AbstractErosion, hydrothermal activity, and magmatism at volcanoes can cause large and unexpected mass wasting events. Large fluidized debris flows have occurred within the past 6000 yr at Mount Adams, Washington, and present a hazard to communities downstream. In August 2017, we began a pilot experiment to investigate the potential of infrasound arrays for detecting and tracking debris flows at Mount Adams. We deployed a telemetered four-element infrasound array (BEAR, 85 m aperture), ~11 km from a geologically unstable area where mass wasting has repeatedly originated. We present a preliminary analysis of BEAR data, representing a survey of the ambient infrasound and noise environment at this quiescent stratovolcano. Array processing reveals near continuous and persistent infrasound signals arriving from the direction of Mount Adams, which we hypothesize are fluvial sounds from the steep drainages on the southwest flank. We interpret observed fluctuations in the detectability of these signals as resulting from a combination of (1) wind-noise variations at the array, (2) changes in local infrasound propagation conditions associated with atmospheric boundary layer variability, and (3) changing water flow speeds and volumes in the channels due to freezing, thawing, and precipitation events. Suspected mass movement events during the study period are small (volumes <105  m3 and durations <2 min), with one of five visually confirmed events detected infrasonically at BEAR. We locate this small event, which satellite imagery suggests was a glacial avalanche, using three additional temporary arrays operating for five days in August 2018. Events large enough to threaten downstream communities would likely produce stronger infrasonic signals detectable at BEAR. In complement to recent literature demonstrating the potential for infrasonic detection of volcano mass movements (Allstadt et al., 2018), this study highlights the practical and computational challenges involved in identifying signals of interest in the expected noisy background environment of volcanic topography and drainages.

SUMMARY Infrasound sensors are deployed in a variety of spatial configurations and scales for geophysical monitoring, including networks of single sensors and networks of multisensor infrasound arrays. Infrasound signal detection strategies exploiting these data commonly make use of intersensor correlation and coherence (array processing, multichannel correlation); network-based tracking of signal features (e.g. reverse time migration); or a combination of these such as backazimuth cross-bearings for multiple arrays. Single-sensor trace-based denoising techniques offer significant potential to improve all of these various infrasound data processing strategies, but have not previously been investigated in detail. Single-sensor denoising represents a pre-processing step that could reduce the effects of ambient infrasound and wind noise in infrasound signal association and location workflows. We systematically investigate the utility of a range of single-sensor denoising methods for infrasound data processing, including noise gating, non-negative matrix factorization, and data-adaptive Wiener filtering. For the data testbed, we use the relatively dense regional infrasound network in Alaska, which records a high rate of volcanic eruptions with signals varying in power, duration, and waveform and spectral character. We primarily use data from the 2016–2017 Bogoslof volcanic eruption, which included multiple explosions, and synthetics. The Bogoslof volcanic sequence provides an opportunity to investigate regional infrasound detection, association, and location for a set of real sources with varying source spectra subject to anisotropic atmospheric propagation and varying noise levels (both incoherent wind noise and coherent ambient infrasound, primarily microbaroms). We illustrate the advantages and disadvantages of the different denoising methods in categories such as event detection, waveform distortion, the need for manual data labelling, and computational cost. For all approaches, denoising generally performs better for signals with higher signal-to-noise ratios and with less spectral and temporal overlap between signals and noise. Microbaroms are the most globally pervasive and repetitive coherent ambient infrasound noise source, with such noise often referred to as clutter or interference. We find that denoising offers significant potential for microbarom clutter reduction. Single-channel denoising of microbaroms prior to standard array processing enhances both the quantity and bandwidth of detectable volcanic events. We find that reduction of incoherent wind noise is more challenging using the denoising methods we investigate; thus, station hardware (wind noise reduction systems) and site selection remain critical and cannot be replaced by currently available digital denoising methodologies. Overall, we find that adding single-channel denoising as a component in the processing workflow can benefit a variety of infrasound signal detection, association, and location schemes. The denoising methods can also isolate the noise itself, with utility in statistically characterizing ambient infrasound noise.

Infrasound data are routinely used to detect and locate volcanic and other explosions, using both arrays and single sensor networks. However, at local distances (<15 km) topography often complicates acoustic propagation, resulting in inaccurate acoustic travel times leading to biased source locations when assuming straight-line propagation. Here we present a new method, termed Reverse Time Migration-Finite-Difference Time Domain (RTM-FDTD), that integrates numerical modeling into the standard RTM back-projection process. Travel time information is computed across the entire potential source grid via FDTD modeling to incorporate the effects of topography. The waveforms are then back-projected and stacked at each grid point, with the stack maximum corresponding to the likely source. We apply our method to three volcanoes with different network configurations, source-receiver distances, and topography. At Yasur Volcano, Vanuatu, RTM-FDTD locates explosions within ∼20 m of the source and differentiates between multiple vents. RTM-FDTD produces a more accurate location for the two Yasur subcraters than standard RTM and doubles the number of detected events. At Sakurajima Volcano, Japan, RTM-FDTD locates the source within 50 m of the active vent despite notable topographic blocking. The RTM-FDTD location is similar to that from the Time Reversal Mirror method, but is more computationally efficient. Lastly, at Shishaldin Volcano, Alaska, RTM and RTM-FDTD both produce realistic source locations (<50 m) for ground-coupled airwaves recorded on a four-station seismic network. We show that RTM is an effective method to detect and locate infrasonic sources across a variety of scenarios, and by integrating numerical modeling, RTM-FDTD produces more accurate source locations and increases the detection capability.