APPLICATION OF GNSS NETWORKS TO DETECT AND ANALYZEATMOSPHERIC-INDUCED IONOSPHERIC DISTURBANCES COINCIDENT WITH

EARTHQUAKES AND TSUNAMIS

Yu-Ming Yang, James L. Garrison, See-Chen Lee

Purdue UniversitySchool of Aeronautics and Astronautics

701 W. Stadium Ave., West Lafayette, IN 47907-2045

ABSTRACT

Traveling ionospheric disturbances (TIDs) induced by acoustic-

gravity waves(AGWs) in the neutral atmosphere are subse-

quently observable in trans-ionospheric Global Navigation

Satellite System (GNSS) measurements. Disruptive events on

the Earth’s surface, such as earthquakes, tsunamis and large

explosions are one source of these disturbances. In this study,

we apply a wavelet method to enhance the cross-correlation

technique for detecting the presence of TIDs in dual fre-

quency IEC time series collected from GNSS networks. Data

were collected after the March 11, 2011 earthquake in Japan

and the February 27, 2010 earthquake in Chile. Both earth-

quakes produced large tsunamis. Through use of the wavelet

coherence analysis, we are able to find major a wave train,

present in the data collected from these networks, with two

dominant frequency bands.

Index Terms— ionosphere, Global Positioning System,

wavelet transforms, array signal processing

1. INTRODUCTION

Traveling ionospheric disturbances (TIDs) are the result of

interactions between acoustic-gravity waves (AGWs) and

neutral particles in the ionosphere. Extensive research on

the mechanism of ionospheric disturbances induced by at-

mospheric AGWs have been conducted (for example: [1]

[2] [3]). AGWs from solid earth events may strong enough

to transfer energy from ground/sea surface into atmosphere

and up to the F-region [4] [5][6]. These atmospheric waves

are classified into acoustic and internal gravity waves. Iono-

spheric disturbances attributed to severe weather and artificial

events such as thunder storms, tsunamis, rocket launches, nu-

clear tests and mining explosions have been observed by

different techniques [7] [8] [9]. These wave-like distur-

bances with periods of between 1 min and an hour propa-

gate at speeds that range from a few tens to few hundreds

Yu-Ming Yang was supported by the NASA Earth and Space Sciences

Fellowship, grant NNX09AN52H

m/s. Since the 1960’s, substantial observations of TIDs in-

duced by AGWs were conducted by reflection techniques

such as ionospheric sounding radar [8][9]. In contrast to

reflection techniques, GNSS senses the integrated electron

content (IEC) along the signal propagation path. Ionospheric

disturbances caused by earthquakes [10], mine blasts [11],

tsunamis [12], volcanic explosions [13], solar flares [14], and

rocket launches [15] have been observed in GNSS network

data. Methods for detecting these disturbances in GNSS

data have included statistical tests on the TEC time series

[16], the Statistical Angle-of-Arrival and Doppler Method

(SADM-GPS) [17] and cross-correlation [18],[19]. All of

them assume that a single perturbation is observable at a

time, and that the disturbance can be approximated as a

quasi-monochromatic wave. However, multiple perturbations

may be present within the same time and space. In this study,

we applied wavelet analysis to enhance the cross-correlation

technique for dealing with the disturbances not approximated

as quasi-monochromatic. In [20] and [21], tsunami induced

ionospheric disturbances were observed within three differ-

ent ranges of propagation speeds; ≈ 200m/s, ≈ 1km/s, and

≈ 3.4km/s. In this paper, we will present applications of

this new method to data collected during the March 11, 2011

Japan and February 27, 2010 Chile earthquakes and tsunamis.

2. METHODS AND DATA PROCESSING

In this research, we applied the method introduced in [18] to

estimate the propagation speed and direction of ionospheric

disturbances. This technique is developed based on the as-

sumptions of planar wave propagations, flat earth and a thin

shell ionosphere at the height of the maximum electron con-

tent. Through dividing the full network into smaller sub-

areas, it is possible to map the variation of propagation di-

rection and speed over a large area while maintaining the as-

sumption of a planar wave over each small sub-area. In our

study of the March 11, 2011 Japan earthquake and tsunami,

the full GEONET network (1235 GNSS stations) was divided

into 32 sub-areas (approximately 1◦ x 1◦ grid shown in Fig.

1131978-1-4577-1005-6/11/$26.00 ©2011 IEEE IGARSS 2011

5). Wavelet coherence analysis was then run on small sets of

data from the GNSS stations within each sub-area. Potential

ionospheric perturbations were detected by comparing the co-

herence spectrum (Fig. 1) to a threshold value. (A threshold

of 0.45 used in sub-area 2 for the 2010 Chile earthquake and

tsunami event, for example). This method is explained in the

next section.

2.1. Wavelet Analyses

The Integrated Electron Content (IEC) along the path of radio

signal propagation is defined by

IEC(t) =∫ receiver

satellite

ne(l, t)dl (1)

ne(l, t) is the electron density, which varies spatially and tem-

porally due to the electron distribution in the ionosphere, the

time-varying geometry of the GNSS line-of-sight, and the

perturbation that we wish to observe. IEC was calculated

from dual-frequency GNSS obseravations using the method

introduced in [22]. A complex-valued continuous wavelet

transform (CWT) was applied to transform each IEC time se-

ries into time-frequency space and compute the cross-wavelet

spectrum from pairs of IEC time series, for wavelet coher-

ence analysis. Findings from this analysis were then used to

design a filter bank, tuned to the frequencies at which distur-

bances are present. The filters were applied prior to the IEC

time series prior to the cross-correlation [18].

The wavelet coherence of two IEC time series IEC1(t)and IEC2(t) is defined as

R21,2(t, f) =

|W12(t, f)|2(|W1(t, f)|2|W2(t, f)|2) , (2)

where the cross-wavelet spectrum W12(t, f) is defined as

W12(t, f) = W1(t, f)W ∗2 (t, f). Wi(a, b) is the continuous

wavelet transform of an IEC time series

Wi(a, b) =∫ ∞

−∞IECi(t)ψ(

t− a

b)dt, (3)

(W ∗i (t, f) is conjugate of Wi(t, f).) In (3), ψ is a wavelet

function, a is the localized time index, and b is the wavelet

scale. We chose the Morlet wavelet, defined as a complex

exponential function ψ(t) = eiω0te−t2/2 where w0 adjusts the

time and scale resolution. Strength of the wavelet coherence

will indicate the presence, at time t, of signal structure with

frequency 1/b.Regions of high R1,2(t, f) are used as passbands in the

design of filters tuned to these individual components. As

shown by the regions of Fig. 1 exceeding the threshold,

two dominant frequency bands (0.0021-0.0083 and 0.0008-

0.0017 Hz) of relatively strong coherent structures are present

in the data from PRN20.

Fig. 1. Illustrations of wavelet coherence for PRN20 obser-

vations from sub-area 2. The coherence analysis revealed two

dominant frequency bands distributed in different time local-

ities for the potential ionospheric perturbations.

2.2. Cross-Correlation

Recent work has applied cross-correlation methods to esti-

mate the characteristics (horizontal speed and direction) of

traveling ionospheric disturbances [19] [18]. In this paper,

we used the method introduced in [18] to estimate the prop-

agation speed and direction of ionospheric disturbances from

sets of IEC time series produced from each filter in the filter

bank. These techniques are based on the assumptions of a pla-

nar wave, flat earth and single-shell ionosphere at the height

of the maximum electron content.

We sub-divided a large-scale GNSS network to map the

variation of propagation direction/speed over a large area

while keeping the assumption of a planar wave within each

small sub-area. In [18], the time-lag between pairs of fil-

tered IEC time series is assumed to equal to the travel time

of the propagation wave-front between the two ionosphere

pierce points (IPPs). As shown in Fig. 2, �rpp is the relative

range vector of �r1(t1) and �r2(t2) corresponding to the IPP

trajectories of a station pair viewing the same satellite. The

propagation direction and velocity can be estimated from a

set of Δ�rpp(t1, t1+Δtmax) via a linear least-squares method.

When applied to every pair of stations in a large and dense

network such as SCIGN or Japan GEONET, a greatly over

determined system is produced.

3. RESULTS AND ANALYSES

3.1. March 11, 2011 Japan Earthquake and Tsunami

On March 11, 2011, a Mw = 9.0 earthquake occurred

at 05:46:23 UTC near the east coast of Honshu, Japan

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Fig. 2. The geometrical relationships among the TID propa-

gation direction �k, IPP trajectories, and propagation displace-

ment between two arrival of the wave-front at two IPP loca-

tions separated by Δ�rpp.

(38.322◦N , 142.369◦E). It triggered a tsunami with run-

upreaching 10 m in Sendai.

A wavelet-based detection shows that disturbances with-

ing two different frequency bands were present in the PRN15

and PRN18 measurements. Fig.3 illustrates coherence struc-

tures in the data from these two satellites. The disturbances

within the higher frequency band (0.0021-0.0083 Hz) occur

approximately 20 min later than those in the lower frequen-

cies (0.0008-0.0017). From the estimation results, long pe-

riod (10 - 22 min) disturbances were observed in the measure-

ments from PRN 15 with propagation speeds between 195

and 354 m/s. The 95 % confidence intervals ranged between

7 m/s and 12 m/s (depending upon the sub-area).

The short period TIDs observed in the same measure-

ments from PRN15 were found to propagate with speeds in

two different ranges; 652-920 m/s and 1.5-2.3 m/s. The 95 %

confidence interval for these speed estimates ranged between

31 and 81 m/s. The 95% confidence interval in direction, for

both speed regimes, ranged between 3◦ and 8◦.

Fig. 3. Coherence structures for PRN15 (left) and PRN18

(right) measurements from GEONET for March 11, 2011

earthquake and tsunami event. The coherence analysis re-

vealed two dominant frequency bands distributed in different

time localities for the potential ionospheric perturbations.

Fig.4(a) and 4(b) show IEC maps for the short period

ionospheric disturbances. TIDs with propagation speed be-

tween 1.5-2.3 km/s were detected in the southern part of

Japan, and the disturbances with propagation speeds be-

tween 652-920 m/s were identified along the east coast of

Japan 18 min after the earthquake. Fig. 5 shows propaga-

tion directions and speeds of all observed TIDs from PRN15

measurements. Comparisons of Fig.4(a)(b) and Fig.5 show

an agreement with estimated directions, locations and occur-

rence times.

Fig. 4. Snapshots of IEC map at 06:02 and 06:08 UTC from

PRN15 and PRN18 measurements March 11, 2011 earth-

quake and tsunami event.

3.2. February 27, 2010 Chile Earthquake and Tsunami

On June 23, 2001, a Mw = 8.4 earthquake struck Peru (17.41◦

S, 72.49◦ W). This earthquake triggered a tsunami with local

run-up reaching 2-5 m. After approximate 20-22 hours, the

tsunami waves traveled through Pacific ocean and reached the

Japanese coast at 16:00-18:00 UT on June 24.

Results of our wavelet coherence analysis show distur-

bances with two different periods (2-6 min and 8-22 min)

in the PRN13 and PRN20 measurements, as shown in Fig.6.

Long period (8-20 min) disturbances were observed in the

measurements from PRN 13 and 20 with propagation speeds

between 100 and 175 m/s ( 95 % confidence interval between

7 and 20 m/s). Short period TIDs were observed in the mea-

surements from PRN13 and 20, with propagation speeds be-

tween 600-900 m/s and 2-3 km/s. (95 % confidence interval

between 21 and 101 m/s.)

4. SUMMARY

In this study, we have demonstrated that different types of

TIDs were detected in measurements from GNSS networks

through a technique that combines wavelet coherence analysis

with the cross-correlation method in [18]. Results show that

three different classifications of TIDs were identified from

the measurements of GEONET and GNSS networks in South

America for the 2011 Japan and 2010 Chile tsunami and

earthquake events. The short-period disturbances with speeds

of 600-1000 m/s were observed in the near- and far-fields

on both earthquake and tsunami events. Propagation speeds

of these short-period TIDs are in the range of infrasound

speed in the ionosphere. In the far-field of the 2010 Chile

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Fig. 5. Illustration of estimated wave propagation directions

and speeds (shown as the red and blue vectors for the short-

period and long-period TIDs respectively) from GEONET

measurements for the March 11, 2011 Japan earthquake and

tsunami event. Individual GEONET stations and four tide

gauge stations are shown as yellow dots and green squares re-

spectively. Sub-areas of stations, processed independently us-

ing the wavelet analysis and the distance cross-correlation de-

tection method from section 2 are shown by the black boxes.

The starting point of the disturbance speed vectors are located

at the estimated position of the disturbance in the ionosphere

at the time of maximum IEC amplitude.

event, these disturbances appear 30-50 minutes before the

first tsunami wave arrives. This suggests that measurements

for GNSS networks may be useful for generating tsunami

warnings.

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