Infrasonic tracking of large bubble bursts and ash venting at Erebus Volcano, Antarctica

Journal of Volcanology and Geothermal Research 177 (2008) 661–672

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Journal of Volcanology and Geothermal Research j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / j v o l g e o r e s

Infrasonic tracking of large bubble bursts and ash venting at Erebus Volcano, Antarctica Kyle R. Jones, Jeffrey B. Johnson ⁎, Rick Aster, Philip R. Kyle, W.C. McIntosh Department of Earth and Environmental Science, New Mexico Institute of Mining and Technology, Socorro, NM 87801, United States

a r t i c l e

i n f o

Article history: Received 12 October 2006 Accepted 1 February 2008 Available online 26 February 2008 Keywords: infrasound Strombolian eruption Erebus Volcano lava lake

a b s t r a c t Explosive degassing at Erebus Volcano produces infrasound that can be used to locate, characterize, and quantify eruptive activity from multiple vents. We use a three element distributed microphone network to pinpoint eruption sources and track the activity at the prominent vents through time. Eruptive mechanisms for both source types are analyzed in conjunction with the telemetered time-synced video imagery. We identify two commonly active vents corresponding to the large (often N 10-m diameter) bubble bursts at the free surface of a persistent phonolitic lava lake (‘Ray Lake’), and the less frequent ash-rich eruptions from a constricted vent (‘Active Vent’) located ∼ 80 m from the lava lake. During a 3-month study interval from 6 January to 13 April 2006 we identified and mapped more than 350 eruptive sources from the lava lake and 20 sources from the ash vent. Lava lake events are characterized by high-amplitude infrasonic transients that reflect rapid (less than a few s) acceleration and rupture of magma bubble films followed by an explosion of pressurized gases. Precise infrasonic localization of the lava lake events to accuracies of a few m indicates variable bubble source locations across a 40 by 50-m region spanning the lava lake. Spatial variability is corroborated by the video data. In contrast, degassing from the ash vent produces longer-duration (tens of s), lower amplitude transients that reflect diminished impulsivity and an extended degassing duration, features that are corroborated by video. Because infrasound networks can operate continuously in all weather conditions and during both diurnal and seasonal polar darkness, and are easily incorporated into automatic processing, they significantly contribute to the completeness and quantification of eruption catalogues for Erebus. © 2008 Elsevier B.V. All rights reserved.

1. Introduction Low frequency acoustic signals from many active volcanoes have been analyzed to understand their activity and eruptive behavior (e.g., Vergniolle and Brandeis, 1994; Firstov and Kravchenko, 1996; Yamasato, 1998; Garces et al., 1999; Hagerty et al., 2000; Johnson and Lees, 2000). Volcano acoustic studies are typically conducted in the infrasonic (b20 Hz) bandwidth because volcanoes tend to produce the most intense sounds at around ∼1 to 5 Hz and this bandwidth is amenable to conventional seismic data acquisition hardware and sample rates (Johnson, 2003). At the most basic level, integrated seismo-acoustic records enable differentiation of explosion earthquakes (either transient or tremor signals) from deeper sub-surface seismicity (traditional long period earthquakes or tremor) that are unassociated with eruptions. More involved seismo-acoustic studies may be used to determine source mechanisms and energy release and precisely locate events in three dimensions (e.g., Vergniolle and Brandeis, 1994; Garces et al., 1998; Yamasato, 1998; Ripepe et al., 2001; Johnson et al., 2006; Johnson, 2007).

⁎ Corresponding author. Tel.: +1 505 835 5634; fax: +1 505 835 6436. E-mail address: [email protected] (J.B. Johnson). 0377-0273/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jvolgeores.2008.02.001

Digital seismic and acoustic observations from temporary and permanent geophysical networks on Erebus Volcano have enabled nearly continuous assessment of the state of unrest since the 1980s (Dibble et al., 1984; Skov, 1994; Aster et al., 2004). Studies of eruption mechanisms, shallow volcanic structure and fluid transport have been largely realized through seismic techniques (e.g., Dibble et al., 1994; Kaminuma, 1994; Aster et al., 2003, 2008-this volume), while interpretation of seismicity associated with Erebus eruptive events has been facilitated by co-analysis of radiated infrasound (e.g., Dibble et al., 1984; 1989). The intense impulsive infrasound radiated during Erebus lava lake explosions has also been utilized to better understand the structure of the Erebus lava lake (Rowe et al., 2000), the controls on energy partitioning between radiated seismic and acoustic phases (Johnson and Aster, 2005), and to quantify eruption intensity and material flux (Johnson et al., 2003, 2004, 2008-this volume). This paper employs a network of infrasonic pressure transducers deployed at close range to the vent (hundreds of m) to map and track activity at Erebus Volcano to better understand long-term eruptive chronology. Events are located with the acoustic network to a resolution of a few m following techniques that were previously used to distinguish infrasound sources at the multiple-vent Stromboli volcano (Johnson, 2005). In the Stromboli study, the azimuthal distribution of the acoustic pressure transducers was sufficient so that vents could be

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distinguished using a grid search approach. Such vent localization techniques present a complementary strategy to recent studies at Stromboli (Ripepe and Marchetti, 2002), Kilauea (Garces et al., 2003), and at Mount St. Helens (Matoza et al., 2007), where arrays of pressure sensors have been deployed at distances of up to many km from active vents and are used to identify source back-azimuth and to produce high signal-to-noise beams. 2. Erebus Volcano Mount Erebus, Antarctica (77.55° S, 167.17° E) is a large stratovolcano (3794 m) that is renowned for its long-lived (decades or longer) active convecting phonolitic lava lake, which is commonly referred to as ‘Ray Lake’. Though Erebus eruptions were first reported as early as 1841 (e.g., Kyle, 1994), the ‘Ray Lake” has been observed regularly only since 1972 (Giggenbach et al., 1973). Typical eruptive activity from this lava lake is characterized by short-duration Strombolian explosions, in which one (or more) large pressurized bubbles breach the surface, burst, and eject sections of the bubble film as ballistics (Rowe et al., 2000). Short-period seismic monitoring, which began in 1980, has traditionally been used to catalog and otherwise characterize eruptive events (Dibble et al., 1984). This network has been enhanced with long-term and seasonal broadband sensor deployments integrated with other geophysical monitoring as part of the Mount Erebus Volcano Observatory (MEVO). Currently, MEVO maintains a multi-disciplinary monitoring program that employs a video camera and a network of seismometers, infrared sensors, infrasonic sensors, GPS, and tiltmeters operating continuously as the power systems allow (Aster et al., 2004). These data are complemented by yearly visits to the volcano during the austral summer, during which additional seasonal studies and observations are conducted (e.g., Calkins et al., 2008-this volume; Gerst et al., 2008-this volume). Integrated geophysical/volcanological study has greatly improved our understanding of eruption phenomenology and mechanism, particularly at the Erebus lava lake. Morphology of the ‘Ray Lake’ (size and location) and frequency of Strombolian-type explosive events have evolved in the last century, but were not routinely documented until regular scientific visits commenced in the 1970s (Giggenbach et al., 1973). During the last three decades this convecting/exploding magma reservoir in the northeast corner of the Inner Crater has been a consistent feature even though it lost its open-conduit appearance in 1984 due to temporary burial during a heightened 4-month eruptive phase (Kyle et al., 1990;

Kaminuma, 1994). During this period of elevated explosivity, bombs up to 10-m diameter were ejected out over the crater rim. In general, Erebus Strombolian activity ebbs and grows over long cycles (months to years), including a very notable hiatus between August 2002 and January 2004 during which essentially no eruptions occurred (Fig. 1). Overall, most large Strombolian ballistics during the past three decades have been confined to the crater, but a renewed period of enhanced activity, in which explosions were powerful enough to erupt bombs over the crater rim, began in 2005 and coincides with this study period (January 2006 to April 2006). During this latest paroxysm the lava lake has continued to evolve, forming a well-developed spatter levee and achieving its 2005–2006 diameter of ∼ 40–50 m. This current estimated dimension falls in between the 5 and 60-m range cited in previous studies for other observational periods (Kyle, 1977; Kyle et al., 1990). At present Strombolian eruption frequency is relatively similar to the 2–6 events occurring daily from 1972–1984 (Dibble et al., 1984). In addition to lava lake eruptions, other activity has been observed from peripheral vents within the ∼150-m diameter Inner Crater. Between 2000 and 2001, small vents produced numerous ash-rich eruptions manifested by long-duration (tens of s) ‘jetting’ of gas and ballistics (Aster et al., 2003). The observed behavior of these vents, named the ‘Active Vent’ and ‘Werner Vent’, contrasts markedly with the short-duration (bfew s) explosive lava lake bubble bursts. It has been speculated that a severely constricted conduit/vent may be responsible for the differences in eruptive behavior relative to ‘Ray Lake’ eruptions. From time to time short (∼25 m) lava flows have also originated from ‘Werner Vent’ when it developed as a smaller lava lake ‘skylight’ that is often referred to as ‘Werner Lake’ (Aster et al., 2003; Calkins et al., 2008this volume). The evolving morphologies of these vents as well as their sub-surface plumbing and extent to which their conduits may be linked in a sub-surface reservoir are currently not well constrained. 3. Data collection Infrasonic monitoring has been conducted at Erebus since the 1980s, beginning with a single pressure transducer at station E1S, approximately 800 m from the lava lake (Dibble et al., 1984) Since 1999 the acoustic network has been expanded and, as of January 2005, consisted of seven sensors at five distinct sites. Several pressure sensors are co-located with the MEVO network broadband seismometers. The networked infrasonic sensors are distributed azimuthally about the active crater and range in distance from about 320 to 820 m from the lava lake. Two of the infrasound sites have

Fig. 1. Collective ‘Ray Lake’ and ‘Active Vent’ eruptions per month from January 1992 to September 2006 (plotted with logarithmic scale). The catalog is overwhelmingly dominated by lava lake explosions. Note that incompleteness exists due to variable reporting criteria and/or instrumentation outages.

K.R. Jones et al. / Journal of Volcanology and Geothermal Research 177 (2008) 661–672 Table 1 Summary of the infrasound stations functioning at Erebus used in this study. Easting and northing coordinates (in m) are referenced to the crosshairs on the map (Fig. 2). Approximated transit times and distances from ‘Ray Lake’ are calculated using a nominal lava lake source elevation of 3550 m and horizontal coordinate (355 m easting and 265 m northing) determined by the mean value of the ‘Ray Lake’ locations Station name

Latitude (easting)

Longitude (northing)

Elevation (m)

Transit distance/time from center of lava lake

E1S

167.1398° (− 254 m) 167.1708° (491 m) 167.1559° (131 m)

−77.5305° (−151 m) −77.5286° (59 m) −77.5261° (337 m)

3659

822 m/2.57 s

3766

328 m/1.03 s

3774

325 m/1.02 s

RAY SHK

microphones co-installed, in duplicate, because two different types of sensors (pressure transducers (PT) and electret condenser elements) have different fidelity and dynamic range. For this study data are

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analyzed from three azimuthally distinct sites (SHK, RAY, E1S) where identical high dynamic range PTs are installed (Table 1; Fig. 2). These three PT sites are used exclusively here because, unlike the electret condenser elements, their pressure records remain unclipped for almost all events. The PTs are amplified, temperature-compensated Honeywell transducers (model DC001NDR5) selected for their wide dynamic range of +/−125 Pa and flat frequency response down to zero frequency when operated in differential mode. To enable pressure equilibration for barometric changes, which may exceed 103 Pa and would saturate the sensor, one of the differential pressure ports was fitted with a 50 μm, 2 cm-long capillary tubing, creating a mechanical high-pass filter with a 3 dB corner at 0.01 Hz. The map image used in this study and displayed in Fig. 2a is derived from a high-resolution (2 m) digital elevation model acquired during the austral summer of 2001–2002 from NASA's Airborne Topographic Mapper (ATM) laser altimetry system (Csatho et al.,

Fig. 2. a) Sun-illuminated digital elevation model image showing detail of summit area at Erebus Volcano and 2006 vents. Labeled PT stations (SHK, RAY, E1S) are shown as triangles. Video field-of-view is shown as a dark cone originating at SHK and directed towards the ESE. Image “crosshairs” are located at 167.15° E and − 77.53° S. Gray patch in lower right is area of no data. b) Grayscale video still image showing the view from SHK. The ‘Ray Lake’ is bright region to the left and the ‘Active Vent’ is in eruption at upper right. Nomenclature for these vents originates from P. Kyle and C. Oppenheimer (pers. comm., 2007).

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Table 2 Erebus event identification for different cross-correlation thresholds (held constant for all three-station pairs) Cross-correlation threshold

Total triggers

Lava lake events

Active vent events

‘Non-events’

0.2 0.3 0.4 0.5

1056 468 405 363

366 358 331 311

26 21 17 10

664 89 57 42

2005). The image was processed by NASA and the Ohio State University from LiDAR data utilizing artificial sun illumination from the southwest. The three PT station locations have been geo-located on the map using individual station GPS coordinates (Table 1). GPS-stamped time series data from the seismo-acoustic network are continuously transmitted via FreeWave™ spread spectrum modems to the base observatory in McMurdo ∼ 35 km away. Data are archived at McMurdo and New Mexico Tech at 40 samples/s using a Scream/Earthworm system (Aster et al., 2004) and are also automatically uploaded to the IRIS Consortium Data Management Center. Major breaks in continuous data archival are most often associated with power drops at the remote station sites, occurring during times when the solar panels no longer receive solar insolation (mid-April to mid-August) or when remote hardware is damaged in large storms. In 2006, signal from stations RAY and SHK was lost due to power outages

in the middle of April. The three PT sites were installed and jointly operating in the first days of January. As such, we have near-continuous records from these three sites between January 5th and April 26th. Intermittent power dropouts at RAY and SHK starting on April 14th and additional corrupted data on individual stations on March 15 and 16 result in a complete dataset of approximately 98 days. Infrasound records were further interpreted in association with digital video, which was acquired until early February. During this period when crater-filling fume was minimal volcanic eruptions were seen at both the ‘Ray Lake' and ‘Active Vent'. This camera observed much of the Inner Crater floor and has a field of view of about 300 m along the southeast crater wall (Fig. 2a,b). Digital video acquired at ∼30 fps and with 640 × 480 resolution is transmitted to McMurdo where it is continuously archived. During January 6th to January 31st approximately 33 clear-weather windowed video events are available for low fume lava lake explosions. 4. Analysis Infrasound recorded at three distinct pressure transducers is used here to identify and locate eruptive events occurring in the Erebus Inner Crater. Though we use only three sensors in this study, the outlined methodology can be generalized for larger networks and tailored for detecting diverse infrasound sources (e.g., Johnson et al., 2006). A statistical summary of the characterized events and evaluation of the detection effectiveness is provided in this section.

Fig. 3. Cross-correlation triggers shown for two events for stations SHK and E1S: a) ‘Ray Lake’ explosion and b) an ‘Active Vent’ event. Grey scale indicates the degree of correlation as a function of timing (x-axis) and phase lag (y-axis). Peak correlation values are 0.88 for the lava lake and 0.68 for the ash vent. Detail of each event shows the correlated pressure transients.

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A more detailed analysis of ‘Ray Lake’ events and the implication of their spatial variability are addressed in the Discussion section. 4.1. Event identification Event identification is performed using 24-h continuous data traces from each of the three pressure transducers. Each trace has been band-pass filtered between 0.25 and 10 Hz to reduce electronic and wind-related noise. A cross-correlation program similar to the one implemented at Stromboli by Johnson (2005) then analyzes the continuous data traces from the three PTs to identify correlated signal. For the results presented here successive 2048 sample trace windows (∼ 51 s; 50% overlap) are compared for the three pairs of stations (E1S and SHK; SHK and RAY; RAY and E1S). With these parameters, 3375 acoustic correlation calculations are made during each 24-h period. For each comparison window, normalized cross-correlation coefficients must exceed a minimum threshold value to be considered correlated signal. In our analysis, different threshold correlation values were tested to maximize event identification while minimizing false triggers. We have discovered that very low threshold correlation

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criteria (i.e., b=0.2) result in excessive identification of spurious events (i.e., events that could not be mapped to any known Erebus vent) while threshold criteria in excess of 0.5 tend to overlook many events of interest, which are contaminated by varying degrees of wind noise. For these reasons our correlation threshold has been fixed to 0.3 for all stations. A comparison of total triggers for different correlation thresholds is presented in Table 2. Cross-correlation triggers must also pass a consistency test to be identified as an actual event. For our three-station geometry the calculated lag times between two different station pairs should transitively define the lag time between the last station pair. Thus, to be characterized as an event, the sum of the three lags (ΔTE1S, SHK, ΔTSHK, RAY, and ΔTRAY, E1S) must approach zero. In our notation ΔTa,b is defined as the time shift between correlated phases at stations a and b (i.e., Ta −Tb). If the sum of these three time lags is less than or equal to an arbitrary consistency threshold K (i.e., |ΔTE1S, SHK + ΔTSHK, RAY + ΔTRAY, E1S| ≤K) then the corresponding data is windowed as a correlated and consistent event and extracted for further analysis (e.g., for localization). In this study K is conservatively set to 3 samples to account for potential cumulative error in lag time picks. Multiple correlated, consistent

Fig. 4. a) Infrasound time series from stations E1S, SHK, and RAY for the entire 98-day study interval. Cross-correlation comparisons of the three-station pairs are shown in the three panels between corresponding time series plots. Open circles indicate triggered lag-consistent infrasound sources (Eq. (1)). A two-day data gap is evident around Julian day 70.

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detections occurring during consecutive windows are considered parts of a single discrete event. Fig. 3 shows examples of correlated, consistent events that were subsequently identified as a ‘Ray Lake’ and an ‘Active Vent’ source. The parameters used in this study were assigned to maximize the identification of real events and minimize false triggers. However, many of the variables, including band-pass corner frequencies, comparison window and overlap window lengths, minimum crosscorrelation threshold values, and consistency threshold should be tailored to optimize event detection for networks of variable size and distance from the vent. A sensitivity analysis for some of these parameters is outlined in Johnson (2005) for Stromboli Volcano based upon its volcanic activity, sensor placement, and background noise levels. In some cases it is appropriate to identify correlated signal in multiple discrete bandwidths (e.g., Garces and Hetzer, 2002; Garces et al., 2003). At Erebus, the event detection algorithm is performed for a relatively broad frequency band, which succeeded nearly as effectively at identifying events as multiple tests in restricted bandwidths. The results of the cross-correlation event identification algorithm are

summarized in Table 2 and Fig. 4, which shows a 98-day infrasound time series (from the three stations) along with triggered crosscorrelation phase lags for 358 lava lake and 21 ash-venting events. The same cross-correlation procedure may be extended for event detection in infrasound networks with more than 3 stations (n N 3). In this case, the number of unique station pairs (m) subject to calculation of lag time delays is

n
1 P

i. Any permutation of three different stations (A,

i¼1

B, and C) with correlated lag delays above threshold are considered robust if their summed delays are consistent, i.e.:

jDTA;B þ DTB;C þ DTC;A j V K:

ð1Þ

Correlated lag delays, which are found to be systematically inconsistent with other lag delays, must be discredited and cannot used for source localization. If only a subset of the station pairs is found to be consistent then the lag time delays eligible for source localization will be less than the total number of station pairs (m).

Fig. 5. a) Map of 385 located events (filled circles) identified during the 3-month study interval (January 6–April 13) for source elevation fixed at 3550 m and homogeneous sound speed of 320 m/s. The 150-m radius search grid (with 5-m spacing) is shown as a stippled region centered on the northeast Inner Crater. The ‘Ray Lake’ exhibits the large diffuse collection of event triggers and the ‘Active Vent’ is represented by a small cluster 80 m to the south. Six other triggered ‘non-events’ are also plotted. b) Detail of event locations shown in panel a. c) Detail of event locations for source elevation fixed at 3570 m and homogeneous sound speed of 320 m/s (corresponding to −19 °C). d) Detail of event locations for source elevation fixed at 3550 m and homogeneous sound of 310 m/s (corresponding to − 34 °C).

K.R. Jones et al. / Journal of Volcanology and Geothermal Research 177 (2008) 661–672

4.2. Event location Because each triggered event for the Erebus dataset is defined as being consistent to within a small value of K (Eq. (1)), the three acoustic delay lag times are uniquely described by only two acoustic delay lag times. These acoustic delay times may be quickly scanned to get an initial idea of temporal and spatial diversity of sources. For the consistent lag times presented in Fig. 4 at least two primary spatial groupings can be immediately identified (e.g., compare lag times of SHK relative to the other two stations). As with the infrasound source localization carried out at Stromboli by Johnson (2005), Erebus events are located by grid search rather than through an iterative inversion because the source region is small and amenable to quick computation. For Erebus sources a horizontal grid with resolution of 5 m is used to precisely locate lag-consistent infrasound sources in the Inner Crater, a roughly circular area of radius 150 m. To perform the grid search, root-mean-squared average timing residuals for the three-station pairs are found over the 2821 Inner Crater grid points. The timing residual for a specific station pair (e.g., between stations A and B) is defined here as ΔTA,B − ΔDA,B / c where ΔDA, B is taken to be the difference in acoustic propagation distances (DA − DB) between a specific grid coordinate and stations A and B. The intrinsic atmospheric velocity c is fixed at a reasonable sound velocity (320 m/s) for expected summer Erebus temperatures (−19 °C). The root-meansquared timing error for the three consistent station pairs is then RMSðx; y; zÞ¼

1 3

      !!1=2 DDNKB;RAY 2 DDRAY;E1S 2 DDE1S;NKB 2 þ DTSHK;RAY
þ DTRAY;E1S
DTE1S;SHK
c c c

ð2Þ and the source location is found where the sum, RMS(x,y,z), is minimized. In this analysis stations SHK and RAY are on the crater rim and DSHK and DRAY are calculated as straight-line distance between a grid point given by x, y, and z and the station coordinate such that DSHK ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðxSHK
xÞ2 þðySHK
yÞ2 þðzSHK
zÞ2

ð3Þ

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðxRAY
xÞ2 þðyRAY
yÞ2 þðzRAY
zÞ2 :

ð4Þ

and DRAY ¼

Here the x, y, and z GPS coordinates for the three stations are listed in Table 1.

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A straight-line propagation approximation is less suitable for DE1S because, unlike SHK and RAY on the crater rim, E1S is located substantially below the summit crater rim and the acoustic ray path is not line of sight. Consequently, the propagation distance is approximated as two straight-line segments corresponding to the source-toseptum and septum-to-station paths, where the septum is defined as the low point on the SW crater rim.

DE1S

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ¼ ðxE1S
xÞ2 þðyE1S
yÞ2
300 þðzE1S þ 100
zÞ2 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi þ 3002 þ 1002 :

ð5Þ

Here the horizontal and vertical septum-to-station distances are set at 300 and 100 m respectively. At Erebus the acoustic source corresponding to the root-meansquare timing residual is ultimately found for only two variables, northing (y) and easting (x), because the elevation coordinate (z) is uniquely mapped as a function of the horizontal location, z(x,y) along the Inner Crater floor. For the purposes of this study z is fixed at the estimated average elevation of the Inner Crater floor. Results of the grid search for 385 triggered consistent infrasound events are plotted in Fig. 5 as an overlay to the digital elevation model-derived map image. Since the Inner Crater floor elevation is uneven and not precisely known, source locations have been mapped for presumed crater elevations of 3550 m (Fig. 5a,b) and 3570 m (Fig. 5c), the latter elevation corresponding to 2006 estimates made of the ‘Ray Lake’ by laser distance measurement (A. Gerst, pers. comm., 2006). Source locations are also mapped for variations in intrinsic sound propagation velocity to demonstrate the spatial sensitivity in located sources for a 10 m/s variation in sound speed (compare Fig. 5b and d). For this relatively extreme variation in sound speed the mapped source translations are on the order of 20 m. Sources were sought only within a ∼150-m radius circular grid space that coincides with the Erebus Inner Crater. This geographic filtering discredited 83 of 89 ‘non-events’ because no clear RMS minima exists within the Inner Crater search area for these events. Excepting 6 remaining ‘non-events’, Inner Crater source locations appear to originate primarily from two distinct clusters. The larger cluster of 358 events coincides with the ‘Ray Lake’, which is manifested as an approximate 50-m diameter circular depression in the 2001–2002 LiDAR image. The secondary cluster of 21 sources is spatially consistent with a smaller vent feature that is also evident on

Fig. 6. Three-month time series showing cumulative lava lake eruption events detected with both the cross-correlation and MEVO methods.

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Upon visual inspection of the windowed data, each of these events was determined to be spurious, caused by electronic dropouts or correlated background noise that were present on all three microphone traces. It is important to note that vent localization in this study was conducted using the minimum number of PTs (n = 3; m = 3) and that sources could only be mapped because source elevation was fixed as a function of x and y. In future studies improved volcano source localization at Erebus and elsewhere can be achieved with more than three distributed infrasound recording stations. In this case Eq. (2) can be extended to accommodate n N 3 stations. For p correlated, consistent station pairs the generalized residual then becomes 1X p i¼1 p

RMSðx; y; zÞ ¼

  !1=2 DDi 2 DTi
: c

ð6Þ

A minimum of four stations giving four unique station pair lag time delays is sufficient to constrain the infrasound sources in 4 dimensions (e.g., x, y, z, and t). 5. Discussion 5.1. Event location

Fig. 7. Histograms showing the size distribution of ‘Ray Lake’ events that were detected by a) the MEVO detection algorithm and b) the infrasonic cross-correlation algorithm.

the map image. These 21 epicenters are ∼ 80 m to the south of the lava lake center and are coincident with the ‘Active Vent’. The 6 additional ‘non-event’ triggers are identified within the Inner Crater, but are spatially distinct from both the ‘Ray Lake’ and ‘Active Vent’ (Fig. 5).

A primary benefit of the infrasound network and cross-correlation algorithm is automated differentiation of various types of Erebus eruption sources. During the 98-day period in early 2006 the crosscorrelation algorithm identified and located 385 discrete events occurring in the Erebus Inner Crater. The vast majority (93%) was located within the vicinity of the ‘Ray Lake’ depression and are attributed to characteristic large bubble bursts and Strombolian eruptions. Six percent of the 385 events were identified and located coincident with the ‘Active Vent’. As expected, many of the 358 lava lake events correspond to transients that were also detected by the independent MEVO Earthworm (USGS) event identification algorithm (Fig. 1). The MEVO event detection procedure identifies events based upon simultaneous infrasound and seismic transients recorded at E1S, which must independently trigger a short-term/long-term average algorithm. The Earthworm (version 6.3) trigger algorithm is implemented using a 1-s

Fig. 8. Survivor plots for combined events identified by both MEVO and cross-correlation methods and for MEVO identified events only. Weibull distribution fits (solid and dashed lines) have been attempted for both data with greater success for the complete catalogue.

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short-term average (STA), a 16-s long-term average (LTA), trigger ratio of 2.3, and a “quiet level” adjustment value of 4.0. A human analyst then reviews the data to verify that the signal was indeed an explosion event. The MEVO detection algorithm was effective at triggering on large transients; however it missed both many of the smaller and less impulsive ash-venting events because the corresponding low-amplitude acoustic signals are more prone to obfuscation by wind. A summary of the detected events using both methods is shown in Fig. 6. During the 98-day study interval the cross-correlation method identified 16% more lava lake events (358 versus 307) than the MEVO event identification scheme. It is vital to note that both methods failed to identify at least 90 events that were uniquely identified by their rival method. This implies that at the very least 450 lava lake explosions occurred during this time interval. However it is uncertain exactly how many additional events might have gone undetected by both methods. Histograms of the maximum acoustic pressures recorded at station E1S suggest that most of the undetected events for the MEVO picking algorithm correspond to relatively small recorded pressures, which are defined here as being less than 10 Pa at station E1S (Fig. 7). For instance, less than 30% of the lava lake events detected by the MEVO picking method have E1S pressure amplitudes less than 10 Pa, but more than 60% of all the events overlooked by this method are less than 10 Pa. This finding suggests that the cross-correlation method is especially good at identifying relatively small transients. It is notable that the cross-correlation event detection appeared to overlook events more often starting in February (refer to upper curves in Fig. 6 starting at Julian day 33). We suspect that diminished detection is due to intermittent electronic interference, of unknown origin, that was observed at the station SHK PT in late season. Because the network relies upon all three pressure sensors for event cross-correlation detection, there is no redundancy in the current system, and glitches or dropouts on a single microphone channel can thus prevent the cross-correlation algorithm from event detection. Future upgrades to the network in 2006–2007 have incorporated additional infrasound sensors to remedy the situation. An incomplete event catalogue leads to inaccurate statistical assessment of the temporal distribution of events inviting potential erroneous interpretation of the fundamental physical processes responsible for the activity (Varley et al., 2006). A survivor function characterizing the total number of events that exceed a given repose interval t is a potentially useful means of assessing the completeness of a catalog of events (Fig. 8). For instance, a survivor function with a h i Weibull distribution of the form exp
ðt=AÞk , has previously been attributed to failure rates of materials in volcanic environments (e.g., Voight, 1998; Connor et al., 2003). For the 2006 data at Erebus the more complete combined catalogue of 450 Erebus lava lake events reasonably follows a Weibull distribution with µ = 5.0 h and k = 0.94 (Fig. 8). In contrast, the incomplete catalogue of 307 MEVO event detections, does not fit the Weibull distribution well (µ = 7.0 h and k = 0.88). The distribution of event magnitudes provides both a measure of catalogue completeness and insight into physical source processes responsible for lava lake eruptions. Frequency–magnitude plots of lava lake events, as shown in Fig. 9, show no evidence for a linear powerlaw size distribution as is commonly observed for seismic earthquake swarms and tectonic earthquake sequences (e.g., Gutenberg and Richter, 1944). In this case the logarithmic squared maximum pressure amplitude, (ΔPmax), which is related to kinetic acoustic energy (Johnson and Aster, 2005), is taken as a proxy for event magnitude. These figures appear to indicate an upper magnitude limit for lava lake bursts, implying that source magnitude is constrained by invariant characteristics of the system, perhaps related to finite conduit dimension and/or gas supply. The quantile–quantile plot in Fig. 9 shows the relative distribution of event sizes as detected by the two competing event identification algorithms. With the exception of very

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Fig. 9. Histograms showing frequency–magnitude distributions for: a) the MEVO catalogue and b) the cross-correlation catalogue. Event magnitude is calculated as log10 (ΔPmax). c) Quantile–quantile plot shows comparison of the event size distributions in terms of their log maximum pressure ΔP.

small events and a few very large events, the population distributions are uniform implying that the detected frequency–magnitude size distributions are similar. The automated detection and windowing of Erebus eruption events enables subsequent processing and analysis of the specific events (Fig. 10a,b) facilitating future study. Previous work has focused on seismic and acoustic radiation from the ‘Ray Lake’ (Rowe et al., 2000; Johnson and Aster, 2005), but the selection of these events, and other Inner Crater activity, may be slightly biased by the analystcontrolled selection procedures. This study demonstrates that it is

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Fig. 10. Selection of a) six lava lake and b) six ash-venting events identified by the cross-correlation algorithm during January–April 2006. Traces are plotted from RAY and E1S. Jday corresponds to decimal Julian day for the start of the event.

particularly beneficial to use the cross-correlation algorithm to identify and study events originating outside the lava lake. For instance, both human picking and the MEVO automated event identification algorithm largely fail to identify ‘Active Vent’ eruptions because the seismic and acoustic manifestation is relatively low intensity and emergent. Moreover, long acquisition periods of months and years are not easily scanned by analysts for the visual occurrence of events. The technique outlined in this paper provides an opportunity to window in on activity that is unassociated with large lava lake bubble burst transients at Erebus (Fig. 10b) and generally provides a method for autodetection of diverse transient infrasonic sources with microphone networks. 5.2. Spatial variability of sources on the ‘Ray Lake’ The 358 lava lake infrasound sources mapped by grid search appear to exhibit significant spatial variability (Fig. 5), which immediately suggests variable bubble rupture locations on the surface of the lava lake. During the 3-month study interval located events were distributed over an area of about 40 m by 50 m, which is the full extent of the lava lake as estimated by visual inspection in early 2006. A significant portion of the lava lake, comprising an area extending 30 m by 20 m, exhibited eruption recurrence frequencies greater than 15

events per month per 100 m2 (Fig. 11). Spatial probability densities for the lava lake events were not observed to vary systematically over the course of the ∼3.5-month study interval. In other words, the mapped lava lake source distributions from the months of January and February are nearly identical to the mapped source locations from March and April, and thus do not appear to be correlated with any systematic seasonal sound speed changes. Variable source locations on the surface of the lava lake can be verified with analysis of corresponding video footage. Fig. 11 highlights two located events that possess high quality accompanying video. One of these events occurred on 7 January and the other occurred on 11 January and was located about 30 m to the northeast (to the left in the field of view of the video). The infrasound locations appear to coincide with centroids of the growing bubbles, which are marked accordingly in the second frames of each video sequence (Fig. 11). It should be noted that it is much easier to identify the centroids of expanding bubbles in the video footage than in the still frames. It is relevant to note that the initial locations of the emerging bubbles (refer to first video frames in Fig. 11) are not spatially coincident with the growing bubble as seen ∼0.5 s later. Moreover, the bubble positions in these subsequent image frames are in better agreement with the mapped infrasound sources. This is to be expected if the recorded infrasound is proportional to the time-

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Fig. 11. Middle panel shows detail of lava lake as viewed from the camera site with contours showing explosion recurrence densities for 358 events. Contours correspond to 0, 5, and 15 events per month per 100 m2. Grid spacing is 5 m. Two select events are located and shown along with two zoomed-in still image frames taken from the corresponding digital video. Still image frames are indicated with 0.5-s separation.

integrated volumetric acceleration of the atmosphere, as for a monopole acoustic source (Lighthill, 1978). If the volumetric acceleration of the atmosphere is approximated as the product of a radial outward acceleration of a bubble membrane with the bubble membrane surface area, it follows that the atmosphere is more efficiently perturbed when the bubble is larger and acceleration high, which occurs some time after the initial appearance of the bubble. The 7 January explosion at 01:59 AM, for instance, appears to first break surface in the northeast portion of the lava lake, but within a few tens of s distends the entire lava lake and its infrasonic location is ultimately centrally located. In contrast, the small January 11th event remains confined to the northeast where it is located by the grid search. It is evident from the combined video and mapped source locations that much of the infrasound is generated only when the bubbles have achieved considerable size (on the order of 10 m or more in diameter). Such large sources are only approximations of a point source and are also substantially larger than the grid size (5 m) that is used to locate them. Strictly speaking our grid search is locating a centroid location within a finite ‘origin volume’ of the large bubble, rather than a point source epicenter. 6. Conclusion We have demonstrated a technique for automatic identification and precise localization of two distinct sources of volcanic infrasound at Erebus Volcano utilizing a three-station distributed network. This type of network and associated cross-correlation algorithm and grid search provides a simple yet efficient tool for cataloguing eruptive events and assessing changes in the fundamental level of impulsive and extended-duration volcanic activity over month-long time scales. Improved catalogue completeness and system robustness could be easily achieved by the inclusion of additional pressure transducers to the network. In summary, a network of acoustic pressure sensors azimuthally distributed around a crater shows great utility for tracking activity at volcanoes with numerous individual vents. Acknowledgments Funding for this work was provided by NSF grants OPP-9814291, OPP-0116577, OPP-0229305, and EAR 0440225. We acknowledge the Ohio State, USGS, NASA/GSFC, and NSF Polar Programs for the detailed

Erebus digital elevation map image incorporated in this study. IRIS facilities are supported by Cooperative Agreement NSF EAR-000430 and the Department of Energy National Nuclear Security Administration.

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