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We monitored geyser activity in the Lower Geyser Basin (LGB) of Yellowstone National Park with dual four-element microphone arrays separated by ~ 600 m. The arrays were independently used to identify incident coherent plane wave energy, then conjoint cross beam back-azimuths from the two arrays were used to precisely locate signal sources. During a week in August 2011 we located repeating infrasound events, peaked in energy between 1 and 10 Hz, originating from at least five independent geothermal features, including the episodically erupting Great Fountain, Fountain and Kaleidoscope Geysers, as well as periodic infrasound from nearby Botryoidal and persistent sound from Firehole Spring. Although activity from nearby cone-type geysers was not detected in the infrasound band up through 50 Hz, the major fountain-type geysers (i.e., with columns greater than 10 m) could be detected at several kilometers, and two minor geysers (i.e., a few meters in eruption height) could be tracked at distances up to a few hundred meters. Detection of geyser activity was especially comprehensive at night when ambient noise was low. We conclude that infrasound monitoring of fountain-type geysers permits convenient tracking of geyser activity, episodicity, signal duration, energy content, and spectral content. These parameters enable objective statistical quantification of geyser behavior and changes over time that may be due to external forcing. Infrasonic study of geyser activity in an individual basin has great monitoring utility and can be reasonably accomplished with two or more distributed sensor arrays.
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Detecting geyser activity with infrasound
J.B. Johnson
a,
, J.F. Anderson
b
, R.E. Anthony
b
, M. Sciotto
c
a
Department of Geosciences, Boise State University, 1910 University Drive, Boise, ID 83702, United States
b
Department of Earth and Environmental Sciences, New Mexico Tech, 801 Leroy Place, Socorro, NM 87801, United States
c
Dipartimento di Scienze Biologiche, Geologiche e Ambientali, Sezione Scienze della Terra, Università di Catania, Corso Italia 57, 95129 Catania, Italy
abstractarticle info
Article history:
Received 23 September 2012
Accepted 25 February 2013
Available online 13 March 2013
Keywords:
Geyser
Yellowstone
Infrasound monitoring
Array analysis
We monitored geyser activity in the Lower Geyser Basin (LGB) of Yellowstone National Park with dual four-
element microphone arrays separated by ~600 m. The arrays were independently used to identify incident
coherentplane wave energy, thenconjoint cross beam back-azimuthsfrom the two arrays wereused to precisely
locate signal sources. During a week in August 2011 we located repeating infrasound events, peaked in energy
between 1 and 10 Hz, originating from at least ve independent geothermal features, including the episodically
erupting Great Fountain, Fountain and Kaleidoscope Geysers, as well as periodic infrasound from nearby Botryoidal
and persistent sound from Firehole Spring. Although activity from nearby cone-type geysers was not detected in
theinfrasoundbandupthrough50Hz,themajorfountain-type geysers (i.e., with columns greater than 10 m)
could be detected at several kilometers, and two minor geysers (i.e., a few meters in eruption height) could be
tracked at distances up to a few hundred meters. Detection of geyser activity was especially comprehensive at
night when ambient noise was low. We conclude that infrasound monitoring of fountain-type geysers permits
convenient tracking of geyser activity, episodicity, signal duration, energy content, and spectral content. These
parameters enable objective statistical quantication of geyser behavior and changes over time that may be due
to external forcing. Infrasonic study of geyser activity in an individual basin has great monitoring utility and can
be reasonably accomplished with two or more distributed sensor arrays.
© 2013 Elsevier B.V. All rights reserved.
1. Introduction
Geyser sound and volcano sound generation may be considered
analogous in a number of respects. In both systems, volatiles can
reach a liquid's free surface (water in the case of the geyser; silicate
melt in the case of the volcano) and burst with considerable overpres-
sure relative to the atmosphere. In volcanic systems both the distension
of the free surface due to sub-surface strains (Garces and McNutt, 1997;
Yokoo and Iguchi, 2010), and the expansion of gas following fragmenta-
tion (Ripepe and Gordeev, 1999; Jones et al., 2008), have been considered
as volumetric sources, which produce intense low-frequency sounds.
High-velocity emissions of gas and/or condensed phases are also respon-
sible for jetting sounds at volcanoes (Woulff and McGetchin, 1976;
Matoza et al., 2009), which may serve as analogs for certain geysers
that erupt as collimated jets of water and steam.
The style and vigor of a volcanic eruption generally dictate the
spectral content and intensity of the radiated sound. For relatively
low-energy explosive volcanic eruptions, often characterized as
strombolian or vulcanian, the radiated sound is most intense around
the near-infrasound band (and specically in the frequency range of
a few seconds to a few Hz) (Johnson et al., 2004; Marchetti et al.,
2009). These low frequencies predominate because of the relatively
large physical dimension and long duration of source movements,
such as bubble oscillations or gas expansion (Vergniolle and Brandeis,
1996; Gerst et al., in review). Geysers, though smaller in physical scale
than volcanoes, are still capable of producing relatively large volume
uid ejections with columns as wide as a few meters and as high as a
few tens of meters. Accordingly, fountain-type geysers radiate predom-
inantly low frequency acoustic energy in the near-infrasound band
(120 Hz).
Geophysical sources of infrasound, including volcanoes, earthquakes,
avalanches, thunder, bolides, and storms, are amenable to remote moni-
toring and tracking in large part because infrasonic frequencies attenuate
slowly with distance (Arrowsmith et al., 2010); however, geophysical
infrasound detection and interpretation are often obscured by unwanted
signals (e.g., human activity or microbaroms) or noise contributions from
atmospheric winds (Bowman et al., 2005; Fee and Garces, 2007). In order
to distinguish targeted signals from noise, microphone arrays are typical-
ly deployed to identify signal coherency and source direction (Rost and
Thomas, 2002). Toward the goal of locating and tracking geyser activity
at Yellowstone, we deployed two separated infrasound microphone
arrays in August 2011.
Although various seismic surveys have been carried out at geysers
to study ground-propagating elastic waves (e.g., Kieffer, 1984; Kedar
Journal of Volcanology and Geothermal Research 256 (2013) 105117
Corresponding author. Tel.: +1 208 426 2959; fax: + 1 208 426 4061.
E-mail addresses: jeffreybjohnson@boisestate.edu (J.B. Johnson), jfanders@nmt.edu
(J.F. Anderson), ranthony@nmt.edu (R.E. Anthony), mariangela.sciotto@ct.ingv.it
(M. Sciotto).
0377-0273/$ see front matter © 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.jvolgeores.2013.02.016
Contents lists available at SciVerse ScienceDirect
Journal of Volcanology and Geothermal Research
journal homepage: www.elsevier.com/locate/jvolgeores
et al., 1996) this work is the rst of its kind to investigate broadband
sound waves radiated from geysers into the atmosphere.
2. Background
Yellowstone National Park in Wyoming, USA hosts the world's
densest concentration of geysers with about 500 active in a typical
year, or more than half the world's total. Most of Yellowstone's geysers
are located in three basins, Upper Geyser Basin (UGB), Lower Geyser
Basin (LGB), and Norris Basin, which are extensive geographic regions
that comprise distinct groups of thermal features. For instance, the LGB,
which is the focus of this study, is 13 km
2
in area and has more than
1500 thermal features organized into about 13 distinct groups (Bryan,
2008). Classication as a geyser requires that a thermal feature exhibit
intermittent discharge of water accompanied by steam. According to
Bryan (2008) there are well over one hundred features that qualify as
geysers in the LGB alone.
Because we anticipated that violent ejection of steam and water is
most likely to generate high signal-to-noise infrasound, we deployed
our microphone arrays within a few hundred meters of Great Fountain,
one the most prominent geysers of the LGB. Though Great Fountain
Geyser is located near the eastern edge of the LGB, we still anticipated
recording geyser activity from other nearby features. Table 1 provides
a list of selected LGB geysers, where plume height in excess of a few me-
ters is often reported (e.g., Bryan, 2008). A map showing these geysers
and our microphone arrays is provided in Fig. 1. Despite having fewer
major geysers than the UGB, the LGB provided an excellent test bed
for acoustic monitoring because of lower tourist trafc and associated
cultural noise.
3. Experiment
We deployed two four-element infrasound arrays in the LGB between
Aug. 8th (Julian Day 220) and Aug. 14th (Julian Day 226) of 2011. These
arrays consisted of four identi cal low-frequency microphones with at re-
sponse between 0.02 Hz and a Nyquist frequency of 50 Hz. Linear
dynamic range of the instruments was +/125 Pa and noise oor in
the 1 to 10 Hz band was ~2 mPa rms (Marcillo et al., 2012). Three of
the array elements were positioned at the vertices of an approximate
equilateral triangle and connected to the central datalogger by 30-m
cables. A fourth microphone was co-located at the center of the array
next to a 6-channel, 24-bit logger (Refraction Technol ogy RT-130) record-
ing continuously at 100 Hz. GPS timing of the loggers allowed coordina-
tion between the two arrays, and kinematic GPS surveying provided
sensor node locations accurate to within ~ 0.5 m in the horizontal and
~1 m in the vertical.
The array centers were separated from each other by 620 m. The
midpoint of the two arrays, or network center, was located at 110.802°
W, 44.537° N, and 2237 m above sea level, and is used as the coordinate
reference for mapped acoustic sources. The purpose of dual arrays was
to identify and locate sources producing coherent signals. We identify
source locations by rst using each four-element array to independently
determine back-azimuth of coherent infrasound. Then we nd the inter-
section region of the back-azimuth beams to identify the responsible
geyser. Owing to the distribution of the two arrays, location resolution
and errors are azimuthally and radially variable. We discuss location
uncertainties as part of our study's network response.Thearray re-
sponse, a function of array geometry, is also examined as it inuences
aliasing and back-azimuth uncertainty.
3.1. Array response and precision
The array response of a distribution of sensors characterizes the sus-
ceptibility of an array to aliasing. Such aliasing is problematic for arrays
with apertures that are large relative to incident plane wave wave-
lengths and is especially pronounced in four-element arrays with equal
spacing between sensor nodes (Christie and Campus, 2010). The normal-
ized theoretical wavenumber response of an n-element array is a func-
tion of 2-D wavenumber (k
x
and k
y
)(Rost and Thomas, 2002):
Rk
x;ky

¼1
n2X
n
i¼1
effiffiffiffiffi
1
pkxxiþkyyi
ðÞ
2
1
The array output is the convolution of the array response and the
horizontal waveeld dened by a propagation vector. An ideal array
response has a single peak at the origin (k
x
= 0 and k
y
=0)andnegli-
gible side lobe peaks.
Array responses with signicant sidelobes (see Fig. 2b,e for the West
and East arrays respectively) are susceptible to possible aliasing. To
illustrate the potential ambiguity associated with an ~5 Hz infrasound
tone suppose that a recording on channel #1 of the West Array exhibits
a phase shift of half a cycle relative to channels #24. In the absence of
other information these observations could be attributed either to
horizontally propagating acoustic energy coming from either the
WNW or the ESE, corresponding to two different array response peaks.
Forourlocalgeysersourcesweassume that propagation must be sub-
horizontal (i.e., f¼c=2πðÞ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
k2
xþk2
y
q;Fig. 2). This, coupled with the facts
that geyser infrasound is generally broadband (with frequencies less
Table 1
Location list of microphone arrays and various geysers, geyser groups, and geyser basins near LGB: Names and abbreviations used in gures, latitude, longitude, elevation, distance
from the center of the microphone network, geyser eruption repeat interval, duration of geyser activity, typical height of play, and type of geyser indicated as either (F)ountain, (C)
one, or (V)arious. Details are taken from Bryan (2008).
Name Latitude (degrees N) Longitude (degrees W) Elev. (m) Dist. (m) Interval Duration (minutes) Height (m) Type
Network Center (x) 44.536614 110.801662 2231 0
East Array (EA) 44.536765 110.797793 2238 309
West Array (WA) 44.536462 110.805531 2229 309
Great Fountain (GF) 44.536578 110.800026 2234 130 915 h 30120 2367 F
Firehole Spring (FS) 44.535141 110.801949 2235 165 Continuous Continuous b2F
Botryoidal Spring (BS) 44.534882 110.799529 2238 265 35 min 1 3 F
White Dome (WD) 44.539394 110.802823 2228 323 15180 min 2 69C
Pink Cone (PC/PCG) 44.542893 110.796273 2235 819 1825 h 90120 b9C
Bead (B/PCG) 44.543418 110.794972 2239 924 2733 min 2.5 78C
Narcissus (N/PCG) 44.544322 110.797004 2235 933 26h b15 46F
Labial (B/PCG) 44.543751 110.795304 2240 940 57h b2b8F
Steady (S/BWG) 44.544198 110.786705 2247 1455 Continuous Continuous b4F
Artesia (A/BWG) 44.544075 110.784056 2253 1623 Continuous Continuous b3F
Fountain (F/FG) 44.551205 110.808326 2228 1705 415 h 30 ~25 F
Kaleidescope Group (various/KG) 44.554275 110.813347 2212 ~2200 Various Various b45 V
Middle Geyser Basin (various/MGB) 44.525055 110.838148 2218 ~3200 Various Various b5V
Upper Geyser Basin (various/UGB) 44.466665 110.836993 2238 ~ 8700 Various Various 60 V
106 J.B. Johnson et al. / Journal of Volcanology and Geothermal Research 256 (2013) 105117
Fig. 1. Detail map featuring LGB study area. Its position relative to the Middle Geyser Basin (MGB) and Upper Geyser Basin (UGB) is given by the red rectangle in the locator map.
Locations are shown for Great Fountain (GF), Firehole Spring (FS), Botryoidal Spring (BS), White Dome (WD), the Pink Cone Group (PCG) including Narcissus (N), Pink Cone (PC),
Bead (B), and Labial (L), the Black Warrior Group (BWG) including Steady (S) and Artesia (A), the Fountain Group (FG) featuring Fountain (F), and the Kaleidescope Group (KG).
Details of these geysers are summarized in Table 1. West Array (WA) and East Array (EA) microphone sites are shown as blue triangles along with the network center indicated by
crosshairs. Array geometry detail is shown in Fig. 2.
Fig. 2. a,d) Detail plan view maps of West and East microphone arrays. Parenthetical coordinates are the east-west and north-south array center location relative to the center of the
network and map origin. b,e) Corresponding array responses calculated according to Eq. (1). Contours indicate wavenumbers for 5, 10 and 15 Hz horizontal acoustic plane waves. c,
f) Histograms of angular uncertainties in calculated back-azimuth for data digitally discretized to 0.01 s.
107J.B. Johnson et al. / Journal of Volcanology and Geothermal Research 256 (2013) 105117
than 5 Hz) and often has transient pulses, limits our arrays' susceptibility
to aliasing.
Array back-azimuth precision is limited by small array dimension
and/or coarse timing resolution for correlated phases crossing the array
elements. For our digital data the precision of cross-correlation lag
times is discretized to the nearest sample, which is 0.01 s in our analysis.
Subsequent back-azimuth determination (see source localization section
below) is calculated by inverting these rounded phase lag times. To
anticipate the associated error due to time discretization we calculate
time of arrivals for incident rays crossing the arrays at a range of
azimuths and then round these arrival times to the nearest 0.01 s before
inverting for an inferred back-azimuth. For 360 different plane waves
crossing the arrays at 1° azimuthal increments the standard deviation dif-
ference between actual and calculated azimuths are 1.9 and 1.6 degrees
for the West and East arrays respectively (Fig. 2c,f).
3.2. Network response
Our two arrays separated by 620 m are used to locate infrasound
sources when source back-azimuths cross obliquely. The compass
azimuth (relative to true North, or 0°) connects the West array to
the East array at 87° and the azimuth connecting East to West array
is 93° (or 267°). As such, back-azimuth beams cross for
θW>θEwhen 93bθWb87 and 93bθEb87
or
θWbθEwhen 87bθWb267 and 87bθEb267 ð2Þ
where θ
W
and θ
E
are the compass bearing back-azimuths from the
West and East arrays to the source. Overlapping back-azimuth direc-
tions are indicated as colored regions in Fig. 3, which also show the
corresponding distance and azimuth to the crossing beams (Fig. 3a,b).
These parameters are determined by computing the locations of con-
verging beams (i.e., the inferred source location) for all possible permu-
tations of θ
W
and θ
E
(ranging from 93 to 267°).
Errors in source location distance (Fig. 3c) are calculated as the mag-
nitude of the gradient of Fig. 3a. At a distance rthe distance error per
degree of back-azimuth uncertainty is dened as:
εr¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
r
θW

2
þr
θE

2
sð3Þ
Fig. 3. a) Distance (r) to cross-beam sources for conjointly computed back-azimuths from West and East arrays. b) Compass bearing (θ) to inferred sources. c) Radial error (ε
r
) per
degree of back-azimuth uncertainty. d) Azimuthal error (ε
θ
) per degree of back-azimuth uncertainty. All distance and source azimuths are relative to the network center (crosshairs
in Fig. 1). Blank regions correspond to non-converging back-azimuths. Expected bearing to those geysers and groups indicated in Fig. 1 and Table 1 are shown as white circles with
names annotated in panel c.
108 J.B. Johnson et al. / Journal of Volcanology and Geothermal Research 256 (2013) 105117
For instance, the 100-m/° contour in Fig. 3c implies that the distance
to a source (e.g., the Fountain Group (FG)) is uncertain to ~100 m for a
back-azimuth uncertainty of one degree. An azimuthal error (Fig. 3d) is
transverse to the radial error and is computed from the azimuth to the
source (Fig. 3b) as:
εθ¼rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
θ
θW

2
þθ
θE

2
sð4Þ
Generally, radial uncertainties are much larger than azimuthal un-
certainties and both uncertainties increase for greater source-receiver
distances.
4. Source localization
Our procedure to locate robust infrasound sources using dual arrays
involves identication of coherent energy arriving coincidentally at both
arrays. Coherency at a single array is established if timing of phase lags,
determined through cross-correlation of the array elements, is internally
consistent. If so, potential source back-azimuths may be calculated. For
coherent energy that traverses both arrays simultaneously a candidate
source is mapped as the intersection of two back-azimuths. This poten-
tial source is reliable if its position is in agreement with the phase delay
observed between beam-stacked waveforms at the WA and EA.
4.1. Back-azimuth determination
Phase lags between two elements of a microphone array are
determined through cross-correlation of pairs of sensors. For melements
in an array there are 1=2ðm2mÞunique sensor pair combinations
that can be cross-correlated. For a cross-correlation to be considered
signicant it must exceed a normalized cross-correlation threshold, which
we x in this study at the 95% condence level for cross-correlated white
noise. For nsamples 2=ffiffiffi
n
pis the expected normalized cross-correlation
for Gaussian white noise. For our 4 node arrays and 20 s (2000 sample)
comparison windows a normalized cross-correlation threshold of 0.045
must be exceeded on all 6 station pairs. More stringent cross correlation
thresholds should probably be applied for three element arrays, which
have only 2 unique station pair comparisons.
In addition to correlation threshold, strict consistency criteria must
be met. Lag times of peak cross-correlation are calculated for sliding
windows and checked for internal consistency similar to that used in
the PMCC technique (Cansi, 1995). While Cansi (1995) searches for
consistency among unique triad pairs, our processing requires consis-
tency among all unique quad pairs. For our four-element array there
are 3 unique sequences of quad pair comparisons: ch1ch2ch3
ch4ch1, ch1ch3ch2ch4ch1, and ch1ch2ch4ch3
ch1. Internal consistency is met when the summed phase lags of the
quad pairs sum toward zero, i.e. |ε
ijkl
t
ij
+ε
ijkl
t
jk
+ε
ijkl
t
jk
+ε
ijkl
t
li
|χ.
Here the indices i,j,k, and lrefer to one of the 4 sensor array channels.
The variable t
ij
is the lag time associated with peak waveform cross-
correlation and ε
ijkl
is the Levi-Civita symbol, where only non-repeating
index permutations are non-zero, + 1 or 1, and sign is dependent
upon the order of indices. Because of digital signal discretization, which
rounds correlation phase lags to the nearest sample, we require the abso-
lute value of consistency to be less than or equal to χ= 4 samples.
Consistent phase lags for unique quad sequences are used to com-
pute a back-azimuth by inverting for the horizontal projection of the
slowness vector s
¼sx;sy

. Following the inversion procedure
outlined in Arechiga et al. (2011) time lags are related to the slowness
vector by
tij
tjk
tkl
tli
2
6
6
43
7
7
5¼
dxij dyij
dxjk dyjk
dxkl dykl
dxli dxli
2
6
6
43
7
7
5
sx
sy
 ð5Þ
where dx and dy are the GPS surveyed east-west and north-south sep-
aration distances between pairs of sensor elements in an individual
array. The distance matrix, denoted as D,canberepresentedasa
two-column matrix because the vertical separation distance is assumed
zero (i.e., dz
ij
=dz
jk
=dz
kl
=dz
li
= 0) as all sensor nodes were
deployed on an approximately level surface to within ~1 m precision.
Because the solution to the slowness vector for t=Ds is overdeter-
mined we solve it by using a least squares solution with the generalized
inverse of D,whereD
g
=(D
T
D)
1
D
T
and the slowness vector is solved
as s=D
g
t. A third (vertical) component of the slowness vector can be
computed assuming that the coherent arrival is an acoustic plane wave
with speed c,wheresz¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
c2s2
xs2
y
q.
Imaginary values of s
z
imply impossibly low slowness values for
acoustic waves traversing the array, however near-horizontal acoustic
waves may potentially result in imaginary vertical slowness values
due to cross-correlation timing discretization, which leads to rounded
values of tand values of s
x
and s
y
, which may be rounded upwards.
For this reason we consider that horizontal slownesses, which exceed
the slowness amplitude (c
1
) by less than 10%, may be treated as hor-
izontally propagating acoustic waves with zero degree elevation angles
(i.e., s
z
=0). We use the following conventions to calculate vertical
slowness:
sz¼imaginary for c1b0:9ffiffiffiffiffiffiffiffiffiffiffiffiffiffi
s2
xþs2
y
q
sz¼0 for 0:9ffiffiffiffiffiffiffiffiffiffiffiffiffiffi
s2
xþs2
y
qc1ffiffiffiffiffiffiffiffiffiffiffiffiffiffi
s2
xþs2
y
q
sz¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
c2s2
xs2
y
qfor ffiffiffiffiffiffiffiffiffiffiffiffiffiffi
s2
xþs2
y
qbc1ð6Þ
When s
z
is imaginary we consider the arrival to be spurious.
From the acoustic wave slowness vector the back-azimuth and inci-
dence are determined. Azimuth of the plane wave is calculated using
the trigonometric relations:
θ¼arctan sx=sy

for sy>0
θ¼arctan sx=sy

þ180for syb0ð7Þ
while plane wave elevation angle, as measured from the horizontal, is
ϕ¼arcsin cs
z
ðÞ ð8Þ
In this analysis of LGB local geyser sources propagation is expected
to be sub-horizontal. Thus, we ignore signals with values of ϕgreater
than 15°. We note that more steeply incident acoustic energy observed
during our study is often moving and attributable to aircraft.
Back-azimuths for internally consistent array detections are inde-
pendently calculated for the three unique permutations of sensor pair
correlations, i.e. ijkl = {1234,1243,1324} and then averaged. These
back-azimuths may then be plotted as a function of time to show the
temporal evolution of potential acoustic sourcedirections. The example
of Fig. 4 shows a one-hour period (starting August 10th at 10:00 PM
local time) when three distinct geyser sources were detected.
4.2. Cross beam source localization and validation
Together the back-azimuths from the West and East arrays are
used to locate potential geyser sources. Back-azimuth beams from
the two arrays converge under the conditions specied in Eq. (2).
Cross beam intersection then occurs at a location x
0
,y
0
where
x0¼xWþsinθW
yWyE
ðÞ
sinθExWxE
ðÞ
cosθE
sinθWcosθEcosθWsinθE

and
y0¼yWþcosθW
yWyE
ðÞsinθExWxE
ðÞcosθE
sinθWcosθEcosθWsinθE

ð9Þ
109J.B. Johnson et al. / Journal of Volcanology and Geothermal Research 256 (2013) 105117
Here x
W
,y
W
and x
E
,y
E
correspond to the UTM coordinates of the
West and East arrays respectively.
A candidate source location is identied for converging beams when
coherent energy is conjointly identied on both arrays (i.e., during the
same 20 s sliding window period). In this case beam waveform stacks
(see Eq. (10) below) are produced for each array and a cross-correlation
time lag is calculated for the two beams. These inter-network lag times
indicate potential source locations lying along hyperbolic curves (Fig. 5).
If the hyperbolic curve for a given lag time coincides with the cross-
beam intersection locus x
0
,y
0
then we consider that source location to
be robust.
Source locations are plotted with footprints that scale with back-
azimuth uncertainty. An azimuthal uncertainty for each array is deter-
mined as the 95% condence intervals for estimated errors (3.6° for
West Array and 2.5° for East Array; Fig. 2c,f). Error ellipses in Fig. 5 are
centered on the intersection of back-azimuths and have axes with di-
mensions of angular and radial uncertainties. It is evident that location
uncertainty increases markedly for more distant sources as predicted
by the network response (Fig. 3).Forinstance,fortheFountainGeyser
source, radial distance error is as great as half a kilometer. Locations of
geysers and other infrasound sources are shown in an animation that
is provided as auxiliary materials. This movie shows a 5-day sequence
of mapped sources, in the form of Fig. 5, for hourly time increments.
5. Results
5.1. Interpretation of beam stacks
Reliable source back-azimuths can be used to produce array beam
stacks δp
b
(t), which provide improved signal-to-noise over wave-
forms from individual channels. To create a beam stack the excess pres-
sure waveforms in an individual array δp
i
(t) are shifted by retardation
times corresponding to relative locations and incident slowness vector
and then stacked (Fig. 6):
δpbtðÞ¼1
mX
m
i¼1
δpitþdxijsxþdyij sy
 ð10Þ
Fig. 4. (Upper left) Example 1-hour time series for West Array infrasound recording (ltered above 0.25 Hz) and calculated coherent back-azimuths for 20-s windows sliding at 1-s
increments. (Lower right) corresponding time series of East Array infrasound and calculated back-azimuths. (Upper right) map of conjoint back-azimuths from the two arrays.
Filled circles indicate conjoint sources with associated converging back-azimuths. Back-azimuths for various geyser basins, geyser groups, or specic geysers that are indicated
in Fig. 1 and Table 1 are shown and described in lower left text panel. Data shown are from a one-hour period starting August 10th at 10:00 PM local time (Julian Day 223 at
04:00). Corresponding detection source locations and example waveforms for this hour are shown in Figs. 5 and 6.
110 J.B. Johnson et al. / Journal of Volcanology and Geothermal Research 256 (2013) 105117
In our study we calculate a center node beam array stack where jis
channel 1.
Similarity between the beam stack waveforms of the two arrays is
variable and depends upon signal strength, background noise, and
frequency band. For the four time windows displayed in Fig. 6 the signal
correlation is indicated for both broadband infrasound and four narrow-
band overlapping frequencies. Signal similarity and relative delay times
are quantied from the peak normalized cross-correlation of band-
passed waveforms. Higher signal-to-noise waveforms, such as the ones
displayed in Fig. 6ab, are more highly correlated than smaller tran-
sients, such as those shown in Fig. 6cd.
For the featured data in Fig. 6 cross-network correlation is generally
greatest in near-infrasoundand low audio band (132 Hz) although this
varies somewhat depending upon the particular source. For example,
Fig. 6a corresponds to infrasound originating from the Northeast and
external to the LGB. For this event peak signal correlation between
arrays occurs in the band 0.25 to 2 Hz and candidate source types
could include earthquakes, bolides, thunder, or cultural signal (such as
aircraft or explosions) (Arrowsmith et al., 2010). In this particular case,
we feel that the most probable signal source is distant thunder owing
to the signal shape and amplitude, intermittency (many events from
this direction occurring over tens of minutes), and spectral content sim-
ilar to that previously observed for thunder (e.g., Assink et al., 2008;
Arechiga et al., 2011).
Geyser sources including Fountain Geyser (Fig. 6b), Botryoidal Spring
(Fig. 6c), and Firehole Spring (Fig. 6d) are also identied during the hour
starting at 10:00 PM local time on August 10th. Fountain Geyser and
Botryoidal Spring signal correlation is greatest in the 0.252Hzbands
while Firehole Spring is best identied in the 18 Hz band. Correlation
lag times are consistent with sources at Fountain Geyser, Botryoidal
Spring, and Firehole Spring and corroborate cross beam locations of
the geyser sound sources. Notably, the low-amplitude correlated signal
from both Firehole and Botryoidal Spring is not clearly evident through
visible inspection of the time series data. For these geysers relatively
high levels of ambient infrasound noise are indicated by similarities
between the spectra for the events and pre-event noise windows
(Fig. 8c). Filtering above ~ 0.25 Hz coupled with array and/or network
analysis is thus vital to identify and track activity from quietergeysers.
5.2. Geyser detection
Dual array cross beaming and validation through inter-array lag time
delays enable robust identication of geyser and/or other signals. If the
source coincides with a known geyser feature, e.g. referenced in Bryan
(2008), we consider it to be a geyser signal. During the week-long mon-
itoring interval in August 2011 we identied at least ve repeating
geyser sources and potential activity from several others. In general,
geyser detection was affected by levels of wind, which contribute to am-
bient noise throughout the near infrasound band. Obfuscation of geyser
signal in the LGB wasparticularly pronounced during windy afternoons,
however nighttime recordings had much improved signal-to-noise
(refer to summary of 5-day record in Fig. 7). The ve primary identied
LGB sources were Great Fountain (130 m), Firehole Spring (165 m),
Botryoidal Spring (265 m), Fountain Geyser (1706 m), and at least one
source from Kaleidoscope Group (2171 m). Descriptions of their activi-
ty, including episodicity and eruption duration, are given below.
Fig. 5. Map of infrasound sources occurring during the-one hour period shown in Fig. 4. Locations of candidate geysers (from Table 1 and Fig. 1) are marked by red circles while red
arrows indicate direction to geyser basins located off the map. Map origin, indicated by crosshair, is the center of the two arrays indicated by blue triangles. Contours indicate
expected time lag delays between the East Array and West Array. Ellipses designate those conjoint back-azimuth intersections, which have been validated by inter-network lag
time delays and for which incidence is nearly horizontal (i.e., elevation angles less than 15°). Sources located off map are indicated with yellow arrows. Numbered source epicenters
correspond to featured events shown in Fig. 6. Events #24 correspond to geyser activity.
111J.B. Johnson et al. / Journal of Volcanology and Geothermal Research 256 (2013) 105117
5.2.1. Great fountain
Ten eruptions of Great Fountain were detected during the 5-day
study interval shown in Fig. 7. Activity of main events is separated by
11 to 19-hour intervals and duration of detected events ranges from
45 to 75 min with one event lasting 130 min. This long-duration event
precedes an exceptionally long 19-hour quiescent interval suggesting
that more voluminous eruptions may require longer recharge intervals.
Great Fountain infrasound records corroborate anecdotal observations
that most events are composed of 4 or more pulses of 5 to 20-minute du-
ration separated by up to 15 min of quiet (Fig. 7). A detailed example of
a typical event from Great Fountain, along with its normalized power
spectrum, is provided in Fig. 8a.
Characteristic event durations and intervals between events can easily
be quantied from the overview records of Figs. 7 and 9a, which both
show detections as a function of time. For our 5-day monitoring interval
the eruption durations Dappear correlated with inter-eruption intervals
I,i.e.I=4.D+7.1hours (r-squared value of 0.73). Despite our
short observation period these observations exhibit similarities with
previous eyewitness observations cited in Bryan (2008) where I=
D+6hours(Fig. 9b).
Due to the relative intensity of the Great Fountain infrasound source
and its proximity to both infrasound arrays the signal from this geyser
is often identiable through visual inspection of the time series after l-
tering above the microbarom band. Peak-to-peak amplitude of the largest
pulses from a Great Fountain sequence occasionally exceed 2 Pa recorded
at the East Array (276 m distant). These peak-amplitude pulses are bipo-
lar ~ 2 Hz wavelets, with asymmetrically larger compression than rarefac-
tion. They accompany explosive bursts from the geyser's vent, which
manifest voluminous vapor and water columns reaching 30 to 50 m
high. Smaller explosive bursts, as seen in video records, are correlated
with less intense infrasound pulses. Impulsivity, frequency content, and
bimodal pulse shape are reminiscent of explosion infrasound N-wave sig-
nals accompanying explosions of pressurized gas at many erupting volca-
noes (Johnson and Ripepe, 2011).
Band-limited acoustic power radiated from a monopole into a homo-
geneous hemisphere may be quantied from ltered infrasound record-
ings according to Dowling (1998):
PtðÞ¼2πr2
ρc
tþΔt
t
δp2τþr=cðÞ
Δtdτð11Þ
where Δtis the averaging interval over which power is calculated,
sethereat1s,andρis the atmospheric density, approximated as
1.0 kg/m
3
. Cumulative energy can then be calculated as the time in-
tegrated acoustic power:
EtðÞ¼2πr2
ρct
0δp2τþr=cðÞdτð12Þ
For the most intense Great Fountain pulses, power can exceed
300 W averaged over a 1-s interval (Fig. 8a). Cumulative energy over
the course of an hour-long event (t= 3600 s) is several thousand
Joules, or slightly less than 1 W averaged over a Great Fountain event.
For some Great Fountain events the infrasound network identies
potential short (minute-long) precursors occurring an hour to several
hours prior to the main eruption. Geyser observers at Great Fountain
have commonly reported this activity as pre-playthat accompanies
boiling and overow from the vent occurring on average 85 min prior
to the main event (Bryan, 2008). Of the ten events detected in our
infrasound records half of them show these precursory infrasound
signals occurring prior to the main event (see indicated red arrows in
Fig. 9a). Infrasound monitoring has the potential to identify pre-play
and quantify how long and how often it precedes Great Fountain erup-
tions. As with statistics relating event duration and quiescent intervals
we suggest that longer monitoring periods will facilitate robust statisti-
cal relationships.
5.2.2. Fountain geyser and Kaleidoscope Group
Fountain Geyser, not to be confused with Great Fountain, is the major
geyser of the regularly performing features in the Fountain Group, located
about 1700 m from the center of our twin infrasound arrays. During
nighttime periods of relative low wind and quiet (9:00 PM through
10:00 AM) events from Fountain were routinely recorded. During our
ve-day observation period 13 events were identied and 11 proba-
ble events were missed due most likely to ambient wind noise. Non-
detected events were inferred from the extrapolation of the excep-
tional regularity of Fountain Geyser eruptions.
From our infrasound records we detect eruptions with regular in-
tervals of 5.9 +/0.3 h that appear to correlate with long mode in-
tervalsdiscussed by Bryan (2008) for Fountain Geyser. Infrasonic
waveforms from Fountain Geyser events were also remarkably similar,
beginning and terminating abruptly, with detections lasting between
27 and 36 min (for 11 events). Signal envelope was substantially differ-
ent from that of Great Fountain with less intense pulses, but more
sustained amplitude reminiscent of a stationary volcanic broadband
tremor (Fig. 8b). At the distance of the East Array peak-to-peak tremor
amplitudes occasionally exceeded 0.1 Pa, which would reduce to ~ 1 Pa
at 100 m invoking a 1/rpressure decay for a homogeneous atmosphere.
The nature of the infrasound is suggestive of the descriptions of typical
Fountain activity, which reportedly begins abruptly and then plays in a
sustained fashion with splashing and a wide column up to 15 m in height
(Bryan, 2008). Cumulative infrasound power radiated from Fountain to-
tals several thousand Joules and is comparable to infrasound from Great
Fountain events.
Sporadic infrasound originating from the vicinity of Fountain
Group, but with a slightly more westerly back-azimuth (335°), was
intermittently recorded during our week-long survey. Although this
back-azimuth is close to that of Fountain Geyser (342°) the location
ellipsoids from this source are spatially distinct from Fountain Geyser
and we conclude that they represent a separate source occurring at
slightly greater distant range (~ 2000 m) than the Fountain Group
(~1700 m). We speculate this is Kaleidoscope Group geyser activity
that is characterized by infrasound with a few bursts that last just a
few minutes (e.g., on Julian Day 223 at 07:20 UTC). Based upon the
infrasound character the most likely candidate geyser source is the
namesake Kaleidoscope Geyser, which hosts short-duration activity
(20 to 120 s) that suddenly shoots water jets 15 to 35 m (Bryan,
2008).
5.2.3. Botryoidal and Firehole Springs
Infrasound radiation from Botryoidal Spring is routinely identied
during periods of low background noise, i.e. when other loudergeysers
are quiet and when wind-induced noise is low. During these conditions
the activity from Botryoidal, which is 338 m from the East Array, is
periodic. During our observational period infrasound bursts occurred
with remarkable regularity at intervals of 4.5 +/0.5 min (Figs. 8c
and 10). Interval times between successive eventdetections are consis-
tent with a normal distribution (Fig. 10).
Transientsignal amplitudes from Botryoidal are invariably small and
short in duration, typically only 0.05 Pa and composed of only one or a
couple of 2.5 Hz oscillations. This spectral content is somewhat higher
Fig. 6. Detail beamstack waveforms ltered into ve frequency bands using 2-pole Butterworth lters with the indicated cornerfrequencies. Peak-to-peak signal amplitudes, normalized
correlation coefcients, and associatedpeak correlationlag times are shown for each waveformand each band. Featuredevents correspond to the bestcorrelated signalsoccurring in Fig. 4
from four representative source regions including: a) probable distant thunder source(s) to the Northeast of the LGB, b) Fountain Geyser ~1700 m, c) Botryoidal Spring ~250 m, and d)
Firehole Spring ~150 m from the network center.
112 J.B. Johnson et al. / Journal of Volcanology and Geothermal Research 256 (2013) 105117
113J.B. Johnson et al. / Journal of Volcanology and Geothermal Research 256 (2013) 105117
than the peak acoustic frequencies of the larger Great Fountain and
Fountain Geysers, which were both peaked in the 0.5 to 1.5 Hz band
(Fig. 8a, b). In general the infrasound observations of Botryoidal are con-
sistent with anecdotal reports. In recent years reported periodicity has
ranged between 2.5 and 5.5 min. Eruptions consist of single steam bub-
bles distending the surface of the spring before bursting and throwing
water to heights of 4 to 6 m (Bryan, 2008).
Nearby Firehole Spring is another geothermal feature, which pro-
duces prodigious (but even lower-amplitude) infrasound. Owing to
its near-continuous activity Firehole Spring is sometimes considered
aperpetual spouterwith play typically reaching only a few meters
in height (Bryan, 2008). Its corresponding infrasound is manifested
as a persistent infrasonic tremor of such low intensity that it is gener-
ally not detected except during the most quiet of intervals, when it is
registered as a quasi-continuous source. At a sourcereceiver dis-
tance of ~ 350 m Firehole Spring infrasound is not visually apparent
in time series records and it is at the limits of signal detection using
array analysis techniques. The spectral content of Firehole Spring
Fig. 7. Infrasound detections from a 5-day interval in August 2011. Featured geysers and their detected activity include Great Fountain (GF; blue), Firehole Spring (FS; green),
Botyroidal Spring (BS; red), Fountain and Kaleidescope (FG/KG; cyan), and other sources (other; mauve). Each detection corresponds to coherent energy identied on the EA.
Gray records correspond to 20-s averaged absolute signal amplitudes, analogous to real-time seismic amplitude measurements (Murray and Endo, 1992).
114 J.B. Johnson et al. / Journal of Volcanology and Geothermal Research 256 (2013) 105117
activity is peaked notably higher (> 4 Hz) than the other frequently
detected geysers.
6. Discussion
We recorded infrasound radiation associated with activity from at
least ve fountain-type geysers. Fountain-type geysers erupt steam and
water from open pools and in the process they accelerate large volumes
of the overriding atmosphere, efciently generating low-frequency
acoustic waves. These acoustic waves are dominated by 18Hzinfra-
sound most likely because the time scales of surface accelerations occur
during tenths of seconds. Corresponding wavelengths of 18Hzinfra-
sound is 40 m or longer, much larger than the vent dimensi on of the stud-
ied geysers. As such, sound generation may reasonably be considered as a
compact, point-source volumetric signal, or a monopole. These sounds
carry efciently for hundreds of meters to several kilometers.
We did not observe any denitive infrasound signal produced by
nearby cone-type geysers. Cone-type geysers are generally erupted as
collimated jets of steam and water from a narrow orice (~ 0.1 m) that
is often located at the summit of a mound of sinter (or geyserite). The
nearest cone-type geyser to our dual arrays was White Dome, a regular
performer located only 323 m from the center of the microphone
network. Although White Dome is frequently active with 9 min to
hour-long quiescent intervals and produces a lofty jet up to 10 m it was
not detected by our infrasound surveillance. Unsurprisingly, Pink Cone,
another similar-sized cone-type geyser located farther away (940 m),
was never denitively detected.
We speculate that cone-type geysers do not produce signicant
amounts of infrasound because their volumetric, or monopole, contribu-
tions are small. Instead th ey erupt multi-phase uid jets, which are often
modeled as dipole or quadrupole sources (Woulff and McGetchin, 1976;
Lighthill, 1978; Matoza et al., 2009), and are much less efcient at
ensonifying the atmosphere, especially in the infrasonic band. White
Dome's jet is narrow and fairly low-energy. Larger cone-type geysers,
such as Old Faithful and Lone Star, are more energetic and more likely
to produce intense sounds. Short-duration infrasound surveys of these
geysers during our August 2011 experiment indeed revealed lower
infrasound spectral power and enhanced higher frequency sound, com-
pared to fountain geysers with eruptions of similar height.
Our survey of the LGB conrmed that major fountain-type geysers
are reliably identied with dual microphone arrays at distances of up
to several kilometers. However, we note that we did not detect reliable
signal from the major fountain geyserslocated in the UGB, located more
than 8.5 km away. This suggests a limit of somewhere between 3 and
8 km for infrasound detection of major fountain-type geysers. We also
note that we did not detect activity from any minor fountain type
Fig. 8. One-hour pressure time series, acoustic power, and corresponding power spectral density for select events at: a) Great Fountain, b) Fountain Geyser, and c) Botryoidal Spring.
Data are shown for band-pass ltered (0.2520 Hz) signal. Acoustic power is calculated according to Eq. (11) using Δt = 1 s intervals. Total energy for the hour-long interval is
shown in each panel. Power spectral density shows a combination of ambient infrasonic noise, centered at the 0.25 Hz corner frequency (dashed line), as well as generally higher
frequency geyser signal. Power spectrum from a low-noise one-hour nighttime period is indicated for comparison.
115J.B. Johnson et al. / Journal of Volcanology and Geothermal Research 256 (2013) 105117
geysers farther than about 500 m distant, such as those found in the
Pink Cone Group or the Black Warrior Group. Based upon our observa-
tions of the minor geyser activity at Firehole and Botryoidal Springs we
conclude that to reliably track smaller features it is necessary to deploy
sensors within a few hundred meters of their sources.
7. Conclusion
Dual acoustic arrays separated by approximately 600 m can be used
to identify and track activity from individual geysers out to several kilo-
meters. As such, future monitoring of geyser activity with non-intrusive
acoustic arrays in the infrasound band is warranted. Acoustic monitor-
ing can also complement ongoing efforts to track geyser activity, such
as those measuring thermal ux in geyser outow channels in Norris
Basin (Perry, 2011). Further, acoustic monitoring can facilitate compre-
hensive records of geyser eruption statistics, including repose periods
between eruptions, eruption duration, and style of eruption (e.g., pulsing,
spasmodic or continuous, intense or benign), which will enable a better
understanding of hydrologic controls including exchange of function
with neighboring features (Marler, 1951). Relationships between erup-
tion duration and repose time can be also be robustly studied given con-
tinuous and long-duration monitoring of a system of geysers and should
aid in a better understanding of periodicity controls (Ingebritsen and
Rojstaczer, 1993). Finally, we anticipate that changes in geyser activity,
due to seasonal effects or dynamic strains from transient earthquake
waves (Husen et al., 2004),canbemorerobustlyquantied and studied
with long-term acoustic monitoring.
We have shown that the ability to comprehensively monitor
geysers is affected by both the intensity of the geyser infrasound source,
which appears greater for fountain-type geysers than for cone-type
geysers, and the level of background noise. During local daytime periods
(e.g., 10:00 AM to 9:00 PM) wind was often so intense in LGB as to
obscure all activity except from the nearby Great Fountain. More compre-
hensive monitoring of geyser activity will be facilitated with better strate-
gic deployment of arrays closer to the smaller geothermal features and
utilization of a greater number of arrays. In the future, local monitoring
of the UGB with its incredible population of major geysers will be partic-
ularly illuminating. We believe that infrasound monitoring is an effective
and non-intrusive tool for tracking activity for a cluster of geysers and can
be substantially less work-intensive than relying upon eyewitness or
video observations.
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.jvolgeores.2013.02.016.
Acknowledgments
Fieldwork was carried out with the help from a team of students in a
volcano geophysical eld methods course held in Yellowstone National
Park in 2011. The students involved included the co-authors as well as
A. Curtis, N. Iverson, R. Johnson, D. Krzesni, and A.Quezada-Reyes. We
are indebted to GPS surveying of the sensor arrays by M. Murray. We
also thank the National Park Service research staff for their assistance
with permitting and logistical advice. NSF EAR grant #1151662 sup-
ported this work.
221 221.5 222 222.5 223 223.5 224 224.5 225 225.5 226
0
5
10
15
20
25
30
# detections per minute
1 2 3 4 5 6 7 8 9
a) Great Fountain detections
Julian day
050 100 150
8
10
12
14
16
18
20
event duration (minutes)
event interval (hours)
b) interval statistics
1
2 3
4
5
6
7
8
Aug. 2012 data fit (R2 = 0.73)
Bryan (2008) observations
Fig. 9. a) Five-day record of Great Fountain event detections showing episodicity of events. Arrows are drawn 85 min prior to select main events and coincide with some detected
precursors. b) Duration of event detections plotted against subsequent quiescent interval for 8 events (black circles). Event interval is measured as the time between a main event's
last detection and the next event's rst detection.
Fig. 10. Distribution of event intervals for detected Botryoidal Spring eruptions. Inter-
vals are dened as time differences between center times of successive event detec-
tions. Normal distribution t to data is shown.
116 J.B. Johnson et al. / Journal of Volcanology and Geothermal Research 256 (2013) 105117
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... Infrasound monitoring, which is increasingly used at volcanoes (De Angelis et al., 2019;Johnson & Ripepe, 2011;Watson et al., 2022), may help address this challenge. Analysis of data from a week-long deployment of an infrasound array in the Lower Geyser Basin, Yellowstone readily discriminated bursting activity from nearby fountain-type geysers but did not register jetting from cone-type geysers (Johnson et al., 2013). Jetting can still be detected if it is both tall and energetic: Steamboat's eruptions have been recorded through targeted infrasound monitoring (Holahan et al., 2021) as have Old Faithful's and Lone Star's (Johnson et al., 2013). ...
... Analysis of data from a week-long deployment of an infrasound array in the Lower Geyser Basin, Yellowstone readily discriminated bursting activity from nearby fountain-type geysers but did not register jetting from cone-type geysers (Johnson et al., 2013). Jetting can still be detected if it is both tall and energetic: Steamboat's eruptions have been recorded through targeted infrasound monitoring (Holahan et al., 2021) as have Old Faithful's and Lone Star's (Johnson et al., 2013). Pilot studies should be conducted for planned infrasound arrays and/or seismometers in multiple seasons to ensure signals can be recorded year-round. ...
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Plain Language Summary What we learn about geysers and volcanoes depends on when, where, and how we are able to monitor them. Here we present a case study of how seasonal changes affect data recorded on a seismometer, which is an instrument that measures ground motion. The world's tallest geyser, Steamboat Geyser in Yellowstone National Park, has intense eruptions that eject a mixture of water and steam. The eruptions are powerful enough to cause tiny ground motions from sound waves that begin in the air and then transfer into the ground. In the winter, we see smaller ground motions at two nearby seismometers. This might imply that Steamboat's eruptions are weaker in the winter; however, winter in Yellowstone comes with snow, and snow is good at absorbing sound wave energy. We find that smaller ground motions occur when snow depths are greater, and that the strength of ground motions should not be used to directly compare eruption intensity. Few geysers around the world are monitored with scientific equipment for long periods of time. Our result highlights the need for more of this type of monitoring so that we can identify biases that may be missed during shorter investigations.
... 2), and acoustic waves (Pa) were recorded at a sampling frequency of 200 Hz. In fountain-type geysers, the timing of an eruption and its duration can be estimated from acoustic signals(Nishimura et al., 2006;Johnson et al., 2013). Timeseries data of the seismometer and acoustic sensor were recorded using an HKS-9700 logger (Keisokugiken Corp.).3-6. ...
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From April to July 2021, West Crater at Iwo-Yama, Kirishima Volcanic Complex, Japan, was repeatedly filled with hydrothermal water and subsequently evacuated. The overall cycle lasted 14–70 h, and the course of a single cycle followed this sequence of phases: (i) steam effusion disappeared 20–40 min before hydrothermal discharge; (ii) hydrothermal discharge occurred, generating a hydrothermal water pool; (iii) steam effusion resumed and gradually increased; and (iv) drain-back (evacuation) of the hydrothermal water occurred 1–1.5 h before the onset of the next hydrothermal discharge. We used multi-parametric observations (optical camera, thermometer, electric self-potential (SP), seismometer, acoustic sensor, and tiltmeter) to investigate the cause of the cyclic hydrothermal discharge. A change in SP data occurred approximately 2 h before the onset of hydrothermal discharge. However, the change in SP was small when hydrothermal discharge did not occur. The temporal change in SP is inferred to have been caused by groundwater flow through the region below West Crater, implying that groundwater flow was occurring 2 h before hydrothermal discharge. The polarity of SP change suggests that groundwater flowed toward the region underlying the vents. Seismic signals in the frequency range of < 20 Hz decreased 15–45 min after the onset of change in SP. This seismic signal pattern is inferred to have been caused by bubble activity in boiling fluid. We interpret that the inflow of cold groundwater inhibited boiling activity in the conduit, which in turn caused the cessation of both steam effusion and seismic activity. SP data suggest that the inflow of cold groundwater gradually decreased before hydrothermal discharge. Pressurization sufficient to force the water in the upper part of the conduit to ascend could have built up in the lower part of the conduit owing to a decrease in the input of groundwater into the upper part of the conduit and the continuing supply of steam bubbles and hot water. This increase in pressure finally led to hydrothermal discharge at the surface. We suggest that the inflow of cold groundwater into the geyser conduit was the key control on the occurrence and cyclicity of hydrothermal discharge in West Crater at Iwo-Yama.
... II.V.III Volcanic eruptions, earthquakes, rockfalls, etc. Processes such as volcanic eruption, earthquakes and major rockfalls and avalanches can produce both audible sound (Romeo et al. 2021, Sylvander andMogos 2005;Singh et al., 2017;Tosi et al., 2012;Brink et al. 2016) and infrasound. Geothermal energy drives geyser activity, and this too can be monitored, analysed and classified using acoustic methods (Johnson et al., 2013). Johnson and Ronan (2015) demonstrated the successful infrasound monitoring of rockfall events on the flanks of the andesitic Santiaguito volcano (Guatemala), in which the timing of occurrence, size and distance travel of rockfalls could be interpreted. ...
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Sound is produced by many geomorphic and hydrological processes, such as rockfalls and landslides, ocean waves, fluvial flood flows and collisions among moving bedload clasts. In these and other areas of study, acoustic methods have found useful application to detect and quantify the operation of important landscape processes. In some, such as the recording of river discharge, the occurrence of rare events such as exfoliation or the presence and movement of dust devils (willy-willies), the use of acoustic methods is still in a relatively early stage of development and testing. The use of acoustic methods in the recording of rainfall occurrence and intensity is also developing and has the capacity to yield data with higher temporal resolution than can be achieved using conventional rain gauges. Novel acoustic methods include the analysis of the sound recorded by security cameras, which potentially form a vast network of observing stations. The frequencies of sound generated by land-surface processes include audible sound, ultrasound and infrasound at frequencies below the human hearing range. All appear to provide opportunities for further development of useful research tools and methodologies.
... The regular integration of DEMs in numerical modeling (Kim et al. 2015) now provides more accurate VFR estimates for short duration explosions that have been validated with independent SO 2 , tephra, and thermal measurements (Dalton et al. 2010;Delle Donne et al. 2016;. Estimating VFR for volcanic jet flows will require observing gas-to ash-rich flows with jet diameter length scales of meters (e.g., fumaroles, geysers) (Johnson et al. 2013;McKee et al. 2017) to hundreds of meters (i.e., VEI 4 + eruptions) (e.g., Matoza et al. 2009a, b;Fee et al. 2010;McKee et al. 2021a,b) with acoustic observations that extend vertically using airborne sensors (e.g., Jolly et al. 2017;Iezzi et al. 2019a;Brissaud et al. 2021), up-to-date DEMs, and high-speed thermal and visual data (e.g., Taddeucci et al. 2012;Gaudin et al. 2016). Field observations should be combined with analogue experiments (e.g., Medici et al. 2014;Cigala et al. 2017;Peña Fernández et al. 2020;Schmid et al. 2020), numerical modeling (e.g. ...
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Over the past two decades (2000–2020), volcano infrasound (acoustic waves with frequencies less than 20 Hz propagating in the atmosphere) has evolved from an area of academic research to a useful monitoring tool. As a result, infrasound is routinely used by volcano observatories around the world to detect, locate, and characterize volcanic activity. It is particularly useful in confirming subaerial activity and monitoring remote eruptions, and it has shown promise in forecasting paroxysmal activity at open-vent systems. Fundamental research on volcano infrasound is providing substantial new insights on eruption dynamics and volcanic processes and will continue to do so over the next decade. The increased availability of infrasound sensors will expand observations of varied eruption styles, and the associated increase in data volume will make machine learning workflows more feasible. More sophisticated modeling will be applied to examine infrasound source and propagation effects from local to global distances, leading to improved infrasound-derived estimates of eruption properties. Future work will use infrasound to detect, locate, and characterize moving flows, such as pyroclastic density currents, lahars, rockfalls, lava flows, and avalanches. Infrasound observations will be further integrated with other data streams, such as seismic, ground- and satellite-based thermal and visual imagery, geodetic, lightning, and gas data. The volcano infrasound community should continue efforts to make data and codes accessible and to improve diversity, equity, and inclusion in the field. In summary, the next decade of volcano infrasound research will continue to advance our understanding of complex volcano processes through increased data availability, sensor technologies, enhanced modeling capabilities, and novel data analysis methods that will improve hazard detection and mitigation.
... Finally, it has been demonstrated that a variety of natural phenomena (e.g., earthquakes and landslides) and anthropogenic processes (e.g., geothermal energy development) (e.g., Scott & Cody 2000, Barrick 2007, Steingisser & Marcus 2009, Saptadji et al. 2016 can change geyser eruption intervals or terminate geyser activity. Therefore, continuous and long-term records of geyser eruption durations and intervals using temperature sensors in outflow channels (Hurwitz et al. 2008 or infrasound ( Johnson et al. 2013) should be collected. These data can guide the protection and preservation of the unique and diverse geysers on Earth. ...
Article
Geysers episodically erupt liquid and vapor. Despite two centuries of scientific study, basic questions persist—why do geysers exist? What determines eruption intervals, durations, and heights? What initiates eruptions? Through monitoring eruption intervals, analyzing geophysical data, taking measurements within geyser conduits, performing numerical simulations, and constructing laboratory models, some of these questions have been addressed. Geysers are uncommon because they require a combination of abundant water recharge, magmatism, and rhyolite flows to supply heat and silica, and large fractures and cavities overlain by low-permeability materials to trap rising multiphase and multicomponent fluids. Eruptions are driven by the conversion of thermal to kinetic energy during decompression. Larger and deeper cavities permit larger eruptions and promote regularity by isolating water from weather variations. The ejection velocity may be limited by the speed of sound of the liquid + vapor mixture. Expected final online publication date for the Annual Review of Earth and Planetary Sciences Volume 45 is May 30, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... At the Earth's surface, the interpretation of seismic and infrasound signals can be supplemented by observations of surface activity to infer hydrothermal/magmatic processes (e.g. Bouche et al., 2010;Gerst et al., 2013;Karlstrom et al., 2013) and a range of signals are observed in audible (acoustic) and infrasound frequencies (e.g., Matoza et al., 2007;Ripepe et al., 2010;Johnson et al., 2013) including tremor like signals (e.g., Hagerty et al., 2000;Fee et al., 2010), discrete burst events (e.g., Ripepe et al., 1996) and directed jets (e.g., Rowell et al., 2014). ...
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White Island volcano, New Zealand, produced two periods (January–February and July 2013) of episodic and persistent eruptions through a viscous shallow mud/sulphur pool. The eruptions included an initial hemispherical bubble burst, which was intermittently followed by an up-channel gas jet, and finally a late stage heaving of a mud/sulphur/water suspension. The late stage heave was systematically directed south-eastward as far as 30–40 m from the vent.
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We combine global detections of volcanic lightning with acoustic and hydroacoustic data to investigate novel indications of plume electrification in ground-based, geophysical data streams during the 2016–2017 eruption of Bogoslof volcano, Alaska. Such signals offer additional ways to diagnose the occurrence of volcanic lightning and confirm whether eruptive activity is producing significant amounts of ash. We discuss three signatures of lightning activity: volcanic thunder, electromagnetic pulses arising from lightning-induced voltages in cabling, and hydroacoustic signals associated with volcanic lightning. Observations of these phenomena provide additional insights into volcanic lightning activity and reveal several periods of electrical activity that were not otherwise detected during the Bogoslof eruption.
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The gas-thrust region of a large volcanic eruption column is predominately a momentum-driven, fluid flow process that perturbs the atmosphere and produces sound akin to noise from jet and rocket engines, termed “jet noise”. We aim to enhance understanding of large-scale volcanic jets by studying an accessible, less hazardous fumarolic jet. We characterize the acoustic signature of ~ 2.5-meter wide vigorously jetting fumarole at Aso Volcano, Japan using a 5-element infrasound array located on the nearby crater. The fumarole opened on 13 July 2015 on the southwest flank of the partially collapsed pyroclastic cone within Aso Volcano's Naka-dake crater and had persistent gas jetting, which produced significant audible jet noise. The array was ~ 220 m from the fumarole and 57.6° from the vertical jet axis, a recording angle not typically feasible in volcanic environments. Array processing is performed to distinguish fumarolic jet noise from wind. Highly correlated periods are characterized by sustained, low-amplitude signal with a 7–10 Hz spectral peak. Finite difference time domain method numerical modeling suggests the influence of topography near the vent and along the propagation path significantly affects the spectral content, complicating comparisons with laboratory jet noise. The fumarolic jet has a low estimated Mach number (0.3 to 0.4) and measured temperature of ~ 260 °C. The Strouhal number for infrasound from volcanic jet flows and geysers is not known; thus we assume a peak Strouhal number of 0.19 based on pure-air laboratory experiments. This assumption leads to an estimated exit velocity of the fumarole of ~ 79 to 132 m/s. Using published gas composition data from 2003 to 2009, the fumarolic vent area estimated from thermal infrared images, and estimated jet velocity, we estimate total volatile flux at ~ 160–260 kg/s (14,000–23,000 t/d).
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VOLCANIC eruptions are sometimes accompanied by a characteristic type of seismicity known as harmonic tremor, in which the signal is dominated by discrete vibration frequencies1-4. This harmonic structure could reflect resonance behaviour in the excitation source4-6 or filtering of the seismic waves as they propagate through the surrounding rocks7-10 but complexity and variability in the properties of volcanic systems make it difficult to discriminate between such mechanisms. To address this question, we have analysed the source and propagation characteristics of seismicity at Old Faithful geyser (Yellowstone National Park), the cyclic behaviour and accessibility of which make it an ideal natural laboratory for studying harmonic tremor associated with near-surface sources. We find that sharp pressure pulses inside the water column trigger distinct seismic events that give rise to a harmonic ground response whose frequency varies spatially but not temporally. A superposition of these seismic events creates the appearance of continuous harmonic tremor. The absence of resonance within the water column suggests that the harmonic motion must arise from the interaction of the seismic waves with heterogeneities in the surrounding elastic medium-most probably a near-surface soft layer.
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Volcanic tremor at Stromboli (Aeolian islands, Italy) is correlated to small infrasonic transients [Ripepe et al., 1996] which repeat almost rythmically in time in a range between 0.8 and 1.2 s. We demonstrate that infrasonic transients are associated to small gas bubble (~0.5 m) burstings which produces no transients in the seismic signal. Tremor ground displacement attenuates with the inverse of the distance from the craters indicating that the source is shallow. Short-term energy release shows that infrasonic and seismic signals are linked to the same dynamical process, while at the long-term scale it is evident that the two signals are controlled by two distinctive mechanisms. We suggest that the possible physical model acts in two steps: first, gas coalescence and, then, gas bursting. In our model, the seismic signal is related to the coalescence of a gas bubble from a layer of small bubbles, while the infrasonic signal is linked to the bursting of the bubble when it reaches the magma surface. Gas bubbles could form by free coalescence in magma or could be forced to coalesce by a structural barrier. We calculate that forced coalescence induces in magma a pressure change (~104Pa) 2 orders of magnitude higher than free coalescence, and it explains best the tremor ground displacement (10-5m) recorded at Stromboli. Moreover, forced coalescence evidences the role of a structural barrier, such as a dike, in volcanic tremor source dynamics. In this gas dynamic process, the delay time of 1-2 s between infrasonic pulses could reflect the gas nucleation interval of basaltic magma [Thomas et al., 1993; Manga, 1996]. We propose that the source function for the shallow volcanic tremor at Stromboli could be the viscoelastic reaction of the magma to the pressure decrease induced by gas bubble growth rate under constant depressurization. The spectrum of our source function is controlled by the time duration of the pressure pulse, which represents the viscoelastic relaxation time of the magma and gas bubble growth rate. The predicted asymptotic decay of the frequency contents fits the spectral behavior of the volcanic tremor ground displacement recorded at Stromboli. We show that the same spectral behavior can be found in ground displacement spectra of volcanic tremor recorded on different volcanoes.
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Volcanic activity which involves the vigorous flow of gases, such as strombolian eruptions and energetic fumarole activity, is commonly accompanied by noise or acoustic radiation caused by the interaction of the gas with the stationary solid boundaries of the vent as well as the turbulence of the gas in the jet itself. Analysis of sounds (both total power emitted and frequency spectra) produced during volcanic eruptions will provide detailed quantitative information concerning gas velocity history. Theoretical considerations suggest that acoustical power radiated during gaseous volcanic eruptions may be related to gas exit velocity by power laws of the form PαVⁿ, where n ranges from 4 to 8 depending on the type of radiation involved. Noise from energetic fumaroles atop Volcan Acatenango, Guatemala, was recorded, analysed, and interpreted in terms of the nature of the radiation produced and the implied gas velocities. This gas vent noise is found to be dipole radiation (due to interaction of the gas jet and solid boundaries), with quadrupole radiation (aerodynamic sound), and monopole radiation (source noise due to changes in mass flux) playing an insignificant role. For the dipole case, total radiated power, PD is approximately: PD=KDρoAD αo³V⁶ where ρo is gas density, AD the vent area, ao the sound speed, V gas velocity, and KD an empirically‐determined constant which our field data suggests is in the range 10⁻² to 10⁻¹. Eruption acoustics appear to constitute a means of quantitatively monitoring volcanic activity which, along with other techniques such as photography and seismology, can yield data bearing on eruption dynamics.
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The signing of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) on 24 September 1996 and the establishment of the International Monitoring System (IMS) for Treaty verification has led to a rapid development in the use of infrasound monitoring technology for the detection of nuclear explosions. The IMS includes a 60-station infrasound monitoring network that is designed to reliably detect infrasonic signals from a 1-kiloton atmospheric nuclear explosion at two or more network stations. The stations in this network are located uniformly over the face of the globe. Each station consists of an array of high-sensitivity microbarometer sensors arranged in an optimal configuration for the detection of signals from atmospheric explosions. The construction of this global infrasound monitoring system is nearing completion. In this chapter, we focus on the fundamental design principles for IMS infrasonic array stations with an emphasis on the recent developments in array design, improvements in infrasound sensor technology, and advances in background noise reduction that can potentially improve the monitoring capability and reliability of the global network.
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The implementation, characterization, and evaluation of a low-cost infrasound sensor developed at the lnfrasound Laboratory at the New Mexico Institute of Mining and Technology (Infra-NMT) are described. This sensor is based on a commercial micromachined piezoresistive differential pressure transducer that uses a mechanical high-pass filter to reject low-frequency outband energy. The sensor features a low-noise, 2.02-mPa rms (0.5-2 Hz), 5.47-mPa rms (0.1-20 Hz), or 5.62-mPa rms (0.05-20 Hz), flat response between 0.01 and at least 40 Hz; inband sensitivity of 45.13 +/- 0.23 mu V Pa-1; and a nominal linear range from -124.5 to +124.5 Pa. Intended for outdoor applications, the influence of thermal changes in the sensor's response has been minimized by using a thermal compensated pressure transducer powered by an ultralow drift (<5 ppm degrees C-1) and noise (<4 mu V from peak to peak) voltage reference. The sensor consumes a minimum of 24 mW and operates with voltages above 8 V while drawing 3 mA of current. The Infra-NMT specifications described above were independently verified using the infrasound test chamber at the Sandia National Laboratories' (SNL's) Facility for Acceptance, Calibration, and Testing (FACT), and the following procedures are for comparison calibration against traceable reference stands in voltage and pressure. Because of the intended broad frequency response of this sensor, the testing chamber was configured in a double-reference sensor scheme. A well-characterized microbarometer (with a flat-amplitude response between 0.01 and 8 Hz) and a microphone (with a flat-amplitude response above 8 Hz) were used simultaneously in this double-reference test configuration.
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Sound waves are considered, taking into account the wave equation, the speed of sound, acoustic energy and intensity, the simple source, the acoustic dipole, compact source regions in general, compact source regions with dipole far fields, ripple-tank simulations, scattering by compact bodies, quadrupole radiation, radiation from spheres, radiation from plane walls, and dissipation of acoustic energy. One-dimensional waves in fluids are discussed along with water waves and internal waves. Attention is given to longitudinal waves in tubes and channels, the transmission of waves through junctions, propagation through branching systems, linear propagation with gradually varying composition and cross-section, frictional attenuation, the nonlinear theory of plane waves, simple waves, shock waves, the theory of simple waves incorporating weak shock waves, hydraulic jumps, nonlinear geometrical acoustics, surface gravity waves, the Fourier analysis of dispersive systems, the energy propagation velocity, wave patterns made by obstacles in a steady stream, ship waves, and a general theory of oscillating sources of waves.
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The ambient infrasound noise environment is characterized for 21 globally distributed infrasound arrays in the frequency band of 0.03 to 7 Hz. Power Spectral Density (PSD) is measured for one site of each array for 21 intervals at each of four times of day from January 2003 through January 2004. The ambient noise at infrasound stations is highly variable by season, time of day and station. Noise spectra for an individual station may vary by four orders of magnitude at any given frequency. Preliminary infrasound noise models are defined, which can be used as baselines for evaluating ambient noise at current and new infrasound stations. Median noise levels in the microbarom band centered on 0.2 Hz vary smoothly in an annual pattern, with most stations observing maximum noise during local winter. Noise amplitudes do not have a normal or log-normal distribution, but rather are skewed to larger amplitudes.
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We use acoustic (3.3-500 Hz) arrays to locate local (<20 km) thunder produced by triggered lightning in the Magdalena Mountains of central New Mexico. The locations of the thunder sources are determined by the array back azimuth and the elapsed time since discharge of the lightning flash. We compare the acoustic source locations with those obtained by the Lightning Mapping Array (LMA) from Langmuir Laboratory, which is capable of accurately locating the lightning channels. To estimate the location accuracy of the acoustic array we performed Monte Carlo simulations and measured the distance (nearest neighbors) between acoustic and LMA sources. For close sources (<5 km) the mean nearest-neighbors distance was 185 m compared to 100 m predicted by the Monte Carlo analysis. For far distances (>6 km) the error increases to 800 m for the nearest neighbors and 650 m for the Monte Carlo analysis. This work shows that thunder sources can be accurately located using acoustic signals.
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Coincident with the arrival of low-frequency, large-amplitude surface waves of the Mw 7.9 Denali fault earthquake (dfe), an abrupt increase in seismicity was observed in the Yellowstone National Park region, despite the large epicentral distance of 3100 km. Within the first 24 hr following the dfe mainshock, we located more than 250 earthquakes, which occurred throughout the entire Yellowstone National Park region. The elevated seismicity rate continued for about 30 days and followed a modified Omori law decay with a P value of 1.02 ± 0.07. For a declustered earthquake catalog, the seismicity following the 2002 dfe uniquely stands out with a significance of 30σ. The increase in seismicity occurred over all magnitude bands. In general, we observed that seismicity following the dfe outlined the spatial pattern of past seismicity routinely observed in the Yellowstone National Park region. However, we found significant differences in triggered seismicity inside and outside the caldera. Earthquakes inside the Yellowstone caldera occurred preferentially as clusters close to major hydrothermal systems, were of larger magnitude, and seismicity decayed more rapidly. This suggests that either different trigger mechanisms were operating inside and outside the caldera or that the crust responded differently to the same trigger mechanism depending on its different mechanical state. Compared with other sites that experienced remote earthquake triggering following the 2002 dfe, Yellowstone showed the most vigorous earthquake activity. We attribute this to strong directivity effects of the dfe, which caused relatively large peak dynamic stresses (0.16–0.22 MPa) in Yellowstone, and to the volcanic nature of Yellowstone.