Abstract
Infrasonic signals investigation plays a fundamental role for both volcano monitoring purpose and the study of the explosion dynamics. Proper and reliable detection of weak signals is a critical issue in active volcano monitoring. In particular, in volcanic acoustics, it has direct consequences in pinpointing the real number of generated events (amplitude transients), especially when they exhibit low amplitude, are close in time to each other, and/or multiple sources exist. 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 obtained by applying subspace detector on real infrasound data highlight that this method 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, the subspace detector reliability and performance, have been assessed by performing a comparison among subspace approach, cross-correlation detector and short-time average/long-time average detector. The obtained confusion matrix and extrapolated performance indices have demonstrated the potentiality, the advantages and drawbacks of the subspace method in tracking volcanic activity producing infrasound events. This method revealed to be a good compromise in detecting low-energy and very close in time events recorded during Strombolian activity.
Highlights
Amplitude transient detection plays a fundamental role in volcano monitoring, allowing counting amplitude transients, identifying amplitude and occurrence rate variations
In addition to infrasound events from the eruptive fracture, an overload continuous low frequency infrasonic tremor (∼0.6 Hz, Figure 2), whose source was located at Bocca Nuova crater (BN; red circles in Figure 1), was recorded
Subspace-based algorithm, as well as correlation detector and STA/LTA, were applied to the dataset of 30/May/2019 (13: 30–14:30), which consists of the infrasound signal recorded by EMFO station, and is characterized by infrasound events located at the eruptive fracture and infrasonic tremor located at BN (Figures 1, 2)
Summary
Amplitude transient detection plays a fundamental role in volcano monitoring, allowing counting amplitude transients, identifying amplitude and occurrence rate variations. In addition to infrasound events from the eruptive fracture, an overload continuous low frequency infrasonic tremor (∼0.6 Hz, Figure 2), whose source was located at Bocca Nuova crater (BN; red circles in Figure 1), was recorded These characteristics make the dataset useful to be used as test for an automatic detection algorithm. A few approaches have been implemented in literature (e.g., Harris, 2006; Song et al, 2014) to set this parameter, aiming to gain a compromise between detecting weak and less represented events (that is events having waveform quite different from reference template) and having low false alarm rate and possible loss of significant events. With the aim of evaluating the advantages/effectiveness of the subspace-based detector, we make a comparison with the performance of STA/LTA and the simple correlator trigger algorithms As regards the former, the detection statistic is calculated: r[n]. Detection window length chosen for subspace and correlator scan was set to 4 times the dominant period to include the entire waveform (Figure 3B)
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