Joint analysis of infrasound and seismic signals by cross wavelet transform: detection of Mt. Etna explosive activity

The prompt detection of explosive volcanic activity
is crucial since this kind of activity can release copious
amounts of volcanic ash and gases into the atmosphere, causing
severe dangers to aviation. In this work, we show how the
joint analysis of seismic and infrasonic data by wavelet transform
coherence (WTC) can be useful to detect explosive activity,
significantly enhancing its recognition that is normally
done by video cameras and thermal sensors. Indeed, the efficiency
of these sensors can be reduced (or inhibited) in the
case of poor visibility due to clouds or gas plumes. In particular,
we calculated the root mean square (RMS) of seismic
and infrasonic signals recorded at Mt. Etna during 2011. This
interval was characterised by several episodes of lava fountains,
accompanied by lava effusion, and minor strombolian
activities. WTC analysis showed significantly high values of
coherence between seismic and infrasonic RMS during explosive
activity, with infrasonic and seismic series in phase
with each other, hence proving to be sensitive to both weak
and strong explosive activity. The WTC capability of automatically
detecting explosive activity was compared with
the potential of detection methods based on fixed thresholds
of seismic and infrasonic RMS. Finally, we also calculated
the cross correlation function between seismic and infrasonic
signals, which showed that the wave types causing
such seismo-acoustic relationship are mainly incident seismic
and infrasonic waves, likely with a common source.