Statistical Analysis of Electrostatic Turbulences over Seismic Regions T. Onishi and J.J. Berthelier Centre d'Etude des Environnements Terrestre et Planétaires

Statistical Analysis of Electrostatic Turbulences over Statistical Analysis of Electrostatic Turbulences over Seismic RegionsSeismic Regions

Statistical Analysis of Electrostatic Turbulences over Statistical Analysis of Electrostatic Turbulences over Seismic RegionsSeismic Regions

T. Onishi and J.J. BerthelierCentre d'Etude des Environnements Terrestre et Planétaires (CETP)

Saint-Maur, France

EuroPlaNet Strategic Workshop on

Earthquakes: Ground-based and Space Observations

Graz, Austria, 1-2 June, 2007

OUTLINEOUTLINE

• Purpose of this study

• Frequency Classification of power spectra of ELF/VLF emissions

• Interferences from other instruments (ISL)

• Selection of Earthquake data

• Preliminary results.

• Conclusion and Future Work

TYPICAL CHARACTERISTICS OF WAVES DETECTED BY ICE

Electrostatic

Turbulance

Electrostatic

Turbulance

Ordinary ELF hissOrdinary ELF hiss

ΩH+

ELF hiss below

Cross-over freq.

ELF hiss below

Cross-over freq.

Log(μV2/Hz)

Purpose of studyPurpose of study

ICE level-1 VLF spectra data with characteristic frequencies (ΩH+, etc…) calculated from IAP data.


Purpose: characterize the shape of the frequency spectra to determine emissions with different origin, propagation condition etc…

Track the characteristics of these emissions to search for changes linked with seismic activity.



ΩH+

Purpose of studyPurpose of study







ΩH+

It is easy to do it on just one spectral plot.

But, there are ten of thousands of them.

It is easy to do it on just one spectral plot.

But, there are ten of thousands of them.

Automatic identification of characteristic frequenciesAutomatic identification of characteristic frequencies ---- Procedure -------- Procedure ----

Savitzky-Golay smoothing ”pwr_smooth”Savitzky-Golay smoothing ”pwr_smooth”

Digitalization of “pwr_smooth” ”pwr_smooth_bin”Digitalization of “pwr_smooth” ”pwr_smooth_bin”

Detection of characteristic frequencies on “pwr_smooth”

from ”pwr_smooth_bin”Detection of characteristic frequencies on “pwr_smooth”

from ”pwr_smooth_bin”

“Minimum” filter in time “Minimum” filter in time

Automatic identification of characteristic frequenciesAutomatic identification of characteristic frequencies

---- “minimum” filter in time domain -------- “minimum” filter in time domain ----

Automatic identification of characteristic frequenciesAutomatic identification of characteristic frequencies 2. Smoothing in frequency domain : Savitzky-Golay filter2. Smoothing in frequency domain : Savitzky-Golay filter

Automatic identification of characteristic frequenciesAutomatic identification of characteristic frequencies 2. Smoothing in frequency domain2. Smoothing in frequency domain

Automatic identification of characteristic frequenciesAutomatic identification of characteristic frequencies 2. Smoothing in frequency domain : Digital filter2. Smoothing in frequency domain : Digital filter

Reduces number of candidate points and makes it easy to pick one

Automatic identification of characteristic frequenciesAutomatic identification of characteristic frequencies 2. Smoothing in frequency domain2. Smoothing in frequency domain

So the analytical tool is ready!


We can start the statistical study,

using actual earthquake data!




To begin with, let us see the electrostatic turbulence at low frequency.




To begin with, let us see the electrostatic turbulence at low frequency.

But…. We have a problem !!!.

Interferences due to the swept Langmuir probe ISLInterferences due to the swept Langmuir probe ISL


Burst modeBurst mode

1. Parasites are present in the form of modulation Why?





2. Knowledge of the waveform of a parasite is not enough to separate parasites from the natural emissions. (Nonlinear effects) Only Burst Mode



)cos(2

)exp(

)exp(

22NPPNPNNP

NNN

PPP

AAAAP

iAW

iAW





3. Removal of parasite signals is very critical for the analysis of low frequency emissions (i.e. Electrostatic turbulences) Detection and removal of parasites



3. Removal of parasite signals is very critical for the analysis of low frequency emissions (i.e. Electrostatic turbulences) Detection and removal of parasites

)cos(2

)exp(

)exp(

22NPPNPNNP

NNN

PPP

AAAAP

iAW

iAW

WHY MODULATION?

Why

mod

ulat

ion

?W

hy m

odul

atio

n ?

VLF waveform and time-average


VLF power spectra obtained from VLF waveform


ULF potential variations

(S1 and S2)


(S1 and S2)

VLF power spectra and waveform with Blackmann-Harris window


Parasite position inside a packet


Why

mod

ulat

ion

?W

hy m

odul

atio

n ?






(S1 and S2)


(S1 and S2)










(S1 and S2)


(S1 and S2)





Why

mod

ulat

ion

?W

hy m

odul

atio

n ?






(S1 and S2)


(S1 and S2)





Why

mod

ulat

ion

?W

hy m

odul

atio

n ?






(S1 and S2)


(S1 and S2)





Why

mod

ulat

ion

?W

hy m

odul

atio

n ?






(S1 and S2)


(S1 and S2)





Why

mod

ulat

ion

?W

hy m

odul

atio

n ?






(S1 and S2)


(S1 and S2)





Why

mod

ulat

ion

?W

hy m

odul

atio

n ?

Why modulation ?Why modulation ?

1. Voltage sweep of Langmuir probe is performed every ~1.0112 second.

2. Exact duration of one packet is 0.0512 second.

3. Relative time position voltage drop inside a packet is periodic in every 4 times.

N Mod(N*1.0112,0.0512) (sec) Relative position (%)

1 0.038399976 74.999955

2 0.025599953 49.999909

3 0.012799930 24.999864

4 0.051199906 99.999818

5 0.038399883 74.999773

6 0.025599860 49.999727

7 0.012799837 24.999682

8 0.051199812 99.999636

9 0.038399789 74.999591

10 0.0255998 49.9995

We understand why parasites show a modulation.

We understand why parasites show a modulation.

Now how can we remove them?

How to detect a parasite positionHow to detect a parasite position

Peak of the sweep is detected first. A point where a potential decreases by 1/e are detected on both S1. Parasite position is defined as the mid point of these two.

Peak of the sweep is detected first. A point where a potential decreases by 1/e are detected on both S1. Parasite position is defined as the mid point of these two.

Potential peak position is not precise enough to define the corresponding potential drop.

Potential peak position is not precise enough to define the corresponding potential drop.

Removal of parasite signals from VLF waveform data and Removal of parasite signals from VLF waveform data and

construction of clean VLF power spectraconstruction of clean VLF power spectra





40 spectra averaged with parasites40 spectra averaged with parasites

40 spectra averaged except those with parasites40 spectra averaged except those with parasites

After the voltage change in a potential sweepAfter the voltage change in a potential sweep

Sweep voltage is reduced from 7.6V to 3.8V from Orbit 2154.0. Parasite effect is also reduced. But……

Sweep voltage is reduced from 7.6V to 3.8V from Orbit 2154.0. Parasite effect is also reduced. But……

After the voltage change in a potential sweepAfter the voltage change in a potential sweep

But with different contour levels, the parasite effect is evident.

But with different contour levels, the parasite effect is evident.

Parasite effects may be small.

But changes due to EQs may be smaller.

Such tiny changes can be masked by parasites.

Therefore, parasite removal is important for low frequency analysis!

Parasite effects may be small.

But changes due to EQs may be smaller.

Such tiny changes can be masked by parasites.

Therefore, parasite removal is important for low frequency analysis!

Now we have a tool and clean data.


We can start the statistical study with actual EQ data.



But… which EQ data can we use?




A bunch of earthquakes often occurs in the same region

and at about the same time (few days difference)




A bunch of earthquakes often occurs in the same region

and at about the same time (few days difference)

If we should find an anomaly before one earthquake,

How do we know if it is a precursor to the earthquake

Or a post-seismic phenomenon of another earthquake?

Earthquake selectionEarthquake selection

Defining M as the magnitude of the main earthquake, following conditions are checked in EQ selection.

1.All EQs of magnitude smaller than M-2 are ignored.

2.No EQs within the dobrovolny distance of the main EQ in the preceding 10 days.

Defining M as the magnitude of the main earthquake, following conditions are checked in EQ selection.

1.All EQs of magnitude smaller than M-2 are ignored.

2.No EQs within the dobrovolny distance of the main EQ in the preceding 10 days.

Number of EQs selected is in total 664.

• M > 7.0 : 4

• 6.0 < M < 6.9 : 27

• 5.0 < M < 5.9 : 633

Number of EQs selected is in total 664.

• M > 7.0 : 4

• 6.0 < M < 6.9 : 27

• 5.0 < M < 5.9 : 633

First attempt to statistical study of electrostatic turbulenceFirst attempt to statistical study of electrostatic turbulenceSelection CriteriaSelection Criteria

1. Earthquakes of magnitude 5.4 ≤ M ≤ 5.9 are used. There are 169 earthquakes.

2. Orbit data are used if …

1. In Burst mode,

2. ap-index ≤ 15,

3. -40 < Latitude < 40,

4. No MTB activation,

5. Within 200km from the epicenter

1. Earthquakes of magnitude 5.4 ≤ M ≤ 5.9 are used. There are 169 earthquakes.

2. Orbit data are used if …

1. In Burst mode,

2. ap-index ≤ 15,

3. -40 < Latitude < 40,

4. No MTB activation,

5. Within 200km from the epicenter

In total, 59 orbits for 32 earthquakes remained.In total, 59 orbits for 32 earthquakes remained.

First attempt to statistical study of electrostatic turbulenceFirst attempt to statistical study of electrostatic turbulenceFirst Result : Power Spectre at 20HzFirst Result : Power Spectre at 20Hz

Time (day) relative to EQ


Example with 4 EQ data sets and 7 orbitsExample with 4 EQ data sets and 7 orbits



First attempt to statistical study of electrostatic turbulenceFirst attempt to statistical study of electrostatic turbulenceFirst Result : Frequency for log(power) = -2.0First Result : Frequency for log(power) = -2.0


First attempt to statistical study of electrostatic turbulence First attempt to statistical study of electrostatic turbulence First Result : Frequency for log(power) = -2.0First Result : Frequency for log(power) = -2.0


Better conditions: Ap-index < 10. Magnitudes 5.0 < M and Better conditions: Ap-index < 10. Magnitudes 5.0 < M and Satellite distance < 100kmSatellite distance < 100km


ConclusionConclusion and and Future WorkFuture Work

• An analysis tool is ready for statistical study on physical phenomena.

• Characteristics of parasite signals have been understood.• Although only in the Burst mode, parasite signals have been

removed.• Earthquake data are carefully selected to avoid “pre or post”

ambiguity of an anomaly.• First result is obtained.• If it should be real, power spectra increases at low frequencies by

the order of –2 db around 60Hz. (It can be just a coincidence…)• Data of plasma density and energy from IAP is being analyzed. • More earthquake data are being added.• Correlation with other parameters such as magnetic local time

should be checked.• Once an anomaly related to the seismic event is confirmed, a

reverse analysis should be performed. (Earthquake-Anomaly ……..)

THANK YOUGrazie, Merci, Danke,,,

Documents

Statistical Analysis of Electrostatic Turbulences over Seismic Regions T. Onishi and J.J. Berthelier Centre d'Etude des Environnements Terrestre et Planétaires