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Matthew M. Haney

Publications and source records attributed to Matthew M. Haney.

At least 19 recordsLinked to original sources
USGS2026

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↗
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↗
USGS2025

Local, regional, and distal recordings of seismic unrest at Tau Island volcano, American Samoa

A seismic swarm near Taʻū Island, a volcanic island in eastern American Samoa, occurred from July to October 2022. The earliest unrest was noted as felt shaking reports in late July, and instrumentation varied in the beginning of the sequence as the U.S. Geological Survey (USGS) Hawaiian Volcano Observatory responded by installing temporary and then permanent seismometers to monitor the activity. This network variability made it difficult to characterize the earliest seismicity and contextualize the entire sequence to discriminate between an underlying tectonic or volcanic source. Here, we present results analyzing hydroacoustic detections from an International Monitoring System hydrophone array near Wake Island, 4500 km northwest of Taʻū Island volcano. Using least-squares beamforming analysis, we create a catalog of T-wave detections from the direction of Taʻū Island to track the earthquakes, some of which were located by the USGS National EarthquakeInformation Center. Both the rate and hydroacoustic pressures, which we interpret as a proxy for earthquake size, gradually increased from late July to August, peaking on August 19 (rate) and August 24 (size), before decreasing to background in late September. Minutes-long bursts of tremor were also contemporaneously recorded as local network data became avail-able on August 20. Tremor activity continued throughout the rest of August, peaking on August 25, before ending in earlySeptember. These tremor bursts were band-limited to ~ 1–5 Hz and recorded as S waves at a regional station on the island of Upolu in Samoa, 250 km to the west of Taʻū Island. Our results do not constrain the tremor locations, but comparisons of earthquake and tremor reduced displacements recorded locally and regionally suggest a deeper tremor source. We interpret the increase in earthquake size and rate, together with the occurrence, characteristics, and relative depth of the tremor to be the result of magmatic activity beneath Taʻū Island volcano.

American Samoa, Taʻū Island↗
USGS2025

2022 Volcanic activity in Alaska and the Northern Mariana Islands—Summary of events and response of the Alaska Volcano Observatory

In 2022, the Alaska Volcano Observatory responded to eruptions, volcanic unrest or suspected unrest, increased seismicity, and other significant activity at 11 volcanic centers in Alaska and in the Northern Mariana Islands. Eruptive activity in Alaska consisted of repeated small, ash-producing, phreatomagmatic explosions from Mount Young on Semisopochnoi Island; the eruption of a thick lava flow within the summit crater at Great Sitkin Volcano; and weak explosive activity and the eruption of small, channelized flows at Pavlof Volcano. Uplift and an increase in seismicity were detected at Mount Edgecumbe, a long-dormant volcano in southeastern Alaska. Anomalous seismicity was also detected at three other volcanoes, including Trident Volcano, Takawangha volcano, and Davidof volcano. Other activity documented in 2022 includes ash resuspension events at Mount Katmai and Aniakchak Crater, and Mount Cleveland had a period of unrest, but no eruptive activity took place. In the Commonwealth of the Northern Marianas Islands, hydroacoustic detections and a submarine plume observed in satellite data at Ahyi seamount indicated underwater eruptive activity there.

Alaska↗
USGS2025

Generalized Bancroft algorithm for locating earthquakes with P- and S-wave arrival times

Because of similarities between locating an earthquake with seismic stations and locating a Global Positioning System (GPS) receiver from satellites, the Bancroft algorithm developed for GPS processing can be used to locate earthquakes. Such an approach to earthquake location differs from the conventional method of choosing an initial or trial solution and then iteratively improving the solution until convergence. The Bancroft algorithm has the advantage of being a direct, noniterative solution but with the disadvantage of only being able to accommodate a homogeneous velocity model. An additional limitation of the standard Bancroft algorithm is that it considers arrival times in a medium with a single propagation velocity. This poses no problem for GPS processing because electromagnetic waves travel at the speed of light; however, for seismic waves it means the algorithm can be applied to collections of either P ‐ or S ‐wave arrival times. Here, I show how the Bancroft algorithm can be generalized to handle both P ‐ and S ‐wave arrival‐time measurements simultaneously. I also show how to accommodate depth‐varying P ‐ and S ‐wave velocity models. I apply the generalized Bancroft algorithm to microearthquakes beneath Tanaga Volcano in Alaska and compare standard locations from the widely used HYPOINVERSE location code to Bancroft locations and to the output of HYPOINVERSE when setting the trial location to the Bancroft location. I find the Bancroft locations outperform the results from the other methods for shallow earthquakes near sea level, where a quantity known as the geometric dilution of precision is large and linearized approaches such as HYPOINVERSE are expected to struggle.

Alaska↗
USGS2025

Vent-specific unrest at Pavlof Volcano, Alaska: Insights from multidisciplinary data

Pavlof Volcano, a frequently active volcano in the Aleutian arc, has erupted six times between 2007 and 2022. Many Pavlof Volcano eruptions lack detectable precursory seismicity or ground deformation, making them challenging to forecast. Since 2007, these eruptions have ranged from Strombolian to Subplinian (VEI 2–3), with their characteristics seemingly related to their active vent location. Summit vent eruptions tend to be abrupt and explosive, while southeast flank vent eruptions tend to be more effusive and longer-lived. To better understand Pavlof Volcano’s plumbing system and improve eruption forecasts, we integrate geophysical, petrologic, and satellite-based thermal infrared and gas data to conduct a multidisciplinary data synthesis for eruptions between 2007 and 2022. Notably, a recently developed volcano seismicity detection model reveals unique pre- and syn-eruptive seismic tremor regimes associated with each vent system. The summit vent eruptions show greater tremor diversity and resurgent eruptive behavior, whereas the southeast flank vent eruptions show a steady tremor transition over a single eruptive phase. We use local infrasound data and air-to-ground coupled waves to build explosion catalogs, revealing gas-rich explosions during the 2021–2022 eruption from the summit vent, which was previously thought to be inactive during that eruption. Whole-rock composition and ash analyses indicate a near-uniform basaltic andesite composition spanning multiple eruptions, and a consistent anomalous presence of altered olivines in erupted material. We use these findings to propose a new conceptual model for Pavlof Volcano’s plumbing system: a shallow T-junction outlet controlling seismic tremor diversity and the partitioning of gas-charged magma; and an elongated, heated conduit transporting magma from depth and storing partly molten, residual magma between eruptions. We suggest that magma ascent rate and vent sealing modulate eruption style at Pavlof Volcano, where the T-junction directs rapidly ascending, gas-rich magma to the summit vent, and diverts slower rising, gas-poor magma towards the southeast flank vent.

Alaska↗
USGS2024

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↗
USGS2024

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↗
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↗
USGS2024

Sustained co-eruptive increase in seismic velocity below Great Sitkin Volcano due to magma extrusion

Volcanic eruptions carry essential information on the dynamics of volcanic systems. Studies have documented variable eruption styles and eruptive surface deformation. However, co-eruptive subsurface structural changes remain poorly understood. Here we characterize the seismic velocity changes from July 2019 to July 2023 at Great Sitkin Volcano in the central Aleutian volcanic arc, using single-station ambient noise interferometry at five three-component seismic stations. Coincident with the lava effusion since late July 2021, about two months after the explosive eruption on May 26th, 2021, we observe a sustained velocity increase, most prominently to the northwest of the caldera. We attribute this velocity increase to the structural changes with magma extrusion, with the spatial variation controlled by the geometry of the magma system or the property of the shallow volcaniclastics. Our findings offer insights into understanding co-eruptive structural modifications at active volcanoes.

Alaska↗
USGS2024

Fault-dike-magma interactions inferred from transcrustal conical structures under Akutan volcano

Volcano monitoring and eruption forecasting requires accurate characterization of transcrustal magmatic structures in order to place volcanic unrest in context within the system where it occurs. Structural imaging using local seismicity is limited to seismogenic depths. Here, we exploit arrivals in teleseismic receiver functions that change polarity with backazimuth to image two surfaces beneath Akutan volcano in the Aleutian arc. The two surfaces delineate an upper to midcrustal inverted conical volume that deepens and thickens away from the volcanic center, with thicknesses of 3 to 13 km. The top of the volume is at depths of 2-3 km below sea level at distances of ~5-15 km from the caldera center. The bottom is at depths of 7-15 km at the same distances, and the cone’s thickness increases outward from ~5 to ~10 km depth. The signal is best fit by a volume with anisotropy with fast symmetry planes that dip outwards from the center and downward increases in shear velocity at both interfaces. The upper boundary coincides with the top of Akutan’s volcano-tectonic (VT) seismogenic zone, with the VT seismicity exhibiting outward dipping planar features matching the anisotropic fast plane orientation within the volume. The bottom of the anisotropic volume is below the termination depth of the majority of the VT seismicity and is therefore likely associated with the brittle-ductile transition. Long-period (LP) events associated previously with magma movement are concentrated below the anisotropic VT volume. Because of the strong spatial association with VT seismicity, we interpret the volume as consisting of concentric outward dipping faults/dikes that align the seismogenic response to stress changes from magmatic processes. Our observations map this volume independent of the present-day seismicity distribution and thus provide a spatially more complete image of the magmatic system.

Alaska↗
USGS2024

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↗
USGS2023

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↗
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

A specific earthquake processing workflow for studying long-lived, explosive volcanic eruptions with application to the 2008 Okmok Volcano, Alaska, eruption

By providing unrivaled resolution in both time and space, volcano seismicity helps to chronicle and interpret eruptions. Standard earthquake detection methods are often insufficient as the eruption itself produces continuous seismic waves that obscure earthquake signals. We address this problem by developing an earthquake processing workflow specific to a high-noise volcanic environment and applying it to the explosive 2008 Okmok Volcano eruption. This process includes applying single-channel template matching combined with machine-learning and fingerprint-based techniques to expand the existing earthquake catalog of the eruption. We detected an order of magnitude more earthquakes, then located, relocated, determined locally calibrated magnitudes, and classified the events in the enhanced catalog. This new high-resolution earthquake catalog increases the number of observations by about a factor of 10 and enables the detailed spatiotemporal seismic analysis during a large eruption.

Alaska↗