1 Tracking changes at volcanoes with seismic interferometry 1 2 MM Haney1, AJ Hotovec-Ellis2 ...

51 Pages·2014·3.78 MB·English

1 Tracking changes at volcanoes with seismic interferometry
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3 M. M. Haney1, A. J. Hotovec-Ellis2, N. L. Bennington3, S. De Angelis4, and C. Thurber3
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5 1U.S. Geological Survey, Alaska Volcano Observatory, Anchorage, AK, USA
6 2University of Washington, Seattle, WA, USA
7 3University of Wisconsin-Madison, Madison, WI, USA
8 4University of Liverpool, Liverpool, UK
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10 Abstract
11 Volcano monitoring hinges on the ability to detect unrest at the earliest moment possible.
12 Relevant types of changes at volcanoes that are routinely monitored include variations in
13 seismicity, deformation, infrasonic radiation, gas emission, and surface temperature.
14 Changes in subsurface structure at volcanoes have only recently begun to be detected and
15 quantified in a systematic way using techniques broadly referred to as seismic
16 interferometry and coda wave interferometry. This article presents an overview and
17 discussion of the application of these methods at volcanoes. Case studies from Okmok
18 and Pavlof volcanoes in the Aleutian Arc illustrate different applications of the method.
19 At Okmok, analysis of pairs of broadband seismometers within the 10 km wide caldera
20 leads to the observation of seasonal changes due to snow loading from correlations of
21 ambient noise. At Pavlof, repeating explosions during the 2007 eruption offer a different
22 type of source for interferometry. Although co-eruptive time-lapse changes in the conduit
23 at Pavlof have been previously documented, the precise mechanism of the change
24 remains unresolved beyond a few hypothesized models capable of explaining the
25 observations. Two new developments in coda wave interferometry, based on the use of
26 stable noise sources and the detection of scattering changes in the subsurface, show
27 promise for expanding the use of interferometry at volcanoes.
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29 1. Introduction
30 The detection and evaluation of time-dependent changes at volcanoes forms the
31 foundation upon which successful volcano monitoring is built. Temporal changes at
32 volcanoes occur over all time scales and may be obvious (e.g., earthquake swarms) or
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33 subtle (e.g., a slow, steady increase in the level of tremor). Some of the most challenging
34 types of time-dependent change to detect are those that occur at depth, beneath active
35 volcanoes. Although difficult to measure, such changes carry important information
36 about stresses and fluids present within hydrothermal and magmatic systems. These
37 changes are imprinted on seismic waves that propagate through volcanoes.
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39 In recent years, there has been a quantum leap in the ability to detect subtle structural
40 changes systematically at volcanoes with seismic waves. The new methodology is based
41 on the idea that useful seismic signals can be generated “at will” from seismic noise
42 [Haney et al., 2009]. This means signals can be measured any time, in contrast to the
43 often irregular and unpredictable times of earthquakes. With seismic noise in the
44 frequency band 0.1-1 Hz arising from the interaction of the ocean with the solid Earth
45 known as microseisms, researchers have demonstrated that cross-correlations of passive
46 seismic recordings between pairs of seismometers yield coherent signals [Campillo and
47 Paul, 2003; Shapiro and Campillo, 2004]. Based on this principle, coherent signals have
48 been reconstructed from noise recordings in such diverse fields as helioseismology
49 [Rickett and Claerbout, 2000], ultrasound [Weaver and Lobkis, 2001], ocean acoustic
50 waves [Roux and Kuperman, 2004], regional [Shapiro et al., 2005; Sabra et al., 2005;
51 Bensen et al., 2007] and exploration [Draganov et al., 2007] seismology, atmospheric
52 infrasound [Haney, 2009], and studies of the cryosphere [Marsan et al., 2012].
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54 Initial applications of ambient seismic noise were to regional surface wave tomography
55 [Shapiro et al., 2005]. Brenguier et al. [2007] first used ambient noise tomography
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56 (ANT) to map the 3D structure of a volcanic interior at Piton de la Fournaise. Subsequent
57 studies have imaged volcanoes with ANT at Okmok [Masterlark et al., 2010], Toba
58 [Stankiewicz et al., 2010], Katmai [Thurber et al., 2012], Asama [Nagaoka et al., 2012],
59 Uturuncu [Jay et al., 2012], and Kilauea [Ballmer et al., 2013b]. In addition, Ma et al.
60 [2013] have imaged a scatterer in the volcanic region of Southern Peru by applying array
61 techniques to ambient noise correlations. Prior to and in tandem with the development of
62 ANT, researchers discovered that repeating earthquakes, which often occur at volcanoes,
63 could be used to monitor subtle time-dependent changes with a technique known as the
64 doublet method or coda wave interferometry (CWI) [Poupinet et al., 1984; Roberts et al.,
65 1992; Ratdomopurbo and Poupinet, 1995; Snieder et al., 2002; Pandolfi et al., 2006;
66 Wegler et al., 2006; Martini et al., 2009; Haney et al., 2009; De Angelis, 2009; Nagaoka
67 et al., 2010; Battaglia et al., 2012; Erdem et al., 2013; Hotovec-Ellis et al., 2014]. Chaput
68 et al. [2012] have also used scattered waves from Strombolian eruption coda at Erebus
69 volcano to image the reflectivity of the volcanic interior with body wave interferometry.
70 However, CWI in its original form was limited in that repeating earthquakes, or doublets,
71 were not always guaranteed to occur. With the widespread use of noise correlations in
72 seismology following the groundbreaking work by Campillo and Paul [2003] and
73 Shapiro [2005], it became evident that the nature of the ambient seismic field, due to its
74 oceanic origin, enabled the continuous monitoring of subtle, time-dependent changes at
75 both fault zones [Wegler and Sens-Schönfelder, 2007; Brenguier et al., 2008b; Wegler et
76 al., 2009; Sawazaki et al., 2009; Tatagi et al., 2012] and volcanoes [Sens-Schönfelder
77 and Wegler, 2006; Brenguier et al., 2008a] without the need for repeating earthquakes.
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79 Seismic precursors to eruptions based on ambient noise were first detected at Piton de la
80 Fournaise volcano on the island of Reunion [Brenguier et al., 2008a; Duputel et al.,
81 2009]. The studies at Piton de la Fournaise demonstrated the possibility of resolving
82 small (~0.1 %) decreases in seismic velocity in the weeks leading up to eruptions.
83 Brenguier et al. [2008a] and Duputel et al. [2009] further showed how subtle spatial and
84 temporal changes at the volcano could be mapped and used as a real-time tool for
85 volcano monitoring and eruption forecasting. Traditionally, the forecasting ability of
86 volcano seismology has rested on the assumption that volcanic unrest is preceded in
87 advance by a significant increase in seismicity. However, some eruptions, such as Okmok
88 in 2008 [Larsen et al., 2009], have begun with little or no precursory seismicity. For
89 those eruptions, advance warnings cannot be made and the public is at risk of being
90 exposed to the harmful effects of volcanic activity. Methods based on ambient noise have
91 the potential to assist with forecasting of volcanic unrest in such cases.
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93 2. Principles of Coda Wave Interferometry
94 CWI can in principle detect several types of temporal variations, among them changes in
95 subsurface velocity, changes in the source location, changes in bulk scattering properties,
96 and changes in the focal mechanism of earthquakes [Snieder, 2006]. The sensitivity to
97 subsurface velocity changes initially led to the widespread adoption of CWI for
98 applications at volcanoes. The sensitivity to subsurface velocity can be shown in simple
99 terms with a model of a homogeneous halfspace of velocity v with randomly distributed,
100 small-scale scatterers. For this model, the travel time t for a particular scattering path is
101 given simply as
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d
103 t= (1)
v
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105 where d is the total distance traveled for that path. Note that this distance is not
106 necessarily along a straight-line path. To analyze variations in traveltime, assume that the
107 velocity changes by an amount Δv but the locations of the scatterers do not change. Since
108 the locations do not change, the distance d traveled by each path stays the same. However,
109 the change in velocity Δv causes there to be a resulting change in the traveltime Δt,
110 causing the relation in equation (1) to become
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d
112 t+!t= (2)
v+!v
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114 Assuming the changes are small and taking a Taylor series approximation of equation (2)
115 yields
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d# !v&
117 t+!t= %1" ( (3)
v$ v '
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119 Combining equations (1) and (3) finally gives the well-known result [Snieder, 2006]
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!t !v
121 =" (4)
t v
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123 Equation (4) is widely used in CWI and it states that the fractional traveltime change is
124 equal to the negative of the fractional velocity change. Thus, by measuring the fractional
125 traveltime change, the change in the subsurface can be estimated. Three different
126 methods have been proposed to measure the time shifts in CWI: time-windowed cross-
127 correlations [Snieder et al., 2002], the stretching method [Wegler and Sens-Schönfelder,
128 2007], and the phase of the cross-spectrum [Poupinet et al., 1984]. Note that equation (4)
129 applies to direct waves as well as scattered or coda waves. However, for a given velocity
130 change, the time shift is greater for waves with longer traveltimes, i.e. scattered or coda
131 waves. Thus, CWI, in contrast to many other seismic techniques, benefits from higher
132 amounts of scattering since later-arriving waves have larger and more clearly identified
133 time shifts.
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135 An alternative to the above derivation is for a changing 1D resonator of length d and with
136 an internal propagation velocity v. Resonators have been discussed extensively in volcano
137 seismology as models for sources acting in a crack or cavity [e.g., Fee et al., 2010]. This
138 model is different from the model of randomly distributed scatterers but still produces a
139 sequence of late-arriving waves. In this case, the derivation proceeds along the same lines
140 as shown above, except that the length d is also allowed to vary, yielding the following
141 expression for the traveltime change:
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!t !d !v
143 = " (5)
t d v
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145 Haney et al. [2009] have interpreted CWI delay times measured from repeating
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146 explosions at Pavlof Volcano in the context of this resonator model. As a result, the
147 observed traveltime change could be interpreted as a change in the length of the resonator,
148 a change in the velocity of the material inside the resonator, or a suitable combination of
149 both types of changes (see discussion on page 173 of Garces and McNutt [1997]). The
150 implication is that changing properties of the resonator, or conduit, control the changes
151 observed in the coda of the repeating explosions at Pavlof. Landro and Stammiejer
152 [2004] similarly make use of equation (5) for interpreting time-lapse changes from a
153 subsurface layer in an exploration seismic setting.
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155 3. Overview of Coda Wave Interferometry at Volcanoes
156 Coda waves of repeating earthquakes, or doublets, have been used to observe temporal
157 changes in the subsurface for several decades [Poupinet et al., 1984]. As described by
158 Snieder et al. [2002], CWI takes advantage of subtle time shifts in the coda of repeating
159 seismic events. In CWI, the time-lag between events as a function of recording time is
160 determined via time-windowed cross-correlation of the traces. From the observed time-
161 lags, a corresponding change in velocity can be determined. The technique can detect
162 small changes at volcanoes [e.g., Haney et al., 2009; Nagaoka et al., 2010, Hotovec-Ellis
163 et al., 2014], with temporal resolution on the same order as the rate of the repeating
164 events [e.g., hourly resolution in Hotovec-Ellis et al., 2013]. However, the requirement of
165 repeating events precludes the use of CWI at many volcanoes where repeating events are
166 infrequent or short-lived. In contrast, ambient noise occurs continuously and, if the signal
167 is highly repeatable, offers the ability to observe temporal changes at volcanoes at will
168 [Sens-Schönfelder and Wegler, 2006].
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170 Since the field of interferometry is in its early stages, only a handful of studies [Sens-
171 Schönfelder and Wegler, 2006; Brenguier et al., 2008a; Duputel et al., 2009; Baptie,
172 2010; Mordret et al., 2010; Anggono et al., 2012; Obermann et al., 2014] have employed
173 ambient seismic noise to study changes at volcanoes. The existing studies have typically
174 found velocity decreases at volcanoes due to or preceding activity: -0.5% by Baptie
175 [2010], -2.3 to 3.3% by Anggono et al. [2012], -0.8% by Mordret et al. [2010], and -0.4%
176 by Brenguier et al. [2008a]. The changes detected with ambient noise to date include
177 seasonal variations [Sens-Schönfelder and Wegler, 2006], eruption precursors due to
178 magma pressurization [Brenguier et al., 2008a; Duputel et al., 2009; Anggono et al.,
179 2012], post-eruption changes in the volcanic edifice due to dome collapse [Baptie, 2010],
180 and changes in the hydrothermal system [Mordret et al., 2010].
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182 The determination of subtle changes in velocity using ambient noise interferometry is
183 carried out in a similar way to the process used in CWI with repeating earthquakes.
184 Following Duputel et al. [2009], a long time period seismic correlation between station
185 pairs is computed, and this correlation represents the reference correlation function (CF).
186 The reference CF must be determined over a period of quiescence at the volcano
187 [Duputel et al., 2009]. The reference CF in Duputel et al. [2009] was generated during a
188 two month period when Piton de la Fournaise was relatively quiet. To identify temporal
189 variations in velocity, they determined the current CF as the seismic correlation between
190 a station pair over a smaller period of time than the reference CF by averaging over a
191 time period of 10 days. Duputel et al. [2009] and Hadziioannou et al. [2009]
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192 demonstrated that the stretching technique is a stable method of determining the relative
193 velocity change between station pairs showing significant time lags. The stretching
194 method is an alternative to the traditional method of time-windowed correlations [Snieder
195 et al., 2002]. In the stretching method, the reference CF is stretched or compressed to best
196 match the current CF. This stretched/compressed CF is calculated using an assumed
197 relative change in velocity. Over a set of possible relative velocity changes, the relative
198 velocity change that yields the best correlation between the stretched/compressed CF and
199 the current CF is selected. Note that the stretching method assumes a uniform velocity
200 change in the subsurface when measuring time delays between signals. This need not be
201 the case, as demonstrated by Pacheco and Snieder [2006] in their study of time delays
202 from localized velocity perturbations. There remains debate over the relative performance
203 of the stretching method and time-windowed correlations [Zhan et al., 2013]. A third
204 method, known as the cross-spectrum method [Poupinet et al., 1984], is closely related to
205 the time-windowed correlation method, since the two represent equivalent processes
206 implemented in either the time or frequency domain.
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208 In the following sections, case studies of CWI are given that use ambient noise and
209 repeating earthquakes at two volcanoes in the Aleutian Islands of Alaska: Okmok and
210 Pavlof. The locations of these volcanoes within the Aleutian Arc are given in Figure 1A.
211 The seismic stations at Pavlof and Okmok discussed in the following sections are shown
212 in Figure 1B and Figure 2, respectively. These two case studies illustrate the relative
213 advantages and disadvantages of using ambient noise or repeating earthquakes to
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