Uncertainty in Detection of Volcanic Activity Using Infrasound Arrays: Examples From Mt. Etna, Italy

Silvio De Angelis,Luciano Zuccarello,Luciano Zuccarello,John J Lyons,Alejandro Diaz-Moreno,Matthew M Haney,Matthew M Haney,David Fee,David Fee,Aaron Wech,Aaron Wech

doi:10.3389/feart.2020.00169

Abstract

The injection of gas and pyroclastic material from volcanic vents into the atmosphere is a prolific source of acoustic waves. Infrasound arrays offer efficient, cost-effective and near real-time solutions to track the rate and intensity of surface activity at volcanoes. Here, we present a simple framework for the analysis of acoustic array data, based on least-squares beamforming, that allows to evaluate the direction and speed of propagation of acoustic waves between source and array; we also propose a new scheme, based on error propagation theory, for the quantitative assessment of uncertainty on these measurements. We the apply the algorithms to data collected by two 6-element infrasound arrays deployed at Mt. Etna during the period July-August 2019. Our results demonstrate that the use of two infrasound arrays allowed detecting and tracking acoustic sources from multiple craters and active vents associated with degassing and ash-rich explosions, vigorous and frequent Strombolian activity, opening of new eruptive fractures and emplacement of lava flows. Finally, we discuss the potential use of metrics based on infrasound array analyses to inform eruption monitoring operations and early warning at volcanoes characterized by episodic intensification of activity.

Highlights

Over the past two decades infrasound has become an increasingly popular tool to monitor volcanoes (e.g., Fee and Matoza, 2013; McNutt et al, 2015)
Data collected by two infrasound arrays, ENEA and ENCR, were pre-processed by applying a band-pass (2-pole, zero-phase) filter within the frequency band of interest, which for the activity recorded during our experiment was between 0.7 and 15 Hz
The mean of the normalized crosscorrelation maxima (MCCM) within each data window reflects the level of coherence between signals across the array; only values of DOA and trace velocity corresponding to MCCM > 0.5 were considered to ensure that inversion was performed for coherent signals across the arrays

Summary

Introduction

Over the past two decades infrasound has become an increasingly popular tool to monitor volcanoes (e.g., Fee and Matoza, 2013; McNutt et al, 2015). Microphones are deployed either as individual sensors within distributed networks or as smallaperture arrays (i.e., tight clusters of three or more instruments at 50–150 m from one another) at distances of between few hundreds of meters to within 20 km of an active volcanic vent. Ripepe et al (2018) reported on over a decade of acoustic monitoring at Mt. Etna (Italy) with two small aperture infrasound arrays, demonstrating the ability to detect eruption onset in real-time, discriminate source position, and dispatch rapid notifications of the ongoing activity to local civil protection authorities. Helens (USA) from October 2004 to March 2005; they discussed the key role of infrasound in separating surface from deeper processes and in identifying the timing and assessing the magnitude of eruptive events

Methods

Results

Conclusion