (September 29, 2009) earthquakes. Advances in Space Research, 2011. 47(1): p. 94-104.
Pulinets, S., et al., Correlation analysis technique revealing ionospheric precursors of earthquakes.
Natural Hazards and Earth System Science, 2004. 4(5/6): p. 697-702.
Chuo, Y., et al., Ionospheric< i> foF2</i> variations prior to strong earthquakes in Taiwan area.
Advances in space research, 2001. 27(6): p. 1305-1310.
Hayakawa, M., et al., Subionospheric VLF/LF probing of ionospheric perturbations associated with
earthquakes: A possibility of earthquake prediction. Journal of Control, Measurment and System
Integration, 2010. 3: p. 10-14.
Gwal, A., et al., Ionospheric precursors observed during some earthquakes. J. Ind. Geophys. Union
(April 2010), 2010. 14(2): p. 115-131.
Hayakawa, M., VLF/LF radio sounding of ionospheric perturbations associated with earthquakes.
Sensors, 2007. 7(7): p. 1141-1158.