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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 26 May 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 shallow volcaniclastics. Our findings offer insights into understanding co‐eruptive structural modifications at active volcanoes. Plain Language Summary: Volcanic eruptions provide important insights into how volcanoes work and the potential risks they pose. This study looks at the ongoing eruption at Great Sitkin Volcano in the central Aleutian volcanic arc to better understand the changes happening beneath the surface. We measure the changes in the velocity of seismic waves, with data from five seismometers on the island. We collected data from July 2019 to July 2023, covering different eruptive stages and multiple lava effusion episodes during the eruption that started on 26 May 2021. Coincident with the lava effusion since late July 2021, we notice a significant increase in seismic velocities, most prominently at a station northwest of the volcano's central crater. We conclude that this increase reflects the structural change in the shallow subsurface due to magma extrusion, with deflation of the edifice and closure of cracks. The findings in this study help to improve our understanding of how volcanoes behave during eruptions. Key Points: We observe prominent, spatially variable increases in seismic velocity during lava effusions at Great Sitkin Volcano since late July 2021The sustained co‐eruptive increase in seismic velocity reflects structural changes with crack closures as the consequence of magma extrusionThe distinct spatial variation of the velocity increase may be controlled by reservoir geometry or the strength of shallow volcaniclastics [ABSTRACT FROM AUTHOR]

Volcanic tremor is a semi‐continuous seismic and/or acoustic signal that occurs at time scales ranging from seconds to years, with variable amplitudes and spectral features. Tremor sources have often been related to fluid movement and degassing processes, and are recognized as a potential geophysical precursor and co‐eruptive geophysical signal. Eruption forecasting and monitoring efforts need a fast, robust method to automatically detect, characterize, and catalog volcanic tremor. Here we develop VOlcano Infrasound and Seismic Spectrogram Network (VOISS‐Net), a pair of convolutional neural networks (one for seismic, one for acoustic) that can detect tremor in near real‐time and classify it according to its spectral signature. Specifically, we construct an extensive data set of labeled seismic and low‐frequency acoustic (infrasound) spectrograms from the 2021–2022 eruption of Pavlof Volcano, Alaska, and use it to train VOISS‐Net to differentiate between different tremor types, explosions, earthquakes and noise. We use VOISS‐Net to classify continuous data from past Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022). VOISS‐Net achieves an 81.2% and 90.0% accuracy on the seismic and infrasound test sets respectively, and successfully characterizes tremor sequences for each eruption. By comparing the derived seismoacoustic timelines of each eruption with the corresponding eruption chronologies compiled by the Alaska Volcano Observatory, our model identifies changes in tremor regimes that coincide with observed volcanic activity. VOISS‐Net can aid tremor‐related monitoring and research by making consistent tremor catalogs more accessible. Plain Language Summary: Volcanic tremor is a persistent vibration of the ground, atmosphere, or both that can occur before and during volcanic eruptions. Despite its importance in volcano monitoring and eruption forecasting, volcano observatories do not have a reliable way of automatically detecting and identifying tremor due to the variable intensities and frequencies at which it occurs. In order to accomplish this, we develop and test a pair of machine learning models that classify spectrograms (i.e., images representing a signal's frequency content over time) from seismic and low‐frequency acoustic data. The models are trained on manually labeled images derived from the recent 2021–2022 eruption of Pavlof Volcano, Alaska, which demonstrated substantial signal diversity (e.g., different tremor types, earthquakes, explosions and noise). Our models achieve 81.2% and 90.0% accuracy on the seismic and low‐frequency acoustic test sets respectively, and perform well when applied to data recorded from past Pavlof Volcano eruptions. In addition, transitions in tremor sequences identified from our analysis generally coincide with shifts in eruptive patterns from Pavlof Volcano. Our tools can help volcano observatories systematically monitor tremor, and advance tremor research by making catalogs of their occurrences more consistent and accessible. Key Points: We develop a pair of convolutional neural networks that detect and classify volcano seismic and acoustic signalsWe apply our models to Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022) and derive volcano seismoacoustic timelinesThe seismoacoustic timelines reveal shifts in unrest regimes linked to explosions and effusive activity [ABSTRACT FROM AUTHOR]

Seismicity during explosive volcanic eruptions remains challenging to observe through the eruptive noise, leaving first‐order questions unanswered. How do earthquake rates change as eruptions progress, and what is their relationship to the opening and closing of the eruptive vent? To address these questions for the Okmok Volcano 2008 explosive eruption, Volcano Explosivity Index 4, we utilized modern detection methods to enhance the existing earthquake catalog. Our enhanced catalog detected significantly more earthquakes than traditional methods. We located, relocated, determined magnitudes and classified all events within this catalog. Our analysis reveals distinct behaviors for long‐period (LP) and volcano‐tectonic (VT) earthquakes, providing insights into the opening and closing cycle. LP earthquakes occur as bursts beneath the eruptive vent and do not coincide in time with the plumes, indicating their relationship to an eruptive process that occurs at a high pressurization state, that is, partially closed conduit. In contrast, VT earthquakes maintain a steadier rate over a broader region, do not track the caldera deflation and have a larger b‐value during the eruption than before or after. The closing sequence is marked by a burst of LPs followed by small VTs south of the volcano. The opening sequence differs as only VTs extend to depth and migrate within minutes of the eruption onset. Our high‐resolution catalog offers valuable insights, demonstrating that volcanic conduits can transition between partially closed (clogged) and open (cracked) states during an eruption. Utilizing modern earthquake processing techniques enables clearer understanding of eruptions and holds promise for studying other volcanic events. Plain Language Summary: Sparse instrumentation and high noise levels prevent us from seeing most of the quakes that occur. This problem is particularly severe during large volcanic eruptions when the explosive processes hide most of the seismic waves. Here, we overcome the problem by utilizing a suite of signal processing methods to dramatically increase our ability to see earthquakes during the noisiest, most difficult to monitor stage of a volcanic eruption. This high‐resolution data set unveils features of the volcanic structure as well as the dynamic interaction between the solid medium and the fluids. When the system is closed (clogged), the rocks are stressed by the trapped pressurized fluids and then break in earthquakes. When the system is open (cracked), ash and steam plumes grow higher, the rock is not being stressed as much and the number of observed earthquakes is lower around the eruptive vents. The volcano is displaying both open and closed vent behavior during a single eruption. Key Points: We analyzed the enhanced seismic catalog of a long‐lived explosive volcanic eruptive sequenceLong‐period earthquakes track eruptive processes when the system is partially closed and they are not associated with mass ejectionVolcano‐tectonic earthquakes are a secondary effect that does not track the deflation and occur at a low stress‐state [ABSTRACT FROM AUTHOR]

Volcanic seismicity provides essential insights into the behavior of an active volcano across multiple time scales. However, to understand how magma moves as the eruption cycle develops, better knowledge of the geometry and physical properties of the magma plumbing system is required. In this study, using full‐wave ambient noise tomography, we image the three‐dimensional (3‐D) crustal shear‐wave velocity structure below Great Sitkin Volcano in the central Aleutian Arc. The velocity model reveals two low‐velocity anomalies correlating with the migration of volcanic seismicity. With a bulk melt fraction of about 2.5%–9%, these low‐velocity anomalies are interpreted as mushy magma reservoirs. We propose a six‐stage eruption cycle to explain the migration of seismicity and the alternating eruption of the two reservoirs with different recharging histories. These findings have broad implications for the dynamics of magma plumbing systems and the structural control of eruption behaviors. Plain Language Summary: Understanding magma accumulation and transport systems below active volcanoes is essential for predicting eruption behavior and assessing the potential hazards. The distribution of earthquakes can partly be used to infer the development of magmatic activity at different times. However, to understand how magma moves at different stages of an eruption cycle, better knowledge of what the magma plumbing system looks like is necessary. In this study, we use an advanced seismic imaging method to construct the 3‐D crustal shear‐wave velocity structure below Great Sitkin Volcano in the central Aleutian Arc. The velocity model reveals two crustal magma reservoirs, which correlate with the migration of seismicity. We propose a six‐stage eruption cycle to explain the evolution of seismicity in space and time across the island and the alternating eruption of two reservoirs. The findings in this study help to understand better the control of eruption behaviors by the underlying magma plumbing system at active volcanoes. Key Points: The pre‐ and co‐eruptive seismicity below Great Sitkin Volcano, Alaska, shows a spatiotemporal migrationA new 3‐D shear‐wave velocity model reveals two crustal low‐velocity anomalies that correlate with the migrating seismicityWe propose a six‐stage eruption cycle involving two magma reservoirs to explain the long‐term and short‐term seismicity patterns [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Volcanic eruptions are some of the most challenging events to study seismically. The eruption itself continually excites seismic waves that hide the signatures of the accompanying earthquakes. The jetting of the plume, explosions, and impact of material on the ground all combine to create a blind spot in the seismological record. In this work, we develop a workflow that can overcome these limitations and reveal events that would otherwise remain hidden in the high‐noise, continuous seismic record. We utilize a combination of machine‐learning and related methods to increase the number of observed events by almost one order of magnitude. We determine the locations of the events using modern, high‐precision methods and use these locations with a customized, calibrated local magnitude scale for the region, which is necessary for volcanoes where seismic waves decay much faster than in the regions where standard magnitude scales were developed. We also automatically separate ordinary earthquakes from the more unusual long‐period ones and see an increase in detectability of both populations. The combined workflow now allows us to illuminate potentially the most interesting, and certainly most obscure, parts of the volcanic seismic cycle. Key Points: We develop a complete workflow suitable for co‐eruptive seismicity, which is inaccessible by normal seismic methodsWe combine different detection algorithms to increase the number of events of an explosive volcanic eruption by almost an order of magnitudeAutomated classification detects both long‐period and ordinary volcanic earthquakes [ABSTRACT FROM AUTHOR]

Surficial mass wasting events are a hazard worldwide. Seismic and acoustic signals from these often remote processes, combined with other geophysical observations, can provide key information for monitoring and rapid response efforts and enhance our understanding of event dynamics. Here, we present seismoacoustic data and analyses for two very large ice–rock avalanches occurring on Iliamna Volcano, Alaska (USA), on 22 May 2016 and 21 June 2019. Iliamna is a glacier-mantled stratovolcano located in the Cook Inlet, ∼200 km from Anchorage, Alaska. The volcano experiences massive, quasi-annual slope failures due to glacial instabilities and hydrothermal alteration of volcanic rocks near its summit. The May 2016 and June 2019 avalanches were particularly large and generated energetic seismic and infrasound signals which were recorded at numerous stations at ranges from ∼9 to over 600 km. Both avalanches initiated in the same location near the head of Iliamna's east-facing Red Glacier, and their ∼8 km long runout shapes are nearly identical. This repeatability – which is rare for large and rapid mass movements – provides an excellent opportunity for comparison and validation of seismoacoustic source characteristics. For both events, we invert long-period (15–80 s) seismic signals to obtain a force-time representation of the source. We model the avalanche as a sliding block which exerts a spatially static point force on the Earth. We use this force-time function to derive constraints on avalanche acceleration, velocity, and directionality, which are compatible with satellite imagery and observed terrain features. Our inversion results suggest that the avalanches reached speeds exceeding 70 m s -1 , consistent with numerical modeling from previous Iliamna studies. We lack sufficient local infrasound data to test an acoustic source model for these processes. However, the acoustic data suggest that infrasound from these avalanches is produced after the mass movement regime transitions from cohesive block-type failure to granular and turbulent flow – little to no infrasound is generated by the initial failure. At Iliamna, synthesis of advanced numerical flow models and more detailed ground observations combined with increased geophysical station coverage could yield significant gains in our understanding of these events. [ABSTRACT FROM AUTHOR]

Bogoslof volcano, a back-arc volcano in Alaska's Aleutian arc, began an eruptive sequence in mid-December 2016 that ended in late August 2017, with 70 individual eruptive episodes. Because there were no local seismic or infrasound stations on the island, the Alaska Volcano Observatory (AVO) relied on distant geophysical networks and remote sensing techniques to assess activity during the eruption. AVO maintains six infrasound arrays to monitor activity along the Aleutian arc: Adak, the Island of Four Mountains, Okmok, Akutan, Sand Point, and Dillingham. Eruption detection at infrasound arrays is subject to local as well as mesoscale meteorological conditions that vary greatly over both short and long timescales. Infrasound detections from the array nearest to Bogoslof (Okmok), with a latency of about 3 min, played a crucial role in monitoring activity during the eruption. Despite the relative proximity of the Okmok array to Bogoslof (60 km), infrasound detections were not uniformly observed with only about two-thirds of the events successfully detected. The farthest array at Dillingham (816 km) detected approximately half of the explosive events, with all other arrays detecting less than half of the events. We compare observations with infrasound propagation model predictions, using both normal mode and parabolic equation forward models, to interpret the variation in detections of the 70 explosive events across the AVO infrasound network. The forward models utilize the newly created, publicly available AVO-G2S atmospheric reconstruction using numerical weather predictions data for the lower atmosphere, coupled with upper atmosphere empirical models of wind speeds and temperature. We find that long-range detections (> 100 km) of Bogoslof events are largely aligned with seasonal variability in favorable propagation conditions, while regional detections (< 100 km) are less consistent with propagation modeling. Understanding the output of numerical models in comparison to past observations will facilitate their use in future operational settings for AVO and other observatories. [ABSTRACT FROM AUTHOR]

We combine global detections of volcanic lightning with acoustic and hydroacoustic data to investigate novel indications of plume electrification in ground-based, geophysical data streams during the 2016–2017 eruption of Bogoslof volcano, Alaska. Such signals offer additional ways to diagnose the occurrence of volcanic lightning and confirm whether eruptive activity is producing significant amounts of ash. We discuss three signatures of lightning activity: volcanic thunder, electromagnetic pulses arising from lightning-induced voltages in cabling, and hydroacoustic signals associated with volcanic lightning. Observations of these phenomena provide additional insights into volcanic lightning activity and reveal several periods of electrical activity that were not otherwise detected during the Bogoslof eruption. [ABSTRACT FROM AUTHOR]

We analyze seismic tremor recorded during eruptive activity over the course of the 2016–2017 eruption of Bogoslof volcano, Alaska. Only regional recordings of the tremor wavefield exist for Bogoslof, making it a challenge to place the recordings in context with other eruptions that are normally captured by local seismic data. We apply a technique of time-frequency polarization analysis to three-component seismic data to reveal the wavefield composition of Bogoslof eruption tremor. We find that at regional distances, the tremor is dominated by P-waves in the band from 1.5 to 10 Hz. Using this information, along with an enriched Bogoslof earthquake catalog, we obtain estimates of average reduced displacement (DR) for eruption tremor during 25 of the 70 Bogoslof events. DR reaches as high as approximately 40 cm2 for two of the major events, similar to other VEI~3 eruptions in Alaska. Overall, average reduced displacement displays a weak correlation to plume height during the first half of the 9-month-long eruption sequence, with a few notable exceptions. The two events with the highest DR values also generated measurable eruption tremor at very-long-periods (VLP) between 0.05 and 0.15 Hz. [ABSTRACT FROM AUTHOR]

Bogoslof volcano, Alaska, experienced at least 70 explosive eruptions between 12 December 2016 and 31 August 2017. Due to its remote location and limited local monitoring network, this eruption was monitored and characterized primarily using remote geophysical and satellite techniques. SO2 emissions from Bogoslof were persistently detected by the Infrared Atmospheric Sounding Interferometer (IASI) satellite sensors. Of Bogoslof's 70 explosive events, 50% produced measurable SO2 masses ranging from 0.1 to 21.5 kt, with a median and standard deviation of 0.7 ± 4.0 kt SO2, respectively. Here, we compare IASI-derived SO2 masses from Bogoslof events to complementary geophysical datasets to provide insights into eruption source processes, namely the degree of seawater scrubbing of water-soluble SO2 and variations in magma flux. Correlations with the number of lightning strokes and infrasound energy are expected to indicate magma-flux as a controlling process, while correlations with infrasound frequency index are expected to indicate variations in vent-water content as a controlling factor. These comparisons suggest that the measured SO2 masses are primarily a function of eruption magnitude (degassed magma mass) and that scrubbing of SO2 emissions by vent seawater may have exerted a minor effect on the observed SO2 masses. SO2 masses were combined with petrologic constraints on melt inclusion and matrix glass S concentrations to calculate degassed magma masses and volumes. The cumulative SO2-derived degassed magma mass and estimated volume (dense-rock equivalent) for the full Bogoslof eruption were found to be 2.8 × 1010 kg and 9.3 × 106 m3, respectively. When individual event masses are compared against event masses calculated using an empirical plume-height method, a strong correlation is found (R2 = 0.83), with better than order-of-magnitude agreement in most cases. These estimates of eruption masses provide useful information on the magnitude, behavior, and associated hazards of the 2016–2017 eruption, and potentially future unrest at Bogoslof volcano. [ABSTRACT FROM AUTHOR]

Characterizing the state of the volcanic vent is key for interpreting observational datasets and accurately assessing volcanic hazards. This is particularly true for remote, complex eruptions such as the 2016–2017 Bogoslof volcano, Alaska eruption sequence. Bogoslof's eruptions in this period were either shallow submarine or subaerial, or some combination of both. Our results demonstrate how low-frequency sound waves (infrasound), integrated with seismic and satellite data, can provide unique insight into shallow vent processes, otherwise not available. We use simple metrics, such as the infrasound frequency index (FI), event duration, and acoustic-seismic amplitude ratio, to look at changes in the elastic energy radiation and infer changes in seawater access to the vent. Satellite imagery before and after selected eruptions is used to ground-truth inferences on vent conditions. High FI and gradual increases in infrasound frequency content at Bogoslof correspond with transitions from submarine to subaerial vent conditions and a diminished or absent role of water, likely resulting in a drying out of the vent region. Event durations generally correlate with high FI and the range of FI values for each event, suggesting long duration events were more effective at drying out the vent region. A trend from low to high acoustic-seismic amplitude ratios for some long duration events also suggests an increase in acoustic efficiency as the vent dried out. We demonstrate that infrasound can serve as a robust indicator of seawater involvement for Bogoslof and other shallow submarine eruptions that may not be inferable from other datasets, particularly in near-real-time. [ABSTRACT FROM AUTHOR]

In December 2016, the unmonitored Bogoslof volcano unexpectedly began erupting, producing at least 70 explosions over the next 8.5 months. Roughly half of the explosions were preceded by observable short-term precursory activity, defined here as seismic activity ending within 30 min of the explosion onset or an earthquake swarm ending within a few hours before the explosion onset. Five different types of precursors were noted during the Bogoslof eruption: non-swarm earthquakes, earthquake swarms, rapid rate events, tremor, and chromatic tremor. Most of the precursors that were detectable by the nearby seismic networks (> 45 km away) occurred during the first 3 months of the eruption, with nearly all precursory earthquake swarms occurring during this period. A hydrophone deployed ~ 7 km northeast of the summit for the last 3 months of the eruption detected weaker activity that was not recorded by the more distant seismic networks. We describe the observed precursors and discuss their inter-relations. Overall, the precursors seem indicative of processes occurring throughout the volcanic system and suggest possible changes in the system during the eruption. We then compare the short-term precursors with explosion parameters, such as plume height, duration, and occurrence of lightning, to look for relations that may provide more insight into the eruption dynamics and subsurface processes and help to further improve forecasting. We find no obvious relations between any of the precursor or explosion parameters, suggesting that their use in forecasting eruption characteristics at Bogoslof is limited. [ABSTRACT FROM AUTHOR]

The article discusses research in the same issue by F. Brenguier et al. on the interaction between earthquakes and volcanoes. Topics include the study's focus on a change in seismic velocity detected beneath volcanoes in Honshu, Japan, after the 2011 Tohoku-Oki earthquake, the triggering of volcanic fluid flow by seismic waves, and research into seismic velocity susceptibility.

Abstract Winds and temperature gradients greatly affect the long-range propagation of infrasound. The spatio-temporal variability of these parameters must therefore be accurately characterized to correctly interpret recorded infrasound at long distances, specifically to differentiate between source and propagation effects. Here we present the first results of an open source reanalysis model, termed Alaska Volcano Observatory Ground-to-Space (AVO-G2S), constructed to accurately characterize the atmosphere and model long-range infrasound propagation from volcanic eruptions in Alaska. We select a number of case studies to examine recent eruptions of Alaskan volcanoes whose ash emissions posed a threat to air traffic, including the two most recent eruptions of Pavlof Volcano and two typical explosions from Cleveland Volcano. Strong tropospheric ducting and low noise at the station during the 21 July 2015 explosion of Cleveland Volcano led to an automated detection of the explosion at an infrasound array 992 km away, whereas low signal-to-noise ratio for the 6 November 2014 Cleveland Volcano explosion helps explain the non-detection in real-time of a predicted strong stratospheric arrival. For the November 2014 Pavlof eruption, discrepancies between local seismic data and a distal infrasound array 460 km away cannot be solely explained by changes in atmospheric conditions, though some features of the complex propagation predictions follow the trends in long-range infrasound signals. The most recent eruption of Pavlof Volcano in March 2016 shows minimal changes in propagation conditions throughout the eruption and therefore indicates that the signals detected at long-range primarily reflect source processes. These results show how detailed examination of the acoustic propagation conditions provides insight into detection capability and eruption dynamics. Future work will implement AVO-G2S and high-resolution long-range infrasound propagation modeling in real-time for Alaskan volcanoes of interest. Highlights • Infrasound is important for monitoring in Alaska where volcanoes are both remote and numerous. • AVO-G2S is an updated ground-to-space atmospheric model tailored to the needs of the Alaska Volcano Observatory. • The long-range propagation of infrasound can be adequately modeled using AVO-G2S. • We analyze recent eruptions in Alaska and compare predicted propagation with detections. [ABSTRACT FROM AUTHOR]

The 2018 eruption of Kīlauea volcano, Hawai'i, 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. • Analyzed cyclic deformation and earthquake swarms from stress loading and faulting. • Evidence for asymmetric failure of the magma reservoir roof observed. • Identified 3 phases of caldera collapse, including decoupling/recoupling of roof. • Observed tangential motion of central piston and movement of peripheral block. • Caldera collapse mostly a passive result of magma reservoir drainage. [ABSTRACT FROM AUTHOR]