VINEDA—Volcanic INfrasound Explosions Detector Algorithm

Infrasound is an increasingly popular tool for volcano monitoring, providing insights of the
unrest by detecting and characterizing acoustic waves produced by volcanic processes,
such as explosions, degassing, rockfalls, and lahars. Efficient event detection from large
infrasound databases gathered in volcanic settings relies on the availability of robust and
automated workflows. While numerous triggering algorithms for event detection have
been proposed in the past, they mostly focus on applications to seismological data.
Analyses of acoustic infrasound for signal detection is often performed manually or by
application of the traditional short-term average/long-term average (STA/LTA) algorithms,
which have shown limitations when applied in volcanic environments, or more generally
to signals with poor signal-to-noise ratios. Here, we present a new algorithm specifically
designed for automated detection of volcanic explosions from acoustic infrasound data
streams. The algorithm is based on the characterization of the shape of the explosion
signals, their duration, and frequency content. The algorithm combines noise reduction
techniques with automatic feature extraction in order to allow confident detection of
signals affected by non-stationary noise. We have benchmarked the performances of the
new detector by comparison with both the STA/LTA algorithm and human analysts, with
encouraging results. In this manuscript, we present our algorithm and make its software
implementation available to other potential users. This algorithm has potential to either be
implemented in near real-time monitoring workflows or to catalog pre-existing databases.

This research was partially funded by KNOWAVES TEC2015-
68752 (MINECO/FEDER), by NERC Grant NE/P00105X/1,
by Spanish research grant MECD Jose Castillejo CAS17/00154
and by VOLCANOWAVES European Union’s Horizon
2020 Research and Innovation Programme Under the Marie
Sklodowska-Curie Grant Agreement no 798480.

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