Infrasound Event Detection Using the Progressive Multi-Channel Correlation Algorithm

  • Yves Cansi
  • Alexis Le Pichon

In the case of a network, the morphology of the signal is often very different from one sensor to another and the measure of the propagation parameters is usually derived from the set of arrival times by an inversion process, as described by Husebye (1969). In the case of a mini-array, we can take advantage of the high degree of signal coherence to compute arrival time differences using classical techniques of signal processing theory. Husebye’s method may then be used to evaluate the propagation parameters from the derived set of arrival time differences.

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Refrences

  1. Cansi, Y., An automatic seismic event processing for detection and location: the PMCC method, Geophys. Res. Lett., 22, 1021–1024, 1995.CrossRefADSGoogle Scholar
  2. Cansi, Y. and Y. Klinger, An automated data processing method for mini-arrays, CSEM/EMSC European-Mediterranean Seismological Centre, NewsLetter 11,1021–1024, 1997.Google Scholar
  3. Capon, J., High resolution frequency wavenumber spectrum analysis, Proc. IEEE, 57, 1408–1418, 1969.CrossRefGoogle Scholar
  4. Husebye, E. S., Direct measurement of dT/dΔ, BSSA, 1969. 59, 2, 717–727.Google Scholar
  5. Le Pichon, A., E. Blanc, D. Drob, S. Lambotte, J. X. Dessa, M. Lardy, P. Bani and S. Vergniolle, Infrasound monitoring of volcanoes to probe high-altitude winds, J. Geophys. Res., 110, D13106, doi:10.1029/2004 JD005587 (2005).Google Scholar
  6. Le Pichon, A., J. Guilbert, M. Vallèe, J. X. Dessa and M. Ulziibat, Infrasonic imaging of the Kunlun Mountains during the great 2001 China earthquake, Geophys. Res. Lett., 1029/2003GL017581, 2003.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Yves Cansi
    • 1
  • Alexis Le Pichon
    • 1
  1. 1.CEA/DASE/LDGFrance

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