Characterisation and Classification of Natural Transients
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, automisation of the detection and classification
process is important. Features based on a combination of wavelet and
statistical methods proved efficient for analysis and characterisation
of transients and as input into a radial basis function network that is
trained to discriminate transients from pulse like to wave like.
[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, Geophysical
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. Dissertation.
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
A11C-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] Jang, S.R., Sun, C.T.,Functional equivalence between radial basis function
networks and fuzzy inference, IEEE Transctions on neural networks,
4(1), 156-159, 1993
[14] Jin, Y., Sendhoff, B., Extracting interpretable fuzzy rules from RBF
networks, Neural Processing Letters, 149-164, 2003
[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, Geophysical
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. Dissertation.
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
A11C-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] Jang, S.R., Sun, C.T.,Functional equivalence between radial basis function
networks and fuzzy inference, IEEE Transctions on neural networks,
4(1), 156-159, 1993
[14] Jin, Y., Sendhoff, B., Extracting interpretable fuzzy rules from RBF
networks, Neural Processing Letters, 149-164, 2003
@article{"International Journal of Earth, Energy and Environmental Sciences:57026", author = "Ernst D. Schmitter", title = "Characterisation and Classification of Natural Transients", 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, automisation of the detection and classification
process is important. Features based on a combination of wavelet and
statistical methods proved efficient for analysis and characterisation
of transients and as input into a radial basis function network that is
trained to discriminate transients from pulse like to wave like.", keywords = "transient signals, statistics, wavelets, neural networks", volume = "2", number = "1", pages = "5-4", }
