A PRE-STUDY OF A PRE-STUDY OF AUTOMATIC DETECTION AUTOMATIC DETECTION OF LEP EVENTS ON THE OF LEP EVENTS ON THE

VLF SİGNALSVLF SİGNALS

VLF waves are guided within the spherical waveguide formed VLF waves are guided within the spherical waveguide formed between the earth and the ionosphere. between the earth and the ionosphere.

Produced by the fraction of the VLF energy radiated by lightning Produced by the fraction of the VLF energy radiated by lightning discharges that escapes into the magnetosphere and propagates as discharges that escapes into the magnetosphere and propagates as a whistler-mode wave. a whistler-mode wave.

The whistler-mode wave interacts with trapped radiation belt The whistler-mode wave interacts with trapped radiation belt electrons through cyclotron resonant pitch angle scattering, electrons through cyclotron resonant pitch angle scattering, causing some of those close to the loss cone to precipitate and causing some of those close to the loss cone to precipitate and produce secondary ionization.produce secondary ionization.

Precipitating energetic electrons (50 to 500 keV) cause Precipitating energetic electrons (50 to 500 keV) cause secondary ionization via impact with atmospheric constituents, secondary ionization via impact with atmospheric constituents, altering the conductivity of the D region of the ionosphere. altering the conductivity of the D region of the ionosphere.

This ionospheric disturbance in turn changes the amplitude This ionospheric disturbance in turn changes the amplitude and/or phase of VLF transmitter signals propagating in the earth-and/or phase of VLF transmitter signals propagating in the earth-ionosphere waveguide on great circle paths that pass through or ionosphere waveguide on great circle paths that pass through or near the localized disturbances. near the localized disturbances.

An example of an LEP event showing the temporal characteristicsAn example of an LEP event showing the temporal characteristics

LEP MeasurablesLEP Measurables

Event Perturbation Magnitude (ΔA) of the VLF signal refers to the change in amplitude, measured in dB, from the ambient levels prior to the event, to the maximum (or minimum) levels reached during the event,

Onset Delay (Δt) refers to the time delay between the causative lightning discharge and the onset of the event. The impulsive spheric associated with the lightning discharge contains energy over a wide range of frequencies and is often visible as a sharp peak in many of the narrowband channels monitored.

Onset Duration (td) refers to the length of time over which the signal amplitude continues to change up to its maximum value (either negative or positive), and corresponds to the temporal duration of the precipitation burst .The onset duration is defined as the time between the onset of the event and the end of the increase in perturbation magnitude

Recovery Time (tr) is the time at which the signal recovers back to the amplitude it would have exhibited in the absence of the perturbation, and it signifies the time at which the ionosphere recovers back to its ambient profile

MeasurableMeasurable QualificationQualification

Perturbation magnitudePerturbation magnitude AA0.50.5

Onset delayOnset delay 200 ms 200 ms t t 2.5s 2.5s

Event durationEvent duration 0.5 s 0.5 s t tdd 5s 5s

Recovery timeRecovery time 10 s 10 s t trr 100 s 100 s

MULTI-RESOLUTION WAVELET DECOMPOSITION

The Wavelet Transform (WT) provides a time-frequency representation of the signal.

It was developed to overcome the short coming of the Short Time Fourier Transform (STFT), which can also be used to analyze non-stationary signals.

While STFT gives a constant resolution at all frequencies, the WT uses multi-resolution technique by which different frequencies are analyzed with different resolutions.

Discrete Wavelet Transform (DWT) can be regarded as a continuous time wavelet decomposition sampled at different frequencies at every level or stage. It is easy to implement and reduces the computation time and resources required.

The DWT functions at level m and time location tm can be expressed as;

mm

mmm

tttxtd

2)()(

This multi-resolution analysis enables us to analyze the signal in different frequency bands; therefore, we could observe any transient in time domain as well as in frequency domain.

Wavelets are a family of basis functions, well-localized in both the time and frequency domains.

They have a compact support, which means that they differ from zero only in a limited time domain.

This property makes the wavelet very appropriate to represent the different features of a signal, especially sharp signals and discontinuities.

At each level, the high pass filter (impulse response, h[n]) produces detail information, dm, while the low pass filter (impulse

response, g[n]) associated with scaling function produces coarse approximations, am, as expressed in below equations.

d[n]=x[n]h[n] a[n]=x[n]g[n]

FLOW CHART OF THE PROPOSED ALGORITHMFLOW CHART OF THE PROPOSED ALGORITHM

Wavelet Filter:symlet Order: 3

Detail Coef. cD1 Approximation Coef.

cA3

Wavelet Multi-resolution

decomposition at level 3

Step 1: Determine probable interval using

specific LEP events such as Perturbation

magnitude, Onset delay, Event duration

Recovery time on the filtered data (cA3)

Step 2:Use detail coefficient for

discontinuity analysis and compare data obtained at step 1

Determine as LEP events comparing the results obtained at Step 1 and

step 2

Draw these determined interval as LEP events and

save as a text file

We have used Symlet Wavelet function family with order three. The results of the Wavelet multi-resolution analysis for level 3 and 7 are shown figure below. At level 7 some details have disappeared in the signal.

Approximation Coefficients (cA3) at Level 3 and Detail Coefficient at levels(1-7).

Signal between Receiver Signal between Receiver and Transmitter and Transmitter

Number of events Number of events determined by eyedetermined by eye

Number of events Number of events founded by proposed founded by proposed

algorithmalgorithm

BO-NAABO-NAA 2828 6161

BO-NAUBO-NAU 1010 239239

WS-NAAWS-NAA 1515 1818

WS-NAUWS-NAU 2121 324324

LV-NAALV-NAA 55 5858

LV-NAULV-NAU 55 338338

CH-NAACH-NAA 2424 101101

CH-NAUCH-NAU 1414 198198

The proposed algorithm found nearly 280 events between 4 - 6x104 for this signal. This part of this signal is very noisy and have many sudden peaks near each other.

FUTURE WORKSFUTURE WORKS

To develop the algorithm for fixing the To develop the algorithm for fixing the events one by oneevents one by one

To separate the events as Early/Fast and To separate the events as Early/Fast and LEP automatically.LEP automatically.

To find the location of eventsTo find the location of events automatically. automatically.

We thank to We thank to STANFORD STANFORD

STARLAB VSTARLAB VLFLF GROUPGROUP

for vlffor vlf data data

Thank you for Thank you for Listening meListening me