Robin S. Matoza

Capturing the Acoustic Fingerprint of Stratospheric Ash Injection

More than 100 separate incidents of interactions between aircraft and volcanic ash were documented between 1973 and 2003. Incidents on international flight paths over remote areas have resulted in engine failures and significant damage and expense to commercial airlines. To protect aircraft from volcanic ash, pilots need rapid and reliable notification of ash- generating events. A global infrasound array network, consisting of the International Monitoring System (IMS) and other national networks, has demonstrated a capability for remote detection of Vulcanian to Plinian eruptions that can inject ash into commercial aircraft cruise altitudes (approximately 12 kilometers) near the tropopause. The identification of recurring sound signatures associated with high- altitude ash injection implies that acoustic remote sensing can improve the reliability and reduce the latency of these notifications.

Coherent ambient infrasound recorded by the International Monitoring System

GEOPHYSICAL RESEARCH LETTERS, VOL. 40, doi:10.1029/2012GL054329, 2013 Coherent ambient infrasound recorded by the International Monitoring System Robin S. Matoza, 1,2 Matthieu Landes, 1 Alexis Le Pichon, 1 Lars Ceranna, 3 and David Brown 4 Received 22 October 2012; revised 4 December 2012; accepted 4 December 2012. 2011] and severe weather [Hetzer et al., 2008]. The capability of the IMS infrasonic network to detect signals of interest exhibits significant spatiotemporal variation, which is in part controlled by station-specific ambient infrasonic noise [Le Pichon et al., 2009; Green and Bowers, 2010]. [ 3 ] Each station of the IMS infrasonic network is a micro- barometer or microphone array, with at least four sensor elements spatially separated with apertures of up to a few kilometers. The arrays are designed such that wind noise will be incoherent (not spatially correlated) between elements, while real acoustic waves will be coherent (spatially correlated). [ 4 ] Wind is the dominant noise source in the frequency band 0.01–5 Hz [Walker and Hedlin, 2010]. At a given infrasound station, wind variations can account for 4 orders of magnitude dif- ference in the background noise power spectral density (PSD) at a particular frequency [Hedlin et al., 2002; Bowman et al., 2005; Brown et al., 2011]. Wind noise PSD probability varies with time of day, season, and geographic location [Bowman et al., 2005]. Previous IMS infrasound noise studies [e.g., Bowman et al., 2005] have considered raw ambient PSD probability without dis- tinguishing between incoherent wind noise and ambient coherent infrasonic signals generated by repetitive natural or anthropo- genic processes. However, it is well known that repetitive coher- ent infrasonic signals (sometimes called “clutter”) present practical constraints on identifying target infrasonic signals of interest [e.g., Evers and Haak, 2001; Hetzer and Waxler, 2009]. [ 5 ] Here we present summary statistics of coherent infra- sound recorded by the IMS network during 2005–2010, identified by systematic broadband (0.01 to 5 Hz) array pro- cessing. Our work represents a first attempt at statistically and systematically characterizing coherent ambient infra- sound recorded by the IMS. [ 1 ] The ability of the International Monitoring System (IMS) infrasound network to detect atmospheric nuclear explosions and other signals of interest is strongly dependent on station- specific ambient noise. This ambient noise includes both incoherent wind noise and real coherent infrasonic waves. Previous ambient infrasound noise models have not distinguished between incoherent and coherent components. We present a first attempt at statistically and systematically characterizing coherent infrasound recorded by the IMS. We perform broadband (0.01–5 Hz) array processing with the IMS continuous waveform archive (39 stations from 1 April 2005 to 31 December 2010) using an implementation of the Progressive Multi-Channel Correlation algorithm in log- frequency space. From these results, we estimate multi-year 5th, 50th, and 95th percentiles of the RMS pressure of coherent signals in 15 frequency bands for each station. We compare the resulting coherent infrasound models with raw power spectral density noise models, which inherently include both incoherent and coherent components. Our results indicate that IMS arrays consistently record coherent ambient infrasound across the broad frequency range from 0.01 to 5 Hz when wind noise levels permit. The multi-year averaging emphasizes continuous signals such as oceanic microbaroms, as well as persistent transient signals such as repetitive volcanic, surf, thunder, or anthropogenic activity. Systematic characterization of coherent infrasound detection is important for quantifying a station’s recording envi- ronment, signal-to-noise ratio as a function of frequency and direction, and overall performance, which all influence the detection probability of specific signals of interest. Citation: Matoza, R. S., Landes, M., Le Pichon, A., Ceranna, L., and Brown, D. (2013), Coherent ambient infrasound recorded by the International Monitoring System, Geophys. Res. Lett. 40, doi: 10.1029/2012GL054329. 2. Data and Methods [ 6 ] We consider data from 39 IMS infrasound stations (Figures 1a and 1b) from 1 April 2005 to 31 December 2010. The 39 stations represent the 42 IMS stations certified as of 1 December 2010 minus 3 stations for which problems were en- countered with metadata or data availability. Since the IMS net- work is currently under construction, data availability varies throughout the time period considered (Figure 1b). Each station consists of an array of at least four sensors with a flat response from 0.01 to 8 Hz (sampled at 20 Hz) and a sensitivity of about 0.1 mPa/count. Array aperture, geometry, and number of ele- ments (Figure 1a) varies between stations of the IMS network [Christie and Campus, 2010]; this is the principal limitation to systematic data analysis. However, in aiming to make our results as comparable as possible between stations, we perform data processing with the same parameters for all stations. [ 7 ] We perform array processing using the Progressive Multi-Channel Correlation algorithm (PMCC) [Cansi, 1995]. PMCC estimates wavefront parameters (e.g., back azimuth, 1. Introduction [ 2 ] The International Monitoring System (IMS) global infrasonic network is designed to detect atmospheric nuclear explosions anywhere on the planet [Christie and Campus, 2010]. The network also has potential application in monitoring natural hazards such as large volcanic explosions [Matoza et al., CEA/DAM/DIF, Arpajon, France. Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California, USA. BGR, Hannover, Germany. CTBTO, Vienna, Austria. Corresponding author: R. S. Matoza, Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093-0225, USA. (rmatoza@ucsd.edu) ©2012. American Geophysical Union. All Rights Reserved. /0094-8276/13/10.1029/2012GL054329,

Three-dimensional seismic velocity structure of Mauna Loa and Kilauea volcanoes in Hawaii from local seismic tomography

We present a new three-dimensional seismic velocity model of the crustal and upper mantle structure for Mauna Loa and Kilauea volcanoes in Hawaii. Our model is derived from the first-arrival times of the compressional and shear waves from about 53,000 events on and near the Island of Hawaii between 1992 and 2009 recorded by the Hawaiian Volcano Observatory stations. The Vp model generally agrees with previous studies, showing high-velocity anomalies near the calderas and rift zones and low-velocity anomalies in the fault systems. The most significant difference from previous models is in V p/Vs structure. The high-Vp and high-V p/Vs anomalies below Mauna Loa caldera are interpreted as mafic magmatic cumulates. The observed low-Vp and high-V p/Vs bodies in the Kaoiki seismic zone between 5 and 15 km depth are attributed to the underlying volcaniclastic sediments. The high-Vp and moderate- to low-Vp/Vs anomalies beneath Kilauea caldera can be explained by a combination of different mafic compositions, likely to be olivine-rich gabbro and dunite. The systematically low-Vp and low-Vp/Vs bodies in the southeast flank of Kilauea may be caused by the presence of volatiles. Another difference between this study and previous ones is the improved Vp model resolution in deeper layers, owing to the inclusion of events with large epicentral distances. The new velocity model is used to relocate the seismicity of Mauna Loa and Kilauea for improved absolute locations and ultimately to develop a high-precision earthquake catalog using waveform cross-correlation data. ©2014. American Geophysical Union. All Rights Reserved.

Infrasonic component of volcano-seismic eruption tremor

Air-ground and ground-air elastic wave coupling are key processes in the rapidly developing field of seismoacoustics and are particularly relevant for volcanoes. During a sustained explosive volcanic eruption, it is typical to record a sustained broadband signal on seismometers, termed eruption tremor. Eruption tremor is usually attributed to a subsurface seismic source process, such as the upward migration of magma and gases through the shallow conduit and vent. However, it is now known that sustained explosive volcanic eruptions also generate powerful tremor signals in the atmosphere, termed infrasonic tremor. We investigate infrasonic tremor coupling down into the ground and its contribution to the observed seismic tremor. Our methodology builds on that proposed by Ichihara et al. (2012) and involves cross-correlation, coherence, and cross-phase spectra between waveforms from nearly collocated seismic and infrasonic sensors; we apply it to datasets from Mount St. Helens, Tungurahua, and Redoubt Volcanoes.

High‐precision relocation of long‐period events beneath the summit region of Kı̄lauea Volcano, Hawai‘i, from 1986 to 2009

Long-period (0.5–5 Hz, LP) seismicity has been recorded for decades in the summit region of Kilauea Volcano, Hawai‘i, and is postulated as linked with the magma transport and shallow hydrothermal systems. To better characterize its spatiotemporal occurrence, we perform a systematic analysis of 49,030 seismic events occurring in the Kilauea summit region from January 1986 to March 2009 recorded by the ∼50-station Hawaiian Volcano Observatory permanent network. We estimate 215,437 P wave spectra, considering all events on all stations, and use a station-averaged spectral metric to consistently classify LP and non-LP seismicity. We compute high-precision relative relocations for 5327 LP events (43% of all classified LP events) using waveform cross correlation and cluster analysis with 6.4 million event pairs, combined with the source-specific station term method. The majority of intermediate-depth (5–15 km) LPs collapse to a compact volume, with remarkable source location stability over 23 years indicating a source process controlled by geological or conduit structure.

Volcanic tremor and plume height hysteresis from Pavlof Volcano, Alaska

Hearing a volcanic plume Monitoring remote eruptions—such as that of Pavlof Volcano, Alaska, in 2016—is challenging. Fee et al. found that the height of the ash plume during the Pavlof eruption could be inferred from sound waves detected by distant infrasound arrays and measurements of seismic tremor. The use of sound waves for monitoring is uncommon but well suited for remote eruptions, especially when we lack visual or satellite observations. Science, this issue p. 45 The seismic and infrasonic volcanic tremors track ash plume height from the 2016 eruption of Pavlof Volcano. The March 2016 eruption of Pavlof Volcano, Alaska, produced an ash plume that caused the cancellation of more than 100 flights in North America. The eruption generated strong tremor that was recorded by seismic and remote low-frequency acoustic (infrasound) stations, including the EarthScope Transportable Array. The relationship between the tremor amplitudes and plume height changes considerably between the waxing and waning portions of the eruption. Similar hysteresis has been observed between seismic river noise and discharge during storms, suggesting that flow and erosional processes in both rivers and volcanoes can produce irreversible structural changes that are detectable in geophysical data. We propose that the time-varying relationship at Pavlof arose from changes in the tremor source related to volcanic vent erosion. This relationship may improve estimates of volcanic emissions and characterization of eruption size and intensity.

Source mechanism of small long-period events at Mount St. Helens in July 2005 using template matching, phase-weighted stacking, and full-waveform inversion

Journal of Geophysical Research: Solid Earth RESEARCH ARTICLE 10.1002/2015JB012279 Key Points: • Source mechanism of small long-period (0.5–5 Hz) subevents at Mount St. Helens • Volumetric source consistent with shallow subhorizontal crack • Similar tiny long-period subevents likely part of source process at other volcanoes Supporting Information: • Figure S1 • Figure S2 • Figure S3 • Figures S1–S3 captions and Table S1 Correspondence to: R. S. Matoza, matoza@geol.ucsb.edu Citation: Matoza, R. S., B. A. Chouet, P. B. Dawson, P. M. Shearer, M. M. Haney, G. P. Waite, S. C. Moran, and T. D. Mikesell (2015), Source mechanism of small long-period events at Mount St. Helens in July 2005 using template matching, phase-weighted stacking, and full-waveform inversion, J. Geophys. Res. Solid Earth, 120, 6351–6364, doi:10.1002/2015JB012279. Received 11 JUN 2015 Accepted 11 AUG 2015 Accepted article online 14 AUG 2015 Published online 18 SEP 2015 Source mechanism of small long-period events at Mount St. Helens in July 2005 using template matching, phase-weighted stacking, and full-waveform inversion Robin S. Matoza 1,2 , Bernard A. Chouet 3 , Phillip B. Dawson 3 , Peter M. Shearer 1 , Matthew M. Haney 4 , Gregory P. Waite 5 , Seth C. Moran 6 , and T. Dylan Mikesell 7,8 1 Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California, USA, 2 Department of Earth Science and Earth Research Institute, University of California, Santa Barbara, California, USA, 3 U.S. Geological Survey, Volcano Science Center, Menlo Park, California, USA, 4 Alaska Volcano Observatory, U.S. Geological Survey Volcano Science Center, Anchorage, Alaska, USA, 5 Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, Michigan, USA, 6 Cascades Volcano Observatory, U.S. Geological Survey Volcano Science Center, Vancouver, Washington, USA, 7 Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA, 8 Department of Geosciences, Boise State University, Boise, Idaho, USA Abstract Long-period (LP, 0.5-5 Hz) seismicity, observed at volcanoes worldwide, is a recognized signature of unrest and eruption. Cyclic LP “drumbeating” was the characteristic seismicity accompanying the sustained dome-building phase of the 2004–2008 eruption of Mount St. Helens (MSH), WA. However, together with the LP drumbeating was a near-continuous, randomly occurring series of tiny LP seismic events (LP “subevents”), which may hold important additional information on the mechanism of seismogenesis at restless volcanoes. We employ template matching, phase-weighted stacking, and full-waveform inversion to image the source mechanism of one multiplet of these LP subevents at MSH in July 2005. The signal-to-noise ratios of the individual events are too low to produce reliable waveform inversion results, but the events are repetitive and can be stacked. We apply network-based template matching to 8 days of continuous velocity waveform data from 29 June to 7 July 2005 using a master event to detect 822 network triggers. We stack waveforms for 359 high-quality triggers at each station and component, using a combination of linear and phase-weighted stacking to produce clean stacks for use in waveform inversion. The derived source mechanism points to the volumetric oscillation ( ∼ 10 m 3 ) of a subhorizontal crack located at shallow depth ( ∼ 30 m) in an area to the south of Crater Glacier in the southern portion of the breached MSH crater. A possible excitation mechanism is the sudden condensation of metastable steam from a shallow pressurized hydrothermal system as it encounters cool meteoric water in the outer parts of the edifice, perhaps supplied from snow melt. 1. Introduction Long-period (LP, 0.5–5 Hz) seismicity, observed at volcanoes worldwide, plays a central role in our ability to assess and forecast unrest and eruption [e.g., Chouet, 1996a; McNutt, 1996; Kawakatsu and Yamamoto, 2007; Kumagai, 2009; Neuberg, 2011; Nishimura and Iguchi, 2011; Zobin, 2012; Chouet and Matoza, 2013]. The term LP seismicity includes individual transient LP events and more continuous volcanic tremor signals. Over the past several decades, numerous competing hypotheses and models have emerged to explain LP seismicity [e.g., Chouet and Matoza, 2013, and references therein]. Among these hypotheses, LP events at shallow depth ( < 2 km) in a volcanic edifice are commonly explained by the impulsive excitation and resonance of fluid-filled cracks resulting from magmatic-hydrothermal interactions [e.g., Chouet et al., 1994; Chouet, 1996a; Kumagai et al., 2002b; Nakano et al., 2003; Nakano and Kumagai, 2005a; Waite et al., 2008; Matoza and Chouet, 2010; Arciniega-Ceballos et al., 2012; Maeda et al., 2013]. ©2015. American Geophysical Union. All Rights Reserved. MATOZA ET AL. The dome-building phase of the 2004–2008 eruption of Mount St. Helens (MSH) produced millions of repetitive seismic events with long-period codas and slowly evolving waveforms [Moran et al., 2008; Thelen et al., 2008]. Many of these events occurred with such precise regularity that they were termed “drumbeats” [Moran et al., 2008], a phenomenon that has been observed at several other volcanoes [e.g., Neuberg, 2000; MOUNT ST. HELENS SMALL LP SOURCE

Seismic Envelope‐Based Detection and Location of Ground‐Coupled Airwaves from Volcanoes in Alaska

Abstract Volcanic explosions and other infrasonic sources frequently produce acoustic waves that are recorded by seismometers. Here we explore multiple techniques to detect, locate, and characterize ground‐coupled airwaves (GCA) on volcano seismic networks in Alaska. GCA waveforms are typically incoherent between stations, thus we use envelope‐based techniques in our analyses. For distant sources and planar waves, we use f ‐ k beamforming to estimate back azimuth and trace velocity parameters. For spherical waves originating within the network, we use two related time difference of arrival (TDOA) methods to detect and localize the source. We investigate a modified envelope function to enhance the signal‐to‐noise ratio and emphasize both high energies and energy contrasts within a spectrogram. We apply these methods to recent eruptions from Cleveland, Veniaminof, and Pavlof Volcanoes, Alaska. Array processing of GCA from Cleveland Volcano on 4 May 2013 produces robust detection and wave characterization. Our modified envelopes substantially improve the short‐term average/long‐term average ratios, enhancing explosion detection. We detect GCA within both the Veniaminof and Pavlof networks from the 2007 and 2013–2014 activity, indicating repeated volcanic explosions. Event clustering and forward modeling suggests that high‐resolution localization is possible for GCA on typical volcano seismic networks. These results indicate that GCA can be used to help detect, locate, characterize, and monitor volcanic eruptions, particularly in difficult‐to‐monitor regions. We have implemented these GCA detection algorithms into our operational volcano‐monitoring algorithms at the Alaska Volcano Observatory.

Capturing the Acoustic Radiation Pattern of Strombolian Eruptions using Infrasound Sensors Aboard a Tethered Aerostat, Yasur Volcano, Vanuatu

We obtained an unprecedented view of the acoustic radiation from persistent strombolian volcanic explosions at Yasur volcano, Vanuatu from the deployment of infrasound sensors attached to a tethered aerostat. While traditional ground-based infrasound arrays may sample only a small portion of the eruption pressure wavefield, we were able to densely sample angular ranges of ~200o in azimuth and ~50o in take-off angle by placing the aerostat at 38 tethered loiter positions around the active vent. The airborne data joined contemporaneously collected ground-based infrasound and video recordings over the period 29 July to 1 August 2016. We observe a persistent variation in the acoustic radiation pattern with average eastward-directed root-mean squared pressures more than 2 times larger than in other directions. The observed radiation pattern may be related to both path effects from the crater walls, and source directionality.

Local, regional, and remote seismo-acoustic observations of the April 2015 VEI 4 eruption of Calbuco volcano, Chile

The two major explosive phases of the 22–23 April 2015 eruption of Calbuco volcano, Chile produced powerful seismicity and infrasound. The eruption was recorded on seismo-acoustic stations out to 1,540 km and on 5 stations (IS02, IS08, IS09, IS27, and IS49) of the International Monitoring System (IMS) infrasound network at distances from 1,525 to 5,122 km. The remote IMS infrasound stations provide an accurate explosion chronology consistent with the regional and local seismo-acoustic data, and with previous studies of lightning and plume observations. We use the IMS network to detect and locate the eruption signals using a brute-force, grid-search, cross-bearings approach. After incorporating azimuth deviation corrections from stratospheric cross-winds using 3D ray-tracing, the estimated source location is 172 km from true. This case study highlights the significant capability of the IMS infrasound network to provide automated detection, characterization, and timing estimates of global explosive volcanic activity. Augmenting the IMS with regional seismo-acoustic networks will dramatically enhance volcanic signal detection, reduce latency, and improve discrimination capability.