Analysing and Classifying VLF Transients

Authors: Ernst D. Schmitter

Abstract:

Monitoring lightning electromagnetic pulses (sferics) and other terrestrial as well as extraterrestrial transient radiation signals is of considerable interest for practical and theoretical purposes in astro- and geophysics as well as meteorology. Managing a continuous flow of data, automation of the analysis and classification process is important. Features based on a combination of wavelet and statistical methods proved efficient for this task and serve as input into a radial basis function network that is trained to discriminate transient shapes from pulse like to wave like. We concentrate on signals in the Very Low Frequency (VLF, 3 -30 kHz) range in this paper, but the developed methods are independent of this specific choice.

Keywords: Transient signals, statistics, wavelets, neural networks

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056510

References:

[1] Betz H.-D., Oettinger W. P., Schmidt K., Wirz M., Modern Lightning Detection and Implementation of a New Network in Germany, General Assembly EGU, Wien/ Austria, April 2005
[2] Betz H.-D., Eisert B. , Oettinger W. P., Four year experience with an atmospherics-based automatic early warning system for thunderstorms, Proc. 26th Int. Conference on Lightning Protection (ICLP), Cracow/ Poland, 91-95, ISBN 83-910689-5-1, 2002
[3] Schienle A., Stark R., Walter B., Vaitl D., Kulzer R., Effects of Low-Frequency Magnetic Fields on Electrocordical Activity in Humans: A Sferic Simulation Study, International Journal of Neuroscience, 90, 21¬36, 1997.
[4] Tzanis, A., Vallianatos, F., A critical review of Electric Earthquake Precursors, Annali di Geofisica, 44/2, 429-460, 2001
[5] Konstantanaras, A., Varley, M.R., Vallianatos, F., Collins, G., Holifield, P., A neuro-fuzzy approach to the reliable recognition of electric earthquake precursors, Natural Hazards and Earth Sciences 4:641-646, 2004
[6] Steinbach, P., Lichtenberger, J., Ferencz, Cs., Case studies of possible earthquake related perturbations on narrow band VLF time series, Geo¬physical research abstracts, Vol. 5, 10946, 2003
[7] Aschwanden, M., Kliem B., Schwarz U., Kurths, J., Wavelet Analysis of Solar Flare Hard X-rays, The Astrophysical Journal, 505:941, 1998, October 1
[8] Cummer, S.A.,Lightning and ionospheric remote sensing using VLF/ELF radio atmospherics, Dissertation. Stanford University. August 1997
[9] Reising, S.C., Remote sensing of the electrodynamic coupling between thunderstorm systems and the mesosphere / lower ionosphere. Disserta¬tion. Stanford University. June 1998
[10] Mushtak V.C., Lowenfels D.F., Williams E.R., Stewart M.F., Full ELF/VLF Bandwitdh Observations of Lightning in the Earth-Ionosphere Waveguide, American Geophysical Union, Fall Meeting 2002, abstract Al1C-0111
[11] Press W.H., Teukolsky S.A., Vetterling W.T., Flannery B.P., Numerical Recipes in C, Cambridge University Press, 1992
[12] Haykin, S., Neural networks, Prentice Hall, 1999
[13] Takagi, T., Sugeno, M., Fuzzy identification of systems and its applica¬tions to modeling and control, IEEE Transactions on Systems, Man and Cybernetics, vol. 15, 116-132, 1985
[14] Jang, S.R., Sun, C.T.,Functional equivalence between radial basis func¬tion networks and fuzzy inference, IEEE Transctions on neural networks, 4(1), 156-159, 1993
[15] Jin, Y., Sendhoff, B., Extracting interpretable fuzzy rules from RBF networks, Neural Processing Letters, 149-164, 2003
[16] Nelles, 0., Nonlinear System Identification, Springer, Berlin, 2001