Application of Subspace-Based Detection Algorithm to Infrasound Signals in Volcanic Areas

Safe triggering process of weak waveforms is a critical issue in active volcano monitoring. In particular, in volcanic acoustics, it has direct consequences in pinpointing the real number of generated discrete events, especially when they exhibit low amplitude, are close in time to each other, and/or multiple sources exist. Infrasonic signals investigation plays a fundamental role for both monitoring purpose and the study of the explosion dynamics. To accomplish this task, several algorithms have been proposed in literature; in particular, to overcome limitations of classical approaches such as short-time average/long-time average and cross-correlation detector, in this paper a subspace-based detection technique has been implemented. Results highlight that subspace detector allows sensitive detection of lower energy events. This method is based on a projection of a sliding window of signal buffer onto a signal subspace that spans a collection of reference signals, representing similar waveforms from a particular infrasound source. A critical point is related to subspace design. Here, an empirical procedure has been applied to build the signal subspace from a set of reference waveforms (templates). In addition, in order to determine detectors parameters, such as subspace dimension and detection threshold, even in presence of overlapped noise such as infrasonic tremor, a statistical analysis of noise has been carried out. Finally, to assess subspace detector reliability and performance, a comparison among subspace approach, cross-correlation detector and short-time average/long-time average detector, by means of confusion matrix and performance indices extrapolation, was performed.