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.