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

 VLF waves are guided within the spherical waveguide formed between the earth and the ionosphere.  Produced by the fraction of the VLF energy radiated by lightning discharges that escapes into the magnetosphere and propagates as a whistler-mode wave.  The whistler-mode wave interacts with trapped radiation belt electrons through cyclotron resonant pitch angle scattering, causing some of those close to the loss cone to precipitate and produce secondary ionization.  Precipitating energetic electrons (50 to 500 keV) cause secondary ionization via impact with atmospheric constituents, altering the conductivity of the D region of the ionosphere.  This ionospheric disturbance in turn changes the amplitude and/or phase of VLF transmitter signals propagating in the earth- ionosphere waveguide on great circle paths that pass through or near the localized disturbances.

An example of an LEP event showing the temporal characteristics

LEP 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 (t d ) 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 (t r ) 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

MeasurableQualification Perturbation magnitude  A  0.5 Onset delay 200 ms   t  2.5s Event duration 0.5 s  t d  5s Recovery time 10 s  t r  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;  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, d m, while the low pass filter (impulse response, g[n]) associated with scaling function produces coarse approximations, a m, as expressed in below equations. d[n]=x[n]h[n] a[n]=x[n]g[n]

FLOW CHART OF THE PROPOSED ALGORITHM

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 and Transmitter Number of events determined by eye Number of events founded by proposed algorithm BO-NAA2861 BO-NAU10239 WS-NAA1518 WS-NAU21324 LV-NAA558 LV-NAU5338 CH-NAA24101 CH-NAU14198

The proposed algorithm found nearly 280 events between 4 - 6x10 4 for this signal. This part of this signal is very noisy and have many sudden peaks near each other.

FUTURE WORKS  To develop the algorithm for fixing the events one by one  To separate the events as Early/Fast and LEP automatically.  To find the location of events automatically.

We thank to STANFORD STARLAB VLF GROUP for vlf data

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