Statistical Analysis of Electrostatic Turbulences over Seismic Regions T. Onishi and J.J. Berthelier Centre 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

OUTLINE 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 hiss Ω H+ ELF hiss below Cross-over freq. ELF hiss below Cross-over freq. Log(μV 2 /Hz)

Purpose of study ICE level-1 VLF spectra data with characteristic frequencies (Ω H+, 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. 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. Ω H+

Purpose of study ICE level-1 VLF spectra data with characteristic frequencies (Ω H+, 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. 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. Ω H+ 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 frequencies ---- Procedure ---- Savitzky-Golay smoothing  ”pwr_smooth” Digitalization of “pwr_smooth”  ”pwr_smooth_bin ” Detection of characteristic frequencies on “pwr_smooth” from ”pwr_smooth_bin ” “Minimum” filter in time

Automatic identification of characteristic frequencies ---- “minimum” filter in time domain ----

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

Automatic identification of characteristic frequencies 2. Smoothing in frequency domain

Automatic identification of characteristic frequencies 2. Smoothing in frequency domain : Digital filter Reduces number of candidate points and makes it easy to pick one

Automatic identification of characteristic frequencies 2. Smoothing in frequency domain

So the analytical tool is ready!

We can start the statistical study, using actual earthquake data!

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.

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. But…. We have a problem !!!.

Interferences due to the swept Langmuir probe ISL

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

Interferences due to the swept Langmuir probe ISL Burst 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 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

Interferences due to the swept Langmuir probe ISL Burst 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 3.Removal of parasite signals is very critical for the analysis of low frequency emissions (i.e. Electrostatic turbulences)  Detection and removal of parasites 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 3.Removal of parasite signals is very critical for the analysis of low frequency emissions (i.e. Electrostatic turbulences)  Detection and removal of parasites

WHY MODULATION?

Why modulation ? VLF waveform and time-average VLF power spectra obtained from VLF waveform ULF potential variations (S1 and S2) ULF potential variations (S1 and S2) VLF power spectra and waveform with Blackmann- Harris window Parasite position inside a packet

Why modulation ? VLF waveform and time-average VLF power spectra obtained from VLF waveform ULF potential variations (S1 and S2) ULF potential variations (S1 and S2) VLF power spectra and waveform with Blackmann- Harris window Parasite position inside a packet

VLF waveform and time-average VLF power spectra obtained from VLF waveform ULF potential variations (S1 and S2) ULF potential variations (S1 and S2) VLF power spectra and waveform with Blackmann- Harris window Parasite position inside a packet Why modulation ?

VLF waveform and time-average VLF power spectra obtained from VLF waveform ULF potential variations (S1 and S2) ULF potential variations (S1 and S2) VLF power spectra and waveform with Blackmann- Harris window Parasite position inside a packet Why modulation ?

VLF waveform and time-average VLF power spectra obtained from VLF waveform ULF potential variations (S1 and S2) ULF potential variations (S1 and S2) VLF power spectra and waveform with Blackmann- Harris window Parasite position inside a packet Why modulation ?

VLF waveform and time-average VLF power spectra obtained from VLF waveform ULF potential variations (S1 and S2) ULF potential variations (S1 and S2) VLF power spectra and waveform with Blackmann- Harris window Parasite position inside a packet Why modulation ?

VLF waveform and time-average VLF power spectra obtained from VLF waveform ULF potential variations (S1 and S2) ULF potential variations (S1 and S2) VLF power spectra and waveform with Blackmann- Harris window Parasite position inside a packet Why modulation ?

1.Voltage sweep of Langmuir probe is performed every ~ second. 2.Exact duration of one packet is second. 3.Relative time position voltage drop inside a packet is periodic in every 4 times. NMod(N*1.0112,0.0512) (sec)Relative position (%)

We understand why parasites show a modulation.

Now how can we remove them?

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

Removal of parasite signals from VLF waveform data and construction of clean VLF power spectra

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

After the voltage change in a potential sweep Sweep voltage is reduced from 7.6V to 3.8V from Orbit Parasite effect is also reduced. But……

After the voltage change in a potential sweep 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.

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?

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)

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) 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 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 : < M < 6.9 : < M < 5.9 : 633 Number of EQs selected is in total 664. M > 7.0 : < M < 6.9 : < M < 5.9 : 633

First attempt to statistical study of electrostatic turbulence Selection 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, < 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, < Latitude < 40, 4.No MTB activation, 5.Within 200km from the epicenter In total, 59 orbits for 32 earthquakes remained.

First attempt to statistical study of electrostatic turbulence First Result : Power Spectre at 20Hz Time (day) relative to EQ

First attempt to statistical study of electrostatic turbulence First Result : Power Spectre at 20Hz Example with 4 EQ data sets and 7 orbits

First attempt to statistical study of electrostatic turbulence First Result : Power Spectre at 20Hz Time (day) relative to EQ

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

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

Better conditions: Ap-index < 10. Magnitudes 5.0 < M and Satellite distance < 100km Time (day) relative to EQ

Conclusion and Future 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 YOU Grazie, Merci, Danke,,,