Geology Reports⌕ Search

Geology topics

John J. Lyons

Publications and source records attributed to John J. Lyons.

At least 19 recordsLinked to original sources

Erupted volume during the 2014–2023 Semisopochnoi unrest and eruption constrained by multi-platform InSAR and volcanic gas observations

Volcano deformation data provide important constraints on magma dynamics during volcanic unrest and eruptions. From these observations, we can estimate subsurface pressure changes and relate them to volume change in the reservoir. However, because magma and rock are compressible, this volume change cannot be directly equated with eruptive volume. When we combine geodetic analyses with gas observations, petrologic constraints, volatile solubility modeling, and magma compressibility equations, however, we can estimate realistic lower and upper bounds of eruptive volume. Leveraging multi‐platform InSAR time series and satellite SO 2 data, we apply these methods to the 2014–2023 unrest and eruption of Semisopochnoi, Alaska. We find that this phreatomagmatic eruption sequence was fed by a spherical magma source at 6.5–7.5 km depth bsl, centered beneath the caldera, ∼5 km offset to the east from the vent. Volatile solubility modeling indicates that ∼50% of the initial dissolved CO 2 has exsolved at depths consistent with the geodetic source depth, increasing the magma compressibility. Combining geodetic model constraints with SO 2 observations, petrologic constraints, solubility models, and magma compressibility findings yields 12–32 Mm 3 as the most‐likely juvenile, dense‐rock equivalent eruptive volume, with a best estimate of 20 Mm 3 . Stress change modeling suggests that the local stress field encouraged conduit formation west of the pre‐eruptive inflation center, and that the 2014 M7.9 Rat Island earthquake, ∼65 km to the west of Semisopochnoi, reduced the compressive stress underneath the volcano, supporting prolonged eruptive activity.

Alaska↗

A 481 m-high landslide-tsunami in a cruise ship-frequented Alaska fjord

Early in the morning of 10 August 2025, a >64 × 10 6 –cubic meter landslide struck Tracy Arm fjord in Alaska. The landslide was preconditioned by glacial retreat caused by climate change. The resulting 481-meter runup megatsunami followed an initial 100-meter-high breaking wave traveling at >70 meters per second. The landslide was preceded by several days of microseismicity, which increased in rate and magnitude until ~1 hour before failure. The landslide produced globally observed long-period seismic waves equivalent in size to a moment magnitude 5.4 earthquake. A long-period (~66 second) global seismic signal, produced by a landslide-induced seiche trapped within the fjord, persisted for up to 36 hours, the second time a days-long seiche had thus been observed. With fjord regions increasingly visited by cruise ships, and climate change making similar events more likely, this unanticipated, near-miss event highlights the growing risk from landslides and tsunamis in coastal environments.

Alaska↗

Rapid seismic and infrasound assessment of large landslides: A case study from Denali National Park and Preserve (Alaska)

Large, rapid landslides are a global hazard that can occur in remote, mountainous areas. Eyewitness reports of landslides and satellite imagery can often be limited or delayed, particularly during inclement weather. However, landslide-generated seismic and infrasound (low-frequency atmospheric sound) waves can be remotely detected in near real-time. This information can significantly expedite characterization and possible landslide response activities. Here, we highlight these capabilities using a > 4 million m³ ice–rock avalanche in Denali National Park and Preserve (Alaska). This event was detected via a landslide-specific seismic location and volume estimation algorithm deployed in Alaska, and — notably — by standard earthquake monitoring systems. Following rapid detection of this event, we combined its seismic and infrasound dataset with optical, synthetic aperture radar, and oblique aerial imagery, multitemporal digital elevation models, and a numerical flow model to reconstruct its failure timeline and dynamics. We apply array processing to infrasound signals traveling > 250 km and find that two precursory events occurred minutes prior to the main failure. We use long-period seismic signals to infer the force exerted by the landslide on the Earth and constrain the rheological parameters of our numerical flow simulation with this result and deposit morphology. The main failure produced a steeply-dipping impulsive initial downward force and reached speeds exceeding 60 m/s. This impulsive force generated relatively strong seismic body waves, which contributed to the earthquake system detection. This large, remote Alaska landslide underscores the key value of seismic and infrasound analysis for rapid landslide assessment and motivates efforts to further operationalize these approaches.

Alaska↗

Seismoacoustic analysis of possible vent-drying sequence during phreatomagmatic activity on 13 July 2021 at Semisopochnoi Island, Alaska

Pulse-like infrasound accompanying volcanic phenomena sometimes suggests interaction with water at the crater. We focus on pulse-like infrasound observed at Semisopochnoi Island in the Aleutian Arc, Alaska, during the phreatomagmatic activity on 13 July 2021. Overflight observations confirmed a small, ephemeral water lake on the crater floor in June 2021. We examined temporal variations in pulse-like infrasound activity, seismoacoustic amplitudes, and spectral characteristics. Pulse-like infrasound activity gradually became intermittent and waned at the end of the day. Seismic-infrasonic amplitude ratio decreased, while the infrasound frequency index increased with time. These results suggest less interaction with water due to drying out of the vent. Satellite observations also show increased ground surface temperatures inside the crater from 13 to 14 July. Numerical simulation suggests a significant topographic effect on the waveform and spectrum of the pulse-like infrasound, which should be considered before interpreting the source process.

Alaska↗

Searching for seismic precursors - The Barry Landslide hazard clean up

The Barry Landslide, located in Barry Arm of Prince William Sound, Alaska, poses a major hazard due to its steep, unstable slopes and the potential for a massive landslide-generated tsunami. With an estimated volume of 500–700 million cubic meters, the Barry Landslide could trigger highly destructive waves. In this study, we focus on seismic signals from the Barry Landslide, which are critical for providing timely tsunami warnings. Since the summer of 2020, the region has been instrumented to monitor the landslide, but the seismic record is complicated by the presence of nearby glaciers and frequent regional earthquakes. Among these signals, we analyze a specific class of short-duration, high-frequency seismic events that exhibit strong seasonal variability, increasing in rate from late summer to mid-winter before ceasing abruptly in late winter or early spring. Our analysis suggests that the source of these signals is likely near or beneath Cascade Glacier, adjacent to the landslide, rather than within the landslide mass itself. We apply detection algorithms to construct a time history for this signal type, which we then compare with environmental factors like precipitation, temperature, and slope displacement data from ground-based radar and remote sensing. Correlations indicate that these seismic events may be driven by seasonal hydrological changes, particularly the freeze-up of subglacial water pathways. While these events are not directly linked to landslide motion, they serve as indirect markers of subsurface hydrological conditions that influence slope stability. Our findings highlight the complex interplay between glaciers, groundwater, and landslide dynamics, emphasizing the need for multi-parameter monitoring to assess evolving geohazards in the region.

Alaska↗

2024 Surprise Inlet landslides: Insights from a prototype landslide‐triggered tsunami monitoring system in Prince William Sound, Alaska

Alaska's coastal communities face growing landslide hazards owing to glacier retreat and extreme weather intensified by the warming climate, yet hazard monitoring remains challenging. As part of ongoing experimental monitoring in Prince William Sound, we detected three large landslides (0.5–2.3 M m 3 ) at Surprise Inlet on 20 September 2024, within the span of an hour. These events were identified in near real-time through seismic data and later confirmed using satellite imagery, tidal records, and infrasound. The landslides generated a modest tsunami, and a 4 cm wave was recorded by a tide gauge 18 km away, marking the first recorded landslide to reach water since monitoring began in this region in 2021. Here, we examine the detection and interpretation of these landslides using multiple data sources and modeling. We demonstrate the effectiveness of this regional seismic monitoring system and show how complementary instrumentation, where available, can enhance detection capabilities.

Alaska↗

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↗

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↗

Catastrophic lava flow levee failure: Precursors, processes, and implications

During an effusive eruption crisis the initial advance of a lava flow is typically the primary focus of model forecasts and hazard management efforts. Flow branching and lateral expansion of lava flows can pose significant dangers within evolving flow fields throughout the duration of an eruption and are an underappreciated hazard. We use field monitoring, infrasound, time lapse imagery, and lidar data collected during the 2018 lower East Rift Zone eruption of Kīlauea (Hawai‘i) to track the origins, progression, and implications of a flow branching event caused by catastrophic levee failure. Our analyses show that surges in effusion rate, rheologic transitions between pāhoehoe and ‘a‘ā flow regimes, slope-breaks, pre-existing topographic highs, and the structure of perched levee walls all played a role in the failure of the levee and subsequent re-routing of the lava flow. Failure of perched lava structures leads to an acutely hazardous situation because lava impounded by the structure can rapidly inundate the landscape. This is the first time a levee failure event has been observed in such detail with numerous monitoring techniques; this unprecedented level of observation provides quantifiable insights into levee failure processes that have important implications for hazard mitigation and an improved understanding of lava flow emplacement dynamics.

Hawaii↗

Seismic techniques and suggested instrumentation to monitor volcanoes

Introduction Changes in the pressure or location of magma can stress or break surrounding rocks and trigger flow of nearby waters and gases, causing seismic signals, such as discrete earthquakes and tremor. These phenomena are types of seismic unrest that commonly precede eruption and can be used to forecast volcanic activity. Mass movements at the surface, including avalanches, debris flows, and lahars, may also generate seismic signals that are specifically addressed in chapter H , this volume (Thelen and others, 2024). Our focus in this chapter is to determine the levels of instrumentation recommended to produce high-quality, well-constrained seismic observations important for early warning of impending eruptions, detecting changes in ongoing eruptions, and characterizing other hazardous volcanic events. There are emerging techniques and new types of instrumentation, such as distributed acoustic sensing or rotational seismometers, that we do not consider here. These types of instrumentation show promise for monitoring but still require maturation before being considered more generally in volcano monitoring. Most of the capabilities mentioned below are universal for all types of volcanic systems, although some are best applied to stratovolcanoes with an apical single vent. In some settings, such as calderas or shield volcanoes, we must broaden coverage to include multiple possible storage regions or vent locations. As an example, Thelen (2014) discretized the long rift zones of shield volcanoes in Hawaiʻi as a set of evenly spaced “vents.” In this construct, each vent comes with recommendations, and several thousand network configurations were simulated to assess the effect on network quality levels and to determine the most efficient network design. The same process could be applied in a caldera setting or a volcanic field, where an evenly spaced grid of potential vents is considered. Localized recommendations for each unique system are beyond the scope of this report and left up to local experts to assess based on the conditions, restrictions, and requirements of each volcano.

Scientific Investigations Report↗

Infrasound for volcano monitoring

Introduction Volcanic eruptions produce acoustic waves when volcanic gases and hot material rapidly expand in the atmosphere. Volcanic activity can produce acoustic signals with a wide range of frequencies, from very long period (>10 seconds) to audible (>20 hertz [Hz]), but the most energetic band is typically in the infrasound from 0.5 to 20 Hz. Studies of volcanic infrasound and the deployment of infrasound for volcano monitoring have increased rapidly in the past two decades as sensors have improved and as analytical tools have become more widely available. Improved sensors and tools have led to a growing diversity of eruptive activity being recorded and characterized, from Hawaiian to Plinian eruption styles at scales from local to global (Johnson and Ripepe, 2011; Fee and Matoza, 2013). Infrasound sensors on volcanoes are most commonly deployed locally with seismic stations, and the combination of co-located seismic and infrasound is more useful for characterizing unrest and detecting changes in activity than either data stream alone (for example, Lyons and others, 2016; Fee and others, 2017a; Matoza and others, 2018). At local (<15 kilometers [km]) to regional (15–250 km) distances from volcanoes, arrays of infrasound sensors are commonly deployed to detect coherent signals, constrain the direction to the source, and provide information on eruption dynamics; thus, infrasound is well suited to regional monitoring of volcanoes when local sensor networks are not feasible. A common usage of infrasound data in an observatory is to provide rapid confirmation that an explosion has occurred (for example, Coombs and others, 2018), although near-real-time eruption intensity quantification is also possible (Fee and others, 2010a; Ripepe and others, 2018; fig. C1). Infrasound is well suited to this task because it is not affected by clouds or precipitation and can propagate long distances with little attenuation. However, wind and ocean noise also produce infrasound, and spatiotemporal variability in the atmosphere can affect the propagation of infrasound, so care must be taken when deploying, analyzing, and interpreting the data. In addition to detecting and monitoring explosive activity, investigations of infrasound records from eruptions help constrain source processes, which in turn enhance syneruptive forecasting capabilities (for example, Fee and others, 2017b; Lyons and others, 2019). The following is a description of the capabilities recommended for real-time monitoring of eruptive phenomena with infrasound. Infrasound is also beginning to be used for tracking hazardous surface flows that occur on volcanoes, including pyroclastic density currents (Ripepe and others, 2010), lahars (Johnson and Palma, 2015), debris flows (Marchetti and others, 2019), snow avalanches (Havens and others, 2014), and lava flows (Patrick and others, 2019). Please refer to the chapter on lahars (this volume; Thelen and others, 2024a) for more information on this application.

Scientific Investigations Report↗

Monitoring lahars

Introduction Lahars, or debris flows that originate from a volcano (Pierson and Scott, 1985; Pierson, 1995), are among the most destructive, far-reaching, and persistent hazards on stratovolcanoes. Lahars may be triggered by syneruptive rapid melting of snow and ice, lake breakouts, or heavy rains in conjunction with large eruptive columns. Alternatively, lahars can follow eruptions, when clastic deposits are mobilized by heavy rainfall or lake breakouts, occurring sporadically for years to decades after large eruptions. Some lahars can travel many tens of kilometers in river drainages stemming from volcanoes, as during the 1980 eruption of Mount St. Helens (Washington) (for example, Janda and others, 1981), recent eruptions of Redoubt Volcano (Alaska) (fig. H1; Dorava and Meyer, 1994; Waythomas and others, 2013), and the 1991 eruption of Mount Pinatubo (Philippines) (Major and others, 1996; Pierson and others, 1996). Large lahars are less likely in the absence of eruptive activity, but still possible. The Electron Mudflow at Mount Rainier (approximately A.D. 1500), Wash., is an example of a potential noneruptive lahar, likely initiated by a spontaneous collapse of weak rock, that reached the Puget Lowland after it flowed dozens of kilometers without a recognized eruptive trigger (Sisson and Vallance, 2009). The extreme hazard posed by lahars was demonstrated tragically by the 1985 Nevado del Ruiz (Colombia) catastrophe that claimed the lives of more than 20,000 people (Naranjo and others, 1986). The potential to provide warnings of minutes to hours in advance of lahar arrival in a populated area (for example, Voight, 1990) is a strong reason to provide special monitoring attention to the hazard. Populated river valleys are located downstream from many very high threat and high threat volcanoes, and these areas could be affected by lahars (for example, Hoblitt and others, 1998). The volume and mobility of lahars are two characteristics that can influence the extent of downstream effects (for example, George and others, 2022). The flows that reach the farthest downstream are mobile and voluminous. Additionally, entrainment of material as a lahar travels downstream may increase the volume, and a lahar that starts small may grow to a destructive size under certain conditions. Increasingly, stratovolcanoes host recreational enthusiasts who could be affected by relatively localized geologic hazards, such as rainfall-induced debris flows, glacial outburst floods, rockfalls, and avalanches. These types of events can be common on many volcanoes, occurring seasonally in the case of debris flows and several times per year in the case of avalanches and rockfalls (for example, Allstadt and others, 2018). Many very high threat stratovolcanoes, especially within the contiguous United States, have low eruption frequencies (less than once per century), such that monitoring networks could be used more often for detection and characterization of small surface flows than for identification of volcanic unrest. Such information can be used to validate avalanche forecasts, inform rescue efforts, or notify other agencies of potentially damaged infrastructure (for example, roads, powerlines, or trails). Note that although many of these smaller surface flows create seismic and infrasound waves, the signals are typically highly distorted by the complex volcanic topography and geology. In general, the smaller the flow, the weaker the geophysical signals that it generates, and thus a denser geophysical network is required to study smaller flows (for example, Allstadt and others, 2018). Lahar detection may not be an appropriate or necessary monitoring capability for all volcanoes. Some very high threat volcanoes, like Kīlauea and Mauna Loa, have no lahar hazards currently, and thus no detection, tracking, and characterization capabilities for lahars are needed. At other very high threat volcanoes, such as Pavlof Volcano, Alaska, lahars might be common but pose minimal threat because the volcano is so remote. Ideally, the local observatory would understand the combination of hazard and risk associated with surface flows and assign monitoring and detection capabilities appropriately. Several volcano monitoring techniques (for example, Real-Time Seismic Amplitude Measurement [RSAM], amplitude-based locations, and infrasound array processing) can be adapted to also detect, characterize, and track debris flows, lahars, and other surface flows, so instrumentation installed for detecting volcanic unrest and eruptions can have multiple purposes. The utility of instrumentation for the purpose of monitoring unrest and lahars further justifies the importance and utility of a dense network of monitoring stations, even if the volcano remains quiescent.

Scientific Investigations Report↗

From field station to forecast: Managing data at the Alaska Volcano Observatory

The Alaska Volcano Observatory (AVO) uses multidisciplinary data to monitor and study dozens of active and potentially active volcanoes. Here, we provide an overview of internally and externally generated data types, tools and resources used in their management, and challenges faced. Data sources include the following: (1) a multiparameter (seismic, infrasound, GNSS, web cameras) ground-based monitoring network that spans 3000 km and transmits data in real time; (2) a variety of satellite-borne sensors that provide information about surface change and volcanic emissions; (3) geologic and gas field campaigns; and (4) other external data products that provide situation awareness. Each data type requires distinct acquisition, processing, storage, visualization, and archiving approaches. AVO uses a variety of externally and internally developed tools to handle individual data types as well as multidisciplinary volcanological data. A primary tool is the Geologic Database of Information on Volcanoes in Alaska (GeoDIVA), which stores detailed, searchable information on more than 140 volcanoes and over 1000 eruptions and unrest events, including images, eruption descriptions, and geologic station and sample data, metadata, and analyses. It interacts with other internal tools that store monitoring reports and other operational records. Additional data management resources used by AVO assist with alarms and alerts, state-of-health monitoring, and multiparameter visualization. Requirements for 24/7 accessibility, the ever-expanding portfolio of data, and transitioning new tools from development to operations are all challenges faced by AVO and other volcano observatories. AVO strives to meet FAIR data practices and ensure that data are available to national and international community efforts using external repositories as well as those hosted by AVO and its parent institutions.

Alaska↗

2020 Volcanic activity in Alaska—Summary of events and response of the Alaska Volcano Observatory

The Alaska Volcano Observatory responded to eruptions, volcanic unrest or suspected unrest, increased seismicity, and other significant activity at nine volcanic centers in Alaska in 2020. The most notable volcanic activity in 2020 was an eruption of Shishaldin Volcano, which produced lava flows, lahars, and ash. Mount Cleveland had one small ash-producing eruption in June but was quiet thereafter. Other activity documented in 2020 consisted of elevated seismicity at the volcanoes Mount Veniaminof, Pavlof Volcano, Makushin Volcano, Atka volcanic complex (Korovin Volcano), Great Sitkin Volcano, and Semisopochnoi Island. Finally, the resuspension of ash deposited during the 1912 Novarupta-Katmai eruption was documented on three occasions.

Alaska↗

2021 Volcanic activity in Alaska and the Commonwealth of the Northern Mariana Islands—Summary of events and response of the Alaska Volcano Observatory

In 2021, the Alaska Volcano Observatory responded to eruptions, volcanic unrest or suspected unrest, increased seismicity, and other significant activity at 15 volcanic centers in Alaska and the Commonwealth of the Northern Mariana Islands. Eruptive activity in Alaska consisted of repeated small, ash-producing, phreatomagmatic explosions from Mount Young on Semisopochnoi Island; an explosion at Great Sitkin Volcano followed by the eruption of a thick lava flow that filled and overflowed the summit crater; weak explosive activity and the eruption of small, channelized flows at Pavlof Volcano; and a short-lived eruption at Mount Veniaminof that produced ash emissions from an intracaldera cone, as well as lava flows confined to a melt pit in the ice mantling the cone’s flank. Mount Cleveland had a period of unrest, but no eruptive activity took place there. Anomalous seismicity was also detected at Atka volcanic complex, Mount Gareloi, and Davidof volcano. New warm springs opened and deposited mud at the summit and north base of Shrub mud volcano. Other activity of note in Alaska consisted of large ice and rock avalanches at Iliamna Volcano and Mount Spurr, ash resuspension events at Mount Katmai and Aniakchak Crater, and anomalous deformation at Mount Okmok that was consistent with a shallow intrusion of magma. In the Commonwealth of the Northern Marianas Islands, a brief, ash-producing eruption occurred at Mount Pagan.

Alaska↗

Back-azimuth estimation of air-to-ground coupled infrasound from transverse coherence minimization

We present the transverse coherence minimization method (TCM)—an approach to estimate the back-azimuth of infrasound signals that are recorded on an infrasound microphone and a colocated three-component seismometer. Accurate back-azimuth information is important for a variety of monitoring efforts, but it is currently only available for infrasound arrays and for seismoacoustic sensor pairs separated by 10 s of meters. Our TCM method allows for the analysis of colocated sensor pairs, sensors located within a few meters of each other, which may extend the capabilities of existing seismoacoustic networks and supplement operating infrasound arrays. This approach minimizes the coherence of the transverse component of seismic displacement with the infrasound wave to estimate the infrasound back-azimuth. After developing an analytical model, we investigate seismoacoustic signals from the August 2012 Humming Roadrunner experiment and the 26 May 2021 eruption of Great Sitkin Volcano, Alaska, U.S.A., at the ranges of 6.5–185 km from the source. We discuss back-azimuth estimates and potential sources of deviation (1°–15°), such as local terrain effects or deviation from common analytical models. This practical method complements existing seismoacoustic tools and may be suitable for routine application to signals of interest.

The Seismic Record↗

Lava fountain jet noise during the 2018 eruption of fissure 8 of Kīlauea volcano

Real-time monitoring is crucial to assess hazards and mitigate risks of sustained volcanic eruptions that last hours to months or more. Sustained eruptions have been shown to produce a low frequency (infrasonic) form of jet noise. We analyze the lava fountaining at fissure 8 during the 2018 Lower East Rift Zone eruption of Kīlauea volcano, Hawaii, and connect changes in fountain properties with recorded infrasound signals from an array about 500 m from the fountain using jet noise scaling laws and visual imagery. Video footage from the eruption reveals a change in lava fountain dynamics from a tall, distinct fountain at the beginning of June to a low fountain with a turbulent, out-pouring lava pond surrounded by a tephra cone by mid-June. During mid-June, the sound pressure level reaches a maximum, and peak frequency drops. We develop a model that uses jet noise scaling relationships to estimate changes in volcanic jet diameter and jet velocity from infrasound sound pressure levels and peak frequencies. The results of this model indicate a decrease in velocity in mid-June which coincides with the decrease in fountain height. Furthermore, the model results suggest an increase in jet diameter, which can be explained by the larger width of the fountain that resembles a turbulent lava pond compared to the distinct fountain at the beginning of June. The agreement between the infrasound-derived and visually observed changes in fountain dynamics suggests that jet noise scaling relationships can be used to monitor lava fountain dynamics using infrasound recordings.

Hawaii↗

Infrasound observations and constraints on the 2018 eruption of Kīlauea Volcano, Hawaii

The 2018 eruption of Kīlauea Volcano was a dynamic event involving explosions, collapses, and fountaining at multiple vents spread over tens of kilometers. The permanent infrasound network operated by the USGS Hawaiian Volcano Observatory (HVO) was well prepared to observe the collapse of the summit, and additional deployments permitted infrasound observations during fissuring in the lower East Rift Zone (LERZ). We provide a summary of infrasound observations, including lava lake spattering, collapses, explosions, rockfall, and lava fountaining, using seismicity and tilt at times to help constrain our interpretations. At the summit of Kīlauea Volcano, we document the process of partial caldera collapse and examine a set of “proto-collapse” events that precede the widely observed events but share many of the same qualities as the larger collapses. For the initial twelve collapse events, we compare the timing of collapse onset to other observations and illustrate the repeatable characteristics of the recorded waveforms and infrasound characteristics associated with each episode of caldera collapse. In the LERZ, we match the acoustic signals with visual observations, including fissure migration, explosions near fissures, and littoral explosions. Lastly, we document and discuss the performance of infrasound alarms during the 2018 Kīlauea eruption. In general, alarming became successful in detecting collapse events at the summit of the volcano after tuning and became a key discriminant in the initial determination of collapse events, especially when visual observations were not available.

Hawaii↗