Geology topics

Alicia J. Hotovec-Ellis

Publications and source records attributed to Alicia J. Hotovec-Ellis.

At least 19 recordsLinked to original sources

USGS2025

A decade of precursory seismicity at Mauna Loa: 2013–2022

The 2022 eruption of Mauna Loa was preceded by nearly 10 years of seismic unrest. Using the earthquake catalog of the Hawaiian Volcano Observatory, we describe and explore many aspects of unrest between January 1, 2013, and the start of the eruption on November 28, 2022 (UTC). Earthquakes associated with unrest occurred above an inflating dike at shallow depths under and to the south of Moku‘āweoweo and in the cluster of seismicity to the west of Moku‘āweoweo associated with a buried rift zone. Earthquakes also occurred in a broad region at the base of the crust, likely associated with a zone of magma accumulation. Unrest is broken down into four phases of increasing intensity with time and into individual geographic regions. While activity occurring at shallow depths under the caldera often alternated with activity on the west flank in the first two phases of unrest, the third phase of unrest featured increased rates of earthquake activity in both areas acting concurrently. We also document a slow shift in seismicity across multiple years from the south of Moku‘āweoweo to an area under the caldera that likely represents a change in the location of the intrusion through time. Broadly speaking, unrest observed in advance of the 2022 eruption occurred in the same areas as the unrest preceding eruptions in 1975 and 1984. Furthermore, precursory unrest prior to eruptions in 1975, 1984 and 2022 lasted years and culminated in an intense phase of seismic activity lasting on the order of hours that represented the final dike propagation to the surface. Concurrent earthquake activity under Moku‘āweoweo and the west flank of Mauna Loa, paired with a shift in earthquakes under Moku‘āweoweo were the two most notable long-term precursors to an eruption; however, neither provided an actionable timeline of an impending eruption. The decade of precursory seismicity prior to the 2022 eruption is similar to long precursory phases prior to eruptions in 1975 and 1984 and underscores the challenges of informing on eruption hazards in a timely manner. By combining seismic activity with other geophysical fields such as geodesy and gravity, we present a conceptual model to aid in the interpretation of future unrest at Mauna Loa.

Hawaii↗

USGS2025

REDPy: A Python tool for automated repeating earthquake detection and visualization

Detecting and cataloging seismic events are among the most fundamental tasks in seismology. Many standardized tools for these tasks exist, including the open‐source package repeating earthquake detector in Python (REDPy). REDPy generates an organized catalog of seismic events from continuous waveform data, in which events are automatically separated into groups (“families”) by their waveform similarity through cross‐correlation. REDPy also automatically generates various outputs that allow a user to visualize important trends in the catalog, which may be used in real time or in retrospective analyses to allow rapid identification of interesting features. The code was designed for near‐real‐time volcano monitoring but is applicable across a broad range of use cases in seismology and seismoacoustics. In this article, the utility and performance of REDPy are demonstrated on two highly seismogenic volcanic eruption sequences: the onset of the dome‐building eruption of Mount St. Helens, Washington, from 2004 to 2005, and the entirety of the summit caldera collapse sequence of Kīlauea, Hawai‘i, in 2018. This article is meant to be a companion to the documentation of the code; in addition to detailing the basic required inputs, script functionality, and resulting outputs, the reasonings behind several important design decisions are also discussed.

Seismological Research Letters↗

USGS2025

A generalized deep learning model to detect and classify volcano seismicity

Volcano seismicity is often detected and classified based on its spectral properties. However, the wide variety of volcano seismic signals and increasing amounts of data make accurate, consistent, and efficient detection and classification challenging. Machine learning (ML) has proven very effective at detecting and classifying tectonic seismicity, particularly using Convolutional Neural Networks (CNNs) and leveraging labeled datasets from regional seismic networks. Progress has been made applying ML to volcano seismicity, but efforts have typically been focused on a single volcano and are often hampered by the limited availability of training data. We build on the method of Tan et al. [2024] ( 10.1029/2024JB029194 ) to generalize a spectrogram-based CNN termed the VOlcano Infrasound and Seismic Spectrogram Neural Network ( VOISS-Net ) to detect and classify volcano seismicity at any volcano. We use a diverse training dataset of over 270,000 spectrograms from multiple volcanoes: Pavlof, Semisopochnoi, Tanaga, Takawangha, and Redoubt volcanoes\replaced (Alaska, USA); Mt. Etna (Italy); and Kīlauea, Hawai`i (USA). These volcanoes present a wide range of volcano seismic signals, source-receiver distances, and eruption styles. Our generalized VOISS-Net model achieves an accuracy of 87 % on the test set. We apply this model to continuous data from several volcanoes and eruptions included within and outside our training set, and find that multiple types of tremor, explosions, earthquakes, long-period events, and noise are successfully detected and classified. The model occasionally confuses transient signals such as earthquakes and explosions and misclassifies seismicity not included in the training dataset (e.g. teleseismic earthquakes). We envision the generalized VOISS-Net model to be applicable in both research and operational volcano monitoring settings.

Volcanica↗

USGS2025

Identifying precursors and tracking pulses of magma ascent in multidisciplinary data during the 2018–2023 phreatomagmatic eruption at Semisopochnoi Island, Alaska

The 2018–2023 phreatomagmatic eruptions at Semisopochnoi Island, Alaska produced abundant long-period (LP) seismicity, harmonic and broadband tremor, and explosion signals over several well-monitored periods of eruption and quiescence. The corresponding dataset provides an excellent opportunity to investigate precursory and syn -eruptive geophysical signals of long-lived phreatomagmatic eruptions using multiparameter observations. We generated explosion and LP event catalogs through novel implementations of the REDPy ( Hotovec-Ellis, 2024 ) repeating event detector in mid-2021 following a network upgrade and the onset of a new phase of the eruption. The hundreds of detected explosions show a high degree of infrasound waveform similarity over more than a year, indicating a repeating source mechanism likely associated with explosive magma-water interaction. The seismic LP catalog shows that events began over a month prior to renewed explosive activity at the beginning of August 2021, and that lower frequency index (FI) LPs were generated in the week prior to the onset of explosions. We applied a recently developed machine learning tool (VOISS-Net, Tan et al., 2024 ) to catalog abundant broadband and harmonic seismic tremor recorded before and during the renewed explosive activity, along with LPs and explosions. The tremor catalogs complement the LP and explosion catalogs by filling out the seismic sequence with the dominant signal types. Together, these catalogs reveal a seismic sequence of renewed unrest that started with several weeks of LP events, followed by LPs with lower FI values and harmonic tremor in the days prior to explosive activity, and finally the onset of discrete explosions and broadband eruption tremor. We interpret this sequence as the ascent of a new pulse of magma that first interacted with the hydrothermal/groundwater system to produce LPs, followed by harmonic tremor, and that ultimately drove explosive magma-water interactions and periods of continuous ash emissions. The 2021 seismic sequence, in combination with long-term records of satellite SO 2 emissions, deformation from interferometric synthetic aperture radar (InSAR) analysis, ash sample analysis, infrasound, and volcano tectonic seismicity, allows us to interpret the entire 9-year period of unrest and eruption that began with an intrusion and earthquake swarm in 2014.

Alaska↗

USGS2025

Remote single-station seismic monitoring of the July–October 2022 earthquake swarm at Tau volcano, American Samoa

From July to October 2022, a non-eruptive volcanic earthquake swarm occurred within ~15 km of Taʻū Island, located in eastern American Samoa. Felt reports from local residents were the only available information about the swarm when it started, as American Samoa lacked a seismic monitoring network. We developed a consistent single-station catalog for the entire swarm, using seismic data from the nearest station IU.AFI, ~250 km away. We applied the EQTransformer deep-learning model (Mousavi et al., 2020), automatically picking Pn and Sn arrivals on IU.AFI continuous data. We retained only events with Sn-Pn times of 22.5–25 seconds, consistent with the expected locations based on felt reports, then detected smaller swarm events with subsequent template-matching. This single-station catalog characterized the swarm’s onset and escalation to peak activity before a multi-agency field response team installed a local seismic network in mid-August 2022. This permanent seismic network captured the swarm’s decline. EQTransformer identified short S-P times on the first two locally deployed seismometers, both Raspberry Shake sensors, to constrain the swarm’s distance from Taʻū Island. Modern seismological processing methods, combined with basic observations such as felt reports, can quickly contribute useful information during an earthquake response in a poorly monitored region.

American Somoa↗

USGS2024

Seismic velocity changes from repetitive seismicity at Mauna Loa prior to and during its 2022 eruption

Mauna Loa’s short-lived eruption from late November to early December 2022 marked the culmination of nearly a decade of elevated seismic activity and geodetic inflation. The volcano has been monitored by a network of permanent, short period and broadband seismometers. I used the continuous waveform data from that network starting in 2012 to generate a catalog of seismicity that enhances the US Geological Survey Hawaiian Volcano Observatory’s public seismic catalog with four times the number of earthquakes, which were then grouped by waveform similarity. Analysis of subtle delays in the timing of arrivals of scattered waves between pairs of earthquakes in this catalog yields a history of small changes in the shallow seismic velocity structure of the volcano. Seismic velocities have been shown at other volcanoes to change during unrest and eruption. My results show a decrease in seismic velocity centered on the summit beginning in September 2022, corresponding to the onset of a vigorous precursory swarm of seismic activity and shallow inflation. During the eruption itself, I observe large changes due likely to dike opening along the northeast rift zone and deflation of the summit reservoir. However, seismic velocity changes associated with non-volcanic sources such as ground shaking from large earthquakes and meteorological influences at seasonal and diurnal time scales are also observed, and these dominate the velocity changes prior to the eruption. Proper accounting of these effects will be a requirement for use in real-time monitoring, and this work serves as a starting point in that endeavor for Mauna Loa.

Hawaii↗

USGS2024

Triggering the 2022 eruption of Mauna Loa

Distinguishing periods of intermittent unrest from the run-up to eruption is a major challenge at volcanoes around the globe. Comparing multidisciplinary monitoring data with mineral chemistry that records the physical and spatio-temporal evolution of magmas fundamentally advances our ability to forecast eruptions. The recent eruption of Mauna Loa, Earth’s largest active volcano, provides a unique opportunity to differentiate unrest from run-up and improve forecasting of future eruptions. After decades of intermittent seismic and geodetic activity over 38 years of repose, Mauna Loa began erupting on 27 November 2022. Here we present a multidisciplinary synthesis that tracks the spatio-temporal evolution of precursory activity by integrating mineral and melt chemistry, fluid inclusion barometry, numerical modeling of mineral zoning, syn-eruptive gas plume measurements, the distribution and frequency of earthquake hypocenters, seismic velocity changes, and ground deformation. These diverse data indicate that the eruption occurred following a 2-month period of sustained magma intrusion from depths of 3–5 km up to 1–2 km beneath the summit caldera, providing a new model of the plumbing system at this very high threat volcano. Careful correlation of both the geochemistry and instrumental monitoring data improves our ability to distinguish unrest from the run-up to eruption by providing deeper understanding of the both the monitoring data and the magmatic system—an approach that could be applied at other volcanic systems worldwide.

Hawaii↗

USGS2024

Preface to the focus section on volcano monitoring in the Americas

From the Andes to the Aleutian Islands, the Americas are rich with volcanism that spans a diverse range of tectonic settings, eruptive styles, levels of activity, and hazards. Over the past 120 yr, the Americas have witnessed catastrophic volcanic eruptions that have significantly impacted nearby populations. Notable events include the 8 May 1902, pyroclastic density current from Mount Pelée in Martinique, which resulted in the loss of over 28,000 lives in Saint‐Pierre, with only one or two survivors (Lacroix, 1904), and the 1985 eruption of Nevado del Ruiz in Colombia, which triggered a lahar and left an estimated...

Seismological Research Letters↗

USGS2023

Volcanic earthquake catalog enhancement using integrated detection, matched-filtering, and relocation tools

Volcanic earthquake catalogs are an essential data product used to interpret subsurface volcanic activity and forecast eruptions. Advances in detection techniques (e.g., matched-filtering, machine learning) and relative relocation tools have improved catalog completeness and refined event locations. However, most volcano observatories have yet to incorporate these techniques into their catalog-building workflows. This is due in part to complexities in operationalizing, automating, and calibrating these techniques in a satisfactory way for disparate volcano networks and their varied seismicity. In an effort to streamline the integration of catalog-enhancing tools at the Alaska Volcano Observatory (AVO), we have integrated four popular open-source tools: REDPy, EQcorrscan, HypoDD, and GrowClust. The combination of these tools offers the capability of adding seismic event detections and relocating events in a single workflow. The workflow relies on a combination of standard triggering and cross-correlation clustering (REDPy) to consolidate representative templates used in matched-filtering (EQcorrscan). The templates and their detections are then relocated using the differential time methods provided by HypoDD and/or GrowClust. Our workflow also provides codes to incorporate campaign data at appropriate junctures, and calculate magnitude and frequency index for valid events. We apply this workflow to three datasets: the 2012–2013 seismic swarm sequence at Mammoth Mountain (California), the 2009 eruption of Redoubt Volcano (Alaska), and the 2006 eruption of Augustine Volcano (Alaska); and compare our results with previous studies at each volcano. In general, our workflow provides a significant increase in the number of events and improved locations, and we relate the event clusters and temporal progressions to relevant volcanic activity. We also discuss workflow implementation best practices, particularly in applying these tools to sparse volcano seismic networks. We envision that our workflow and the datasets presented here will be useful for detailed volcano analyses in monitoring and research efforts.

Alaska↗

USGS2023

Ring fault creep drives volcano-tectonic seismicity during caldera collapse of Kīlauea in 2018

Basaltic caldera collapses are episodic, producing very-long-period (VLP) earthquakes up to M w 5.4, with prolific inter-collapse (between collapses) volcano-tectonic (VT) seismicity. During the 2018 caldera collapse of Kīlauea Volcano, VT seismicity ceased following each collapse, and then accelerated to a quasi-steady rate prior to the next collapse, marking a temporal pattern distinct from typical foreshock/aftershock sequences. There is currently no consensus on the mechanism(s) that generates the VT seismicity. Here we demonstrate that inter-collapse ring fault creep, induced by chamber depressurization, was the main driver of VT seismicity at Kīlauea in 2018. This is evidenced by: 1) the correlation between cumulative number of VT events and GNSS-derived ring fault creep; 2) agreement between repeating earthquake and GNSS derived creep rates; and 3) consistency between the time dependence of mechanically modeled, creep-driven seismicity and observations. We further show that, ring fault creep can be explained by velocity strengthening friction alone or in conjunction with viscous shear zone rheology. The simultaneous occurrence of creep and seismicity highlights the spatially heterogeneous velocity weakening/strengthening friction on the ring fault. If the VT seismicity-creep correlation can be replicated at other basaltic volcanoes, it would demonstrate that VT seismicity can be used as a proxy for ring fault creep in the absence of GNSS measurements on subsiding caldera block(s).

Hawaii↗

USGS2022

Earthquake-derived seismic velocity changes during the 2018 caldera collapse of Kīlauea volcano

The 2018 Kīlauea caldera collapse produced extraordinary sequences of seismicity and deformation, with 62 episodic collapse events which significantly altered the landscape of the summit region. Despite decades of focused scientific studies at Kīlauea, detailed information about the internal structure of the volcano is limited. Recently developed techniques in seismic interferometry can be used to monitor the internal structure of an active volcano more directly by detecting subtle spatiotemporal changes in seismic wave velocity, but their utility relies on accurate interpretations of the underlying phenomena causing those velocity changes. Here, we retrospectively apply repeating-earthquake-based seismic interferometry to the 2018 Kīlauea eruption sequence. We find that seismic velocities changed over two distinct time scales: a sudden increase followed by a slower decrease in velocity in the hours following each collapse event, and a gradual, long-term decrease in velocity over several weeks that ceased approximately 1 month prior to the end of the eruption. Modeling suggests that short-term changes can be explained by magma reservoir pressurization which specifically closed vertical ring fractures. Long-term changes are related to subsidence of the caldera and likely include the influence of inelastic strain from the formation of new fractures. These observations provide new insights into the evolution of Kīlauea during its progressive collapse and will inform future interpretations for near-real-time monitoring at hazardous volcanoes around the world using similar techniques, especially where a dominant fracture orientation is present.

Hawaii↗

USGS2022

Trends in volcano seismology: 2010 to 2020 and beyond

Volcano seismology has been fundamental to our current understanding of crustal magma migration and eruption. The increasing availability of portable seismic networks with the creative use of seismic sources and ambient noise has led to a better understanding of the volcanic structure of many volcanoes and is producing increasingly detailed images of the volcanic subsurface. The past decade (2010-2020) has seen advances in our understanding of seismic sources under and surrounding volcanoes through precise locations, and through analysis of source mechanisms from seismic signals that are more varied and smaller in magnitude, reaching beyond traditional techniques. In tandem with continued research on fundamental physics-based understanding of volcano-seismic sources, new advances in computational analyses including machine learning methods will push our understanding of volcanic processes into the future. Incorporation of multidisciplinary geophysical observations (especially infrasound) has become commonplace, and our understanding of infrasound propagation and sources will feed back into our ability to monitor ongoing eruptions and surficial mass movements. Open-source codes will permit widespread evaluation and adoption of new methodologies for volcano-seismic analysis and inversion. Combined with quantitative and conceptual source models using improved structural constraints, these new methodologies will better characterize the range of volcano-seismic signal evolution scenarios and hold promise for creating better short-term forecasts. Finally, permanent instrumentation is available on an expanding range of volcanoes, and open data policies are increasingly making these data available to the scientific community in near real time.

Bulletin of Volcanology↗

USGS2020

Seismic and geodetic progression of the 2018 summit caldera collapse of Kīlauea Volcano

The 2018 eruption of Kīlauea volcano, Hawaiʻi, resulted in a major collapse of the summit caldera along with an effusive eruption in the lower East Rift Zone. The caldera collapse comprised 62 highly similar collapse cycles of strong ground deformation and earthquake swarms that ended with a magnitude 5 collapse event and one partial cycle that did not end with a collapse event. We analyzed geodetic and seismic data to better understand how the caldera collapse progressed over 3 months of activity, focusing on the cyclical activity. We identified 3 main phases of collapse: initial ring-fault activation and small explosions (Phase 1), an eastward shift in activity and freeing of the central piston (Phase 2), and a recoupling of the piston to the reservoir followed by relatively steady behavior until the eruption’s end (Phase 3). Additionally, we observed geodetic evidence of tangential motion from the localization of the main ring fault (Phase 2) and the formation of a major peripheral ring fault on the eastern side of the collapse caldera during Phase 3. Both geodetic and seismic parameters suggest that the collapse may have had an eastward-component of motion after the ring fault system had formed. The cyclical seismic and geodetic parameters show no obvious signs that the collapse was coming to an end, with the only notable change being a significant increase in the ratio of cyclical displacement to co-collapse displacement observed during the last complete cycle on GNSS stations outside the caldera region.

Hawaii↗

USGS2018

Deep fluid pathways beneath Mammoth Mountain, California, illuminated by migrating earthquake swarms

Although most volcanic seismicity is shallow (within several kilometers of the surface), some volcanoes exhibit deeper seismicity (10 to 30+ km) that may reflect active processes such as magma resupply and volatile transfer. One such volcano is Mammoth Mountain, California, which has also recently exhibited high rates of CO 2 discharge at the surface. We perform high-resolution earthquake detection and relocation to reveal punctuated episodes of rapidly propagating seismicity at mid-crustal depths along a narrow fracture zone surrounding a body of partial melt. We infer that these earthquakes track dike intrusions or fluid pressure pulses associated with CO 2 exsolution, suggesting that the deep plumbing system of Mammoth Mountain is an active conduit for fluid transport from the base of the crust to the surface.

California↗

USGS2016

Feasibility study of earthquake early warning (EEW) in Hawaii

The effects of earthquake shaking on the population and infrastructure across the State of Hawaii could be catastrophic, and the high seismic hazard in the region emphasizes the likelihood of such an event. Earthquake early warning (EEW) has the potential to give several seconds of warning before strong shaking starts, and thus reduce loss of life and damage to property. The two approaches to EEW are (1) a network approach (such as ShakeAlert or ElarmS) where the regional seismic network is used to detect the earthquake and distribute the alarm and (2) a local approach where a critical facility has a single seismometer (or small array) and a warning system on the premises. The network approach, also referred to here as ShakeAlert or ElarmS, uses the closest stations within a regional seismic network to detect and characterize an earthquake. Most parameters used for a network approach require observations on multiple stations (typically 3 or 4), which slows down the alarm time slightly, but the alarms are generally more reliable than with single-station EEW approaches. The network approach also benefits from having stations closer to the source of any potentially damaging earthquake, so that alarms can be sent ahead to anyone who subscribes to receive the notification. Thus, a fully implemented ShakeAlert system can provide seconds of warning for both critical facilities and general populations ahead of damaging earthquake shaking. The cost to implement and maintain a fully operational ShakeAlert system is high compared to a local approach or single-station solution, but the benefits of a ShakeAlert system would be felt statewide—the warning times for strong shaking are potentially longer for most sources at most locations. The local approach, referred to herein as “single station,” uses measurements from a single seismometer to assess whether strong earthquake shaking can be expected. Because of the reliance on a single station, false alarms are more common than when using a regional network of seismometers. Given the current network, a single-station approach provides more warning for damaging earthquakes that occur close to the station, but it would have limited benefit compared to a fully implemented ShakeAlert system. For Honolulu, for example, the single-station approach provides an advantage over ShakeAlert only for earthquakes that occur in a narrow zone extending northeast and southwest of O‘ahu. Instrumentation and alarms associated with the single-station approach are typically maintained and assessed within the target facility, and thus no outside connectivity is required. A single-station approach, then, is unlikely to help broader populations beyond the individuals at the target facility, but they have the benefit of being commercially available for relatively little cost. The USGS Hawaiian Volcano Observatory (HVO) is the Advanced National Seismic System (ANSS) regional seismic network responsible for locating and characterizing earthquakes across the State of Hawaii. During 2014 and 2015, HVO tested a network-based EEW algorithm within the current seismic network in order to assess the suitability for building a full EEW system. Using the current seismic instrumentation and processing setup at HVO, it is possible for a network approach to release an alarm a little more than 3 seconds after the earthquake is recorded on the fourth seismometer. Presently, earthquakes having M≥3 detected with the ElarmS algorithm have an average location error of approximately 4.5 km and an average magnitude error of -0.3 compared to the reviewed catalog locations from the HVO. Additional stations and upgrades to existing seismic stations would serve to improve solution precision and warning times and additional staffing would be required to provide support for a robust, network-based EEW system. For a critical facility on the Island of Hawaiʻi, such as the telescopes atop Mauna Kea, one phased approach to mitigate losses could be to immediately install a single station system to establish some level of warning. Subsequently, supporting the implementation of a full network-based EEW system on the Island of Hawaiʻi would provide additional benefit in the form of improved warning times once the system is fully installed and operational, which may take several years. Distributed populations across the Hawaiian Islands, including those outside the major cities and far from the likely earthquake source areas, would likely only benefit from a network approach such as ShakeAlert to provide warnings of strong shaking.

Hawaii↗

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

A continuous record of intereruption velocity change at Mount St. Helens from coda wave interferometry

In September 2004, Mount St. Helens volcano erupted after nearly 18 years of quiescence. However, it is unclear from the limited geophysical observations when or if the magma chamber replenished following the 1980–1986 eruptions in the years before the 2004–2008 extrusive eruption. We use coda wave interferometry with repeating earthquakes to measure small changes in the velocity structure of Mount St. Helens volcano that might indicate magmatic intrusion. By combining observations of relative velocity changes from many closely located earthquake sources, we solve for a continuous function of velocity changes with time. We find that seasonal effects dominate the relative velocity changes. Seismicity rates and repeating earthquake occurrence also vary seasonally; therefore, velocity changes and seismicity are likely modulated by snow loading, fluid saturation, and/or changes in groundwater level. We estimate hydrologic effects impart stress changes on the order of tens of kilopascals within the upper 4 km, resulting in annual velocity variations of 0.5 to 1%. The largest nonseasonal change is a decrease in velocity at the time of the deep M w  = 6.8 Nisqually earthquake. We find no systematic velocity changes during the most likely times of intrusions, consistent with a lack of observable surface deformation. We conclude that if replenishing intrusions occurred, they did not alter seismic velocities where this technique is sensitive due to either their small size or the finite compressibility of the magma chamber. We interpret the observed velocity changes and shallow seasonal seismicity as a response to small stress changes in a shallow, pressurized system.

Journal of Geophysical Research B: Solid Earth↗

USGS2014

Deep long-period earthquakes west of the volcanic arc in Oregon: evidence of serpentine dehydration in the fore-arc mantle wedge

Here we report on deep long-period earthquakes (DLPs) newly observed in four places in western Oregon. The DLPs are noteworthy for their location within the subduction fore arc: 40–80 km west of the volcanic arc, well above the slab, and near the Moho. These “offset DLPs” occur near the top of the inferred stagnant mantle wedge, which is likely to be serpentinized and cold. The lack of fore-arc DLPs elsewhere along the arc suggests that localized heating may be dehydrating the serpentinized mantle wedge at these latitudes and causing DLPs by dehydration embrittlement. Higher heat flow in this region could be introduced by anomalously hot mantle, associated with the western migration of volcanism across the High Lava Plains of eastern Oregon, entrained in the corner flow proximal to the mantle wedge. Alternatively, fluids rising from the subducting slab through the mantle wedge may be the source of offset DLPs. As far as we know, these are among the first DLPs to be observed in the fore arc of a subduction-zone system.

Oregon↗