Inferring Magma Dynamics at Veniaminof Volcano Via Application of Ambient Noise
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Inferring Magma Dynamics at Veniaminof Volcano via Application of Ambient Noise N. Bennington1*, M. Haney2, C. Thurber1, X. Zeng3 1University of Wisconsin-Madison 2 U.S. Geological Survey, Alaska Volcano Observatory 3Institute of Geodesy and Geophysics, Chinese Academy of Sciences Corresponding author: Ninfa Bennington ([email protected]) Key Points: Ambient noise interferometry is a useful method for identifying magmatic activity associated with eruption Seismic data recorded on a single seismic station can be used to determine changes in seismic velocity as a function time and depth Spatiotemporal changes in seismic velocity yield estimates of the timescale and depths at which magmatic fluids migrate through the crust This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1029/2018GL079909 © 2018 American Geophysical Union. All rights reserved. Abstract Ambient noise interferometry has become an increasingly popular tool for monitoring active volcanoes. We apply this method to investigate seven past eruptive periods at Veniaminof volcano, Alaska. Two of the largest eruptions studied show seismic velocity changes associated with pre-, co-, and post-eruptive volcanic processes. We develop and implement new analysis techniques to determine how seismic velocity changes at Veniaminof are distributed with depth. Spatiotemporal examination of these seismic velocity changes reveals evidence for the distribution of magma storage and the timescale at which magmatic fluids intrude into and reside within these storage regions in the months preceding eruption. We conduct the depth analysis using data recorded on a single seismometer. The same analysis could be applied to any volcano monitored by at least one seismometer in order to detect magmatic activity indicative of impending eruption, specifically the intrusion and migration of magmatic fluids into the volcanic system. Plain Language Summary Veniaminof is one of the most active volcanoes in Alaska. Eruptions at the volcano occur frequently and can begin with little to no warning. In this study, we explore new and emerging seismic methods for identifying magmatic activity indicative of eruption. First, we apply the technique of ambient noise interferometry to determine subtle changes in seismic velocity within Veniaminof’s subsurface. Such changes in seismic velocity indicate the movement of magmatic fluids into the volcanic system as early as weeks to months prior to the start of the two largest eruptions examined. In the second part of our study, we develop new techniques to determine changes in seismic velocity as a function of both depth and time. This analysis provides evidence for the timescales and depths at which magmatic fluids migrate through and are stored within the crust preceding eruption at Veniaminof. In order to carry out this spatiotemporal analysis, we utilize seismic data recorded on a single seismometer. Thus, the same analysis could be applied at any volcano monitored by even a single seismometer in an effort to identify the intrusion, migration, and storage of magmatic fluids in the time leading to eruption. 1 Introduction In the time preceding eruption, magmatic fluids migrate through a volcanic system causing dramatic changes in the physical properties of the subsurface. This can result in observables such as increased surface temperature, ground deformation, increased rates of seismicity, and surface degassing. Short term detection of volcanic unrest, therefore, relies on the use of monitoring tools such as seismometers, GPS, gas sampling instruments, and spaceborne remote sensing. Unfortunately, most eruptions occur at volcanoes that are poorly monitored via ground-based instrumentation (National Academies of Sciences, Engineering, and Medicine, 2017). Additionally, satellite data observing thermal anomalies or ground deformation typically have poor temporal resolution making it difficult to monitor a rapidly evolving volcanic system (National Academies of Sciences, Engineering, and Medicine, 2017). For these reasons, it is imperative that novel techniques be developed to identify magmatic activity leading to volcanic eruption at sparsely instrumented active volcanoes. We define “sparsely instrumented” as those volcanoes monitored by a single type and/or a limited number of ground-based instruments. This definition is based on the U.S. Geological Survey open report of Moran et al. (2008) which attributes the limited monitoring capabilities at U.S. volcanos to an insufficient type or density of ground-based instrumentation. © 2018 American Geophysical Union. All rights reserved. Veniaminof is one of the largest and most active volcanoes in the Aleutian arc, with ground-based monitoring limited to a network of short-period seismic instruments (Figure 1). The permanent network has been operational since late 2002 and has observed the eruptive cycles of the seven most recent Veniaminof eruptions. Pesicek et al. (2018) examine the relationship between increased rates of seismicity and volcanic activity at the volcano. They find that only one of the seven most recent eruptions displayed a statistically significant increase in volcano tectonic seismicity preceding onset. Also, only two of the most recent eruptions showed episodes of low-frequency tremor preceding eruption (Neal et al., 2001; Dixon et al., 2015). The presence of a continuous GPS network would also provide critical information for identifying changes in the magma reservoir in the time preceding eruption, however, such a permanent network is absent at Veniaminof. Fournier and Freymueller (2008) occupied campaign GPS sites surrounding the volcano in the summers of 2002 and 2005 and found low levels of uplift (5-10 mm/yr). They modeled their GPS velocity field using several different source models and obtained long-term deformation source depths varying between ~2.7 and 9 km BSL. Similarly, Lu and Dzursin (2014) stacked coherent, summer-to-summer interferograms (1992-2010) in order to determine a long-term deformation rate of 5 mm/yr. Both geodetic studies suggest that the observed uplift rates may be due to the intrusion of magmatic fluids into a deeper magma storage region. Fournier and Freymueller (2008) further suggest that inferred recharge of this deeper magma storage region could occur immediately prior to eruption and fuel shallow volcanism at Veniaminof. Unfortunately, they were unable to resolve a secondary, shallow deformation source due to the wide spacing of their campaign GPS sites and the large time interval sampled. The geodetic studies at Veniaminof demonstrate an incomplete knowledge of the timescale and depths at which magmatic fluids migrate through and are stored in the crust in the time preceding eruption. Compounding the situation is the fact that volcanic activity at Veniaminof can begin without significant precursory seismicity or low-frequency tremor (Neal et al., 2001; Dixon et al., 2015; Pesicek et al., 2018). Since ground-based monitoring efforts at Veniaminof rely on the existing seismic network, we develop and apply new and existing seismic tools in this study to help identify magmatic activity indicative of imminent eruption. First, we utilize the technique of ambient noise interferometry (ANI), which detects subtle changes in seismic velocity associated with changes in the material properties of the subsurface. ANI has become a well-established technique for identifying magmatic activity at active volcanoes (e.g. Brenguier et al., 2008; Duputel et al., 2009; Mordret et al., 2010; Obermann et al., 2013; Bennington et al., 2015; Haney et al., 2015; Budi-Santoso and Lesage, 2016). This method typically uses ambient noise data derived from pairs of seismic stations; however, Haney et al. (2015) and DePlaen et al. (2016) demonstrate that changes in seismic velocity can also be determined using ambient noise data determined from individual stations. Thus, ANI is an excellent tool for volcanoes monitored with a sparse seismic network. With ANI, we examine the seven past eruptive periods at Veniaminof. Two of the eruptive sequences we analyze yield evidence for the intrusion of magmatic fluids into the crustal magma storage system on a scale of weeks to months preceding the onset of eruption. We follow-up on these results by focusing on the development and application of new analysis techniques that constrain changes in seismic velocity in both time and space. This spatiotemporal analysis of seismic velocity changes is carried out using data recorded on a single seismometer at Veniaminof and reveals the distribution of magma storage and timescale at which magmatic fluids reside within each level of storage in the lead-up to eruption. Similar to the single seismic station ANI approach presented by Haney et al. (2015) and DePlaen et al. (2016), the results presented here suggest that the newly developed spatiotemporal analysis may be a valuable method for identifying magmatic activity at volcanoes instrumented with only a single seismometer. © 2018 American Geophysical Union. All rights reserved. 2 Measuring Changes in Seismic Velocity via Ambient Noise Interferometry Temporal changes in seismic velocity are determined by measuring the apparent time shift between a reference and “current” noise correlation function (NCF) – a process referred