VINEDA—Volcanic INfrasound Explosions Detector Algorithm
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AuthorBueno Sánchez, Ángel; Díaz Moreno, Alejandro; Álvarez Ruiz, Isaac; Torre Vega, Ángel De La; Lamb, Oliver D.; Zuccarello, Luciano; de Angelis, Silvio
Volcanic infrasound explosionsAutomatic detectionSignal processingCharacteristic functionSub-band processing
Bueno A, Diaz-Moreno A, Álvarez I, De la Torre A, Lamb OD, Zuccarello L and De Angelis S (2019) VINEDA—Volcanic INfrasound Explosions Detector Algorithm. Front. Earth Sci. 7:335.
SponsorshipThis 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.
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-timemonitoring workflows or to catalog pre-existing databases.