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IUGG XXIV General Assembly July 2-13, 2007 Perugia, Italy
(S) - IASPEI - International Association of Seismology and Physics of the Earth's
Interior
JSS007 Oral Presentation 1958
Electromagnetic monitoring of La Fournaise volcano (Indian Ocean): Fuzzy
pattern recognition algorithms
Prof. Alexey Gvishiani
Geophysical Center RAS Russian Academy of Sciences IASPEI
J. Zlotnicki, A. Gvishiani, J.L. Le Moul, M.Rodkin, S. Agayan, Sh. Bogoutdinov
Numerous studies investigate electromagnetic (EM) precursors of volcanic eruptions (and earthquakes).
Active volcanoes are excellent natural laboratories for such electric and magnetic studies. Indeed: (1)
usually, volcanoes are not highly populated, so the level of anthropogenic noise is low; (2) the area to
be monitored is typically small, till 10 km from the volcano, (as compared to several hundreds km long
seismic faults), so a limited number of data sensors (≤ 15) is required to install sufficiently dense
network; (3) volcanic activity generally produces a large number events (seismicity, ground
deformation, EM, geochemistry, etc) which can be recorded during a short time period. This study is
devoted to the basaltic, 2640 m high, La Fournaise volcano, located in the South-eastern part of Runion
Island (France, Indian Ocean). The more or less regular volcanic activity allows detailing electric,
magnetic, and EM signals associated to the eruptive phases. Precursory total magnetic force (TMF) and
electric (ES) signals to eruptions have been already reported. These signals can appear a few weeks
before the outburst, and the amplitude can reach a few nT for TMF and some hundreds of mV/km for
ES. Signals are enhanced when the magma migrates towards the ground surface in the last hundreds
meters. These signals are low frequency events, from a few minutes to several weeks or months.
Nowadays, we investigate the EM signals in higher frequency domains. A sharp increase in the sampling
rate of data, up to several kHz, takes place. Manual time series processing by a visual expertise
becomes more and more difficult, not enough objective, and time consuming. Thus, the problem of
automation of visual data processing becomes an important one. We introduce an alternative approach
to visual analysis of data with the building of algorithms based on fuzzy-logics and statistics. These
algorithms carry out the morphological examination of time series, and identify pre-supposed signals in
successive segments of the EM records. Both, a sample of a real record or some ideal pattern
formulated by the expert is used to formulate the recognition procedure. The application of algorithms
allows to find out anomalies in electric and magnetic data, and to discriminate between anomalies of
different type corresponding to diverse physical processes (heavy rains, changes in the hydrothermal
activity, etc.). The algorithms can be used in monitoring systems for automation and for revealing
characteristic morphological sequences in huge data sets.
Keywords: fuzzy logic algorithms, data processing, volcanic activity