Geology topics

Matthew M. Haney

Publications and source records attributed to Matthew M. Haney.

At least 73 records · Page 4Linked to original sources

USGS2018

Volcanic thunder from explosive eruptions at Bogoslof volcano, Alaska

Lightning often occurs during ash-producing eruptive activity, and its detection is now being used in volcano monitoring for rapid alerts. We report on infrasonic and sonic recordings of the related, but previously undocumented, phenomenon of volcanic thunder. We observe volcanic thunder during the waning stages of two explosive eruptions at Bogoslof volcano, Alaska, on a microphone array located 60 km away. Thunder signals arrive from a different direction than coeruptive infrasound generated at the vent following an eruption on 10 June 2017, consistent with locations from lightning networks. For the 8 March 2017 eruption, arrival times and amplitudes of high-frequency thunder signals correlate well with the timing and strength of lightning detections. In both cases, the thunder is associated with lightning that continues after significant eruptive activity has ended. Infrasonic and sonic observations of volcanic thunder offer a new avenue for studying electrification processes in volcanic plumes.

Alaska↗

USGS2017

The 2014 eruptions of Pavlof Volcano, Alaska

Pavlof Volcano is one of the most frequently active volcanoes in the Aleutian Island arc, having erupted more than 40 times since observations were first recorded in the early 1800s . The volcano is located on the Alaska Peninsula (lat 55.4173° N, long 161.8937° W), near Izembek National Wildlife Refuge. The towns and villages closest to the volcano are Cold Bay, Nelson Lagoon, Sand Point, and King Cove, which are all within 90 kilometers (km) of the volcano (fig. 1). Pavlof is a symmetrically shaped stratocone that is 2,518 meters (m) high, and has about 2,300 m of relief. The volcano supports a cover of glacial ice and perennial snow roughly 2 to 4 cubic kilometers (km 3 ) in volume, which is mantled by variable amounts of tephra fall, rockfall debris, and pyroclastic-flow deposits produced during historical eruptions. Typical Pavlof eruptions are characterized by moderate amounts of ash emission, lava fountaining, spatter-fed lava flows, explosions, and the accumulation of unstable mounds of spatter on the upper flanks of the volcano. The accumulation and subsequent collapse of spatter piles on the upper flanks of the volcano creates hot granular avalanches, which erode and melt snow and ice, and thereby generate watery debris-flow and hyperconcentrated-flow lahars. Seismic instruments were first installed on Pavlof Volcano in the early 1970s, and since then eruptive episodes have been better characterized and specific processes have been documented with greater certainty. The application of remote sensing techniques, including the use of infrasound data, has also aided the study of more recent eruptions. Although Pavlof Volcano is located in a remote part of Alaska, it is visible from Cold Bay, Sand Point, and Nelson Lagoon, making distal observations of eruptive activity possible, weather permitting. A busy air-travel corridor that is utilized by a numerous transcontinental and regional air carriers passes near Pavlof Volcano. The frequency of air travel across the region results in a relatively large number of airborne observations of eruptive activity. During the 2014 Pavlof eruptions, the Alaska Volcano Observatory received observations and photographs from pilots and local observers, which aided evaluation of the eruptive activity and the areas affected by eruptive products. This report outlines the chronology of events associated with the 2014 eruptive activity at Pavlof Volcano, provides documentation of the style and character of the eruptive episodes, and reports briefly on the eruptive products and impacts. The principal observations are described and portrayed on maps and photographs, and the 2014 eruptive activity is compared to historical eruptions.

Alaska↗

USGS2017

Reducing risk where tectonic plates collide—U.S. Geological Survey subduction zone science plan

The U.S. Geological Survey (USGS) serves the Nation by providing reliable scientific information and tools to build resilience in communities exposed to subduction zone earthquakes, tsunamis, landslides, and volcanic eruptions. Improving the application of USGS science to successfully reduce risk from these events relies on whole community efforts, with continuing partnerships among scientists and stakeholders, including researchers from universities, other government labs and private industry, land-use planners, engineers, policy-makers, emergency managers and responders, business owners, insurance providers, the media, and the general public. Motivated by recent technological advances and increased awareness of our growing vulnerability to subduction-zone hazards, the USGS is uniquely positioned to take a major step forward in the science it conducts and products it provides, building on its tradition of using long-term monitoring and research to develop effective products for hazard mitigation. This science plan provides a blueprint both for prioritizing USGS science activities and for delineating USGS interests and potential participation in subduction zone science supported by its partners. The activities in this plan address many USGS stakeholder needs: High-fidelity tools and user-tailored information that facilitate increasingly more targeted, neighborhood-scale decisions to mitigate risks more cost-effectively and ensure post-event operability. Such tools may include maps, tables, and simulated earthquake ground-motion records conveying shaking intensity and frequency. These facilitate the prioritization of retrofitting of vulnerable infrastructure; Information to guide local land-use and response planning to minimize development in likely hazardous zones (for example, databases, maps, and scenario documents to guide evacuation route planning in communities near volcanoes, along coastlines vulnerable to tsunamis, and built on landslide-prone terrain); New tools to assess the potential for cascading hazards, such as landslides, tsunamis, coastal changes, and flooding caused by earthquakes or volcanic eruptions; Geospatial models of permanent, widespread land- and sea-level changes that may occur in the immediate aftermath of great ( M ≥8.0) subduction zone earthquakes; Strong partnerships between scientists and public safety providers for effective decision making during periods of elevated hazard and risk; Accurate forecasts of far-reaching hazards (for example, ash clouds, tsunamis) to avert catastrophes and unnecessary disruptions in air and sea transportation; Aftershock forecasts to guide decisions about when and where to re-enter, repair, or rebuild buildings and infrastructure, for all types of subduction zone earthquakes.

Circular↗

USGS2017

Volcanic tremor and plume height hysteresis from Pavlof Volcano, Alaska

The March 2016 eruption of Pavlof Volcano, Alaska produced ash plumes that canceled over 100 flights in North America. The eruption produced strong tremor recorded by seismic and remote low-frequency acoustic (infrasound) stations, including the EarthScope Transportable Array. The relationship between the tremor amplitudes and plume height changes considerably between the waxing and waning portions of the eruption. Similar hysteresis has been observed between seismic river noise and discharge during storms, suggesting flow and erosional processes in both rivers and volcanoes can produce irreversible structural changes detectable in geophysical data. We propose that the time-varying relationship at Pavlof arose from changes in the tremor source related to volcanic vent erosion. This relationship may improve estimates of volcanic emissions and characterization of eruption size and intensity.

Alaska↗

USGS2017

Perturbational and nonperturbational inversion of Rayleigh-wave velocities

The inversion of Rayleigh-wave dispersion curves is a classic geophysical inverse problem. We have developed a set of MATLAB codes that performs forward modeling and inversion of Rayleigh-wave phase or group velocity measurements. We describe two different methods of inversion: a perturbational method based on finite elements and a nonperturbational method based on the recently developed Dix-type relation for Rayleigh waves. In practice, the nonperturbational method can be used to provide a good starting model that can be iteratively improved with the perturbational method. Although the perturbational method is well-known, we solve the forward problem using an eigenvalue/eigenvector solver instead of the conventional approach of root finding. Features of the codes include the ability to handle any mix of phase or group velocity measurements, combinations of modes of any order, the presence of a surface water layer, computation of partial derivatives due to changes in material properties and layer boundaries, and the implementation of an automatic grid of layers that is optimally suited for the depth sensitivity of Rayleigh waves.

Geophysics↗

USGS2016

Volcano deformation source parameters estimated from InSAR: Sensitivities to uncertainties in seismic tomography

The eruption cycle of a volcano is controlled in part by the upward migration of magma. The characteristics of the magma flux produce a deformation signature at the Earth's surface. Inverse analyses use geodetic data to estimate strategic controlling parameters that describe the position and pressurization of a magma chamber at depth. The specific distribution of material properties controls how observed surface deformation translates to source parameter estimates. Seismic tomography models describe the spatial distributions of material properties that are necessary for accurate models of volcano deformation. This study investigates how uncertainties in seismic tomography models propagate into variations in the estimates of volcano deformation source parameters inverted from geodetic data. We conduct finite element model-based nonlinear inverse analyses of interferometric synthetic aperture radar (InSAR) data for Okmok volcano, Alaska, as an example. We then analyze the estimated parameters and their uncertainties to characterize the magma chamber. Analyses are performed separately for models simulating a pressurized chamber embedded in a homogeneous domain as well as for a domain having a heterogeneous distribution of material properties according to seismic tomography. The estimated depth of the source is sensitive to the distribution of material properties. The estimated depths for the homogeneous and heterogeneous domains are 2666 ± 42 and 3527 ± 56 m below mean sea level, respectively (99% confidence). A Monte Carlo analysis indicates that uncertainties of the seismic tomography cannot account for this discrepancy at the 99% confidence level. Accounting for the spatial distribution of elastic properties according to seismic tomography significantly improves the fit of the deformation model predictions and significantly influences estimates for parameters that describe the location of a pressurized magma chamber.

Journal of Geophysical Research B: Solid Earth↗

USGS2016

Seismic envelope-based detection and location of ground-coupled airwaves from volcanoes in Alaska

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

Alaska↗

USGS2016

Erratum to Surface‐wave green’s tensors in the near field

Haney and Nakahara (2014) derived expressions for surface‐wave Green’s tensors that included near‐field behavior. Building on the result for a force source, Haney and Nakahara (2014) further derived expressions for a general point moment tensor source using the exact Green’s tensors. However, it has come to our attention that, although the Green’s tensors were correct, the resulting expressions for a general point moment tensor source were missing some terms. In this erratum, we provide updated expressions with these missing terms. The inclusion of the missing terms changes the example given in Haney and Nakahara (2014).

Bulletin of the Seismological Society of America↗

USGS2016

Long period seismicity and very long period infrasound driven by shallow magmatic degassing at Mount Pagan, Mariana Islands

Long period (LP) seismicity and very long period infrasound (iVLP) were recorded during continuous degassing from Mount Pagan, Mariana Islands, in July 2013 to January 2014. The frequency content of the LP and iVLP events and delay times between the two arrivals were remarkably stable and indicate nearly co-located sources. Using phase-weighted stacking over similar events to dampen noise, we find that the LP source centroid is located 60 m below and 180 m west of the summit vent. The moment tensor reveals a volumetric source modeled as resonance of a subhorizontal sill intersecting a dike. We model the seismoacoustic wavefields with a coupled earth-air 3-D finite difference code. The ratios of pressure to velocity measured at the infrasound arrays are an order of magnitude larger than the synthetic ratios, so the iVLP is not the result of LP energy transmitting into the atmosphere at its epicenter. Based on crater shape and dimensions determined by structure from motion, we model the iVLP as acoustic resonance of an exponential horn. The source of the continuous plume from gas analysis is shallow magmatic degassing, which repeatedly pressurized the dike-sill portion of the conduit over the 7 months of observation. Periodic gas release caused the geologically controlled sill to partially collapse and resonate, while venting of gas at the surface triggered resonance in the crater. LP degassing only accounts for ~12% of total degassing, indicating that most degassing is relatively aseismic and that multiple active pathways exist beneath the vent.

Mount Pagan↗

USGS2015

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

Long-period (LP, 0.5-5 Hz) seismicity, observed at volcanoes worldwide, is a recognized signature of unrest and eruption. Cyclic LP “drumbeating” was the characteristic seismicity accompanying the sustained dome-building phase of the 2004–2008 eruption of Mount St. Helens (MSH), WA. However, together with the LP drumbeating was a near-continuous, randomly occurring series of tiny LP seismic events (LP “subevents”), which may hold important additional information on the mechanism of seismogenesis at restless volcanoes. We employ template matching, phase-weighted stacking, and full-waveform inversion to image the source mechanism of one multiplet of these LP subevents at MSH in July 2005. The signal-to-noise ratios of the individual events are too low to produce reliable waveform-inversion results, but the events are repetitive and can be stacked. We apply network-based template matching to 8 days of continuous velocity waveform data from 29 June to 7 July 2005 using a master event to detect 822 network triggers. We stack waveforms for 359 high-quality triggers at each station and component, using a combination of linear and phase-weighted stacking to produce clean stacks for use in waveform inversion. The derived source mechanism pointsto the volumetric oscillation (~10 m 3 ) of a subhorizontal crack located at shallow depth (~30 m) in an area to the south of Crater Glacier in the southern portion of the breached MSH crater. A possible excitation mechanism is the sudden condensation of metastable steam from a shallow pressurized hydrothermal system as it encounters cool meteoric water in the outer parts of the edifice, perhaps supplied from snow melt.

Washington↗

USGS2015

Monitoring changes in seismic velocity related to an ongoing rapid inflation event at Okmok volcano, Alaska

Okmok is one of the most active volcanoes in the Aleutian Arc. In an effort to improve our ability to detect precursory activity leading to eruption at Okmok, we monitor a recent, and possibly ongoing, GPS-inferred rapid inflation event at the volcano using ambient noise interferometry (ANI). Applying this method, we identify changes in seismic velocity outside of Okmok’s caldera, which are related to the hydrologic cycle. Within the caldera, we observe decreases in seismic velocity that are associated with the GPS-inferred rapid inflation event. We also determine temporal changes in waveform decorrelation and show a continual increase in decorrelation rate over the time associated with the rapid inflation event. Themagnitude of relative velocity decreases and decorrelation rate increases are comparable to previous studies at Piton de la Fournaise that associate such changes with increased production of volatiles and/ormagmatic intrusion within the magma reservoir and associated opening of fractures and/or fissures. Notably, the largest decrease in relative velocity occurs along the intrastation path passing nearest to the center of the caldera. This observation, along with equal amplitude relative velocity decreases revealed via analysis of intracaldera autocorrelations, suggests that the inflation sourcemay be located approximately within the center of the caldera and represent recharge of shallow magma storage in this location. Importantly, there is a relative absence of seismicity associated with this and previous rapid inflation events at Okmok. Thus, these ANI results are the first seismic evidence of such rapid inflation at the volcano.

Alaska↗

USGS2015

A comparison of methods to estimate seismic phase delays--Numerical examples for coda wave interferometry

Time-shift estimation between arrivals in two seismic traces before and after a velocity perturbation is a crucial step in many seismic methods. The accuracy of the estimated velocity perturbation location and amplitude depend on this time shift. Windowed cross correlation and trace stretching are two techniques commonly used to estimate local time shifts in seismic signals. In the work presented here, we implement Dynamic Time Warping (DTW) to estimate the warping function – a vector of local time shifts that globally minimizes the misfit between two seismic traces. We illustrate the differences of all three methods compared to one another using acoustic numerical experiments. We show that DTW is comparable to or better than the other two methods when the velocity perturbation is homogeneous and the signal-to-noise ratio is high. When the signal-to-noise ratio is low, we find that DTW and windowed cross correlation are more accurate than the stretching method. Finally, we show that the DTW algorithm has better time resolution when identifying small differences in the seismic traces for a model with an isolated velocity perturbation. These results impact current methods that utilize not only time shifts between (multiply) scattered waves, but also amplitude and decoherence measurements. DTW is a new tool that may find new applications in seismology and other geophysical methods (e.g., as a waveform inversion misfit function).

Geophysical Journal International↗

USGS2015

Point spread functions for earthquake source imaging: An interpretation based on seismic interferometry

Recently, various methods have been proposed and applied for earthquake source imaging, and theoretical relationships among the methods have been studied. In this study, we make a follow-up theoretical study to better understand the meanings of earthquake source imaging. For imaging problems, the point spread function (PSF) is used to describe the degree of blurring and degradation in an obtained image of a target object as a response of an imaging system. In this study, we formulate PSFs for earthquake source imaging. By calculating the PSFs, we find that waveform source inversion methods remove the effect of the PSF and are free from artifacts. However, the other source imaging methods are affected by the PSF and suffer from the effect of blurring and degradation due to the restricted distribution of receivers. Consequently, careful treatment of the effect is necessary when using the source imaging methods other than waveform inversions. Moreover, the PSF for source imaging is found to have a link with seismic interferometry with the help of the source-receiver reciprocity of Green’s functions. In particular, the PSF can be related to Green’s function for cases in which receivers are distributed so as to completely surround the sources. Furthermore, the PSF acts as a low-pass filter. Given these considerations, the PSF is quite useful for understanding the physical meaning of earthquake source imaging.

Geophysical Journal International↗

USGS2015

Non-perturbational surface-wave inversion: A Dix-type relation for surface waves

We extend the approach underlying the well-known Dix equation in reflection seismology to surface waves. Within the context of surface wave inversion, the Dix-type relation we derive for surface waves allows accurate depth profiles of shear-wave velocity to be constructed directly from phase velocity data, in contrast to perturbational methods. The depth profiles can subsequently be used as an initial model for nonlinear inversion. We provide examples of the Dix-type relation for under-parameterized and over-parameterized cases. In the under-parameterized case, we use the theory to estimate crustal thickness, crustal shear-wave velocity, and mantle shear-wave velocity across the Western U.S. from phase velocity maps measured at 8-, 20-, and 40-s periods. By adopting a thin-layer formalism and an over-parameterized model, we show how a regularized inversion based on the Dix-type relation yields smooth depth profiles of shear-wave velocity. In the process, we quantitatively demonstrate the depth sensitivity of surface-wave phase velocity as a function of frequency and the accuracy of the Dix-type relation. We apply the over-parameterized approach to a near-surface data set within the frequency band from 5 to 40 Hz and find overall agreement between the inverted model and the result of full nonlinear inversion.

Geophysics↗

USGS2014

Tracking changes in volcanic systems with seismic Interferometry

The detection and evaluation of time-dependent changes at volcanoes form the foundation upon which successful volcano monitoring is built. Temporal changes at volcanoes occur over all time scales and may be obvious (e.g., earthquake swarms) or subtle (e.g., a slow, steady increase in the level of tremor). Some of the most challenging types of time-dependent change to detect are subtle variations in material properties beneath active volcanoes. Although difﬁcult to measure, such changes carry important information about stresses and ﬂuids present within hydrothermal and magmatic systems. These changes are imprinted on seismic waves that propagate through volcanoes. In recent years, there has been a quantum leap in the ability to detect subtle structural changes systematically at volcanoes with seismic waves. The new methodology is based on the idea that useful seismic signals can be generated “at will” from seismic noise. This means signals can be measured any time, in contrast to the often irregular and unpredictable times of earthquakes. With seismic noise in the frequency band 0.1–1 Hz arising from the interaction of the ocean with the solid Earth known as microseisms, researchers have demonstrated that cross-correlations of passive seismic recordings between pairs of seismometers yield coherent signals (Campillo and Paul 2003; Shapiro and Campillo 2004). Based on this principle, coherent signals have been reconstructed from noise recordings in such diverse ﬁelds as helioseismology (Rickett and Claerbout 2000), ultrasound (Weaver and Lobkis 2001), ocean acoustic waves (Roux and Kuperman 2004), regional (Shapiro et al. 2005; Sabra et al. 2005; Bensen et al. 2007) and exploration (Draganov et al. 2007) seismology, atmospheric infrasound (Haney 2009), and studies of the cryosphere (Marsan et al. 2012). Initial applications of ambient seismic noise were to regional surface wave tomography (Shapiro et al. 2005). Brenguier et al. (2007) were the ﬁrst to use ambient noise tomography (ANT) to map the 3D structure of a volcanic interior (at Piton de la Fournaise). Subsequent studies have imaged volcanoes with ANT at Okmok (Masterlark et al. 2010), Toba (Stankiewicz et al. 2010), Katmai (Thurber et al. 2012), Asama (Nagaoka et al. 2012), Uturuncu (Jay et al. 2012), and Kilauea (Ballmer et al. 2013b). In addition, Ma et al. (2013) have imaged a scatterer in the volcanic region of southern Peru by applying array techniques to ambient noise correlations. Prior to and in tandem with the development of ANT, researchers discovered that repeating earthquakes, which often occur at volcanoes, could be used to monitor subtle time-dependent changes with a technique known as the doublet method or coda wave interferometry (CWI) (Poupinet et al. 1984; Roberts et al. 1992; Ratdomopurbo and Poupinet 1995; Snieder et al. 2002; Pandolﬁ et al. 2006; Wegler et al. 2006; Martini et al. 2009; Haney et al. 2009; De Angelis 2009; Nagaoka et al. 2010; Battaglia et al. 2012; Erdem and Waite 2005; Hotovec-Ellis et al. 2014). Chaput et al. (2012) have also used scattered waves from Strombolian eruption coda at Erebus volcano to image the reﬂectivity of the volcanic interior with body wave interferometry. However, CWI in its original form was limited in that repeating earthquakes, or doublets, were not always guaranteed to occur. With the widespread use of noise correlations in seismology following the groundbreaking work by Campillo and Paul (2003) and Shapiro et al. (2005), it became evident that the nature of the ambient seismic ﬁeld, due to its oceanic origin, enabled the continuous monitoring of subtle, time-dependent changes at both fault zones (Wegler and Sens-Schönfelder 2007; Brenguier et al. 2008b; Wegler et al. 2009; Sawazaki et al. 2009; Tatagi et al. 2012) and volcanoes (Sens-Schönfelder and Wegler 2006; Brenguier et al. 2008a) without the need for repeating earthquakes. Seismic precursors to eruptions based on ambient noise we

Book chapter↗

USGS2014

The 2013 eruption of Pavlof Volcano, Alaska: a spatter eruption at an ice- and snow-clad volcano

The 2013 eruption of Pavlof Volcano, Alaska began on 13 May and ended 49 days later on 1 July. The eruption was characterized by persistent lava fountaining from a vent just north of the summit, intermittent strombolian explosions, and ash, gas, and aerosol plumes that reached as high as 8 km above sea level and on several occasions extended as much as 500 km downwind of the volcano. During the first several days of the eruption, accumulations of spatter near the vent periodically collapsed to form small pyroclastic avalanches that eroded and melted snow and ice to form lahars on the lower north flank of the volcano. Continued lava fountaining led to the production of agglutinate lava flows that extended to the base of the volcano, about 3–4 km beyond the vent. The generation of fountain-fed lava flows was a dominant process during the 2013 eruption; however, episodic collapse of spatter accumulations and formation of hot spatter-rich granular avalanches was a more efficient process for melting snow and ice and initiating lahars. The lahars and ash plumes generated during the eruption did not pose any serious hazards for the area. However, numerous local airline flights were cancelled or rerouted, and trace amounts of ash fall occurred at all of the local communities surrounding the volcano, including Cold Bay, Nelson Lagoon, Sand Point, and King Cove.

Alaska↗

USGS2014

Surface‐wave Green’s tensors in the near field

We demonstrate the connection between theoretical expressions for the correlation of ambient noise Rayleigh and Love waves and the exact surface‐wave Green’s tensors for a point force. The surface‐wave Green’s tensors are well known in the far‐field limit. On the other hand, the imaginary part of the exact Green’s tensors, including near‐field effects, arises in correlation techniques such as the spatial autocorrelation (SPAC) method. Using the imaginary part of the exact Green’s tensors from the SPAC method, we find the associated real part using the Kramers–Kronig relations. The application of the Kramers–Kronig relations is not straightforward, however, because the causality properties of the different tensor components vary. In addition to the Green’s tensors for a point force, we also derive expressions for a general point moment tensor source.

Bulletin of the Seismological Society of America↗

USGS2014

Backprojection of volcanic tremor

Backprojection has become a powerful tool for imaging the rupture process of global earthquakes. We demonstrate the ability of backprojection to illuminate and track volcanic sources as well. We apply the method to the seismic network from Okmok Volcano, Alaska, at the time of an escalation in tremor during the 2008 eruption. Although we are able to focus the wavefield close to the location of the active cone, the network array response lacks sufficient resolution to reveal kilometer-scale changes in tremor location. By deconvolving the response in successive backprojection images, we enhance resolution and find that the tremor source moved toward an intracaldera lake prior to its escalation. The increased tremor therefore resulted from magma-water interaction, in agreement with the overall phreatomagmatic character of the eruption. Imaging of eruption tremor shows that time reversal methods, such as backprojection, can provide new insights into the temporal evolution of volcanic sources.

Alaska↗