Geology topics

Kyle R. Anderson

Publications and source records attributed to Kyle R. Anderson.

At least 19 recordsLinked to original sources

USGS2026

Advances in volcano monitoring driven by the first decade of Sentinel-1 observations

Sentinel-1 has transformed how satellite radar data (SAR and InSAR) are used in volcanology. The systematic, long-term archive and open-access policy means that volcano observatories and research organisations have invested in integrating Sentinel-1 datasets into their monitoring systems. We identify 233 high priority volcanoes and estimate that Sentinel-1 data has been used in peer-reviewed publications for 90 of them. We examine a global archive of 3.3 million automatically processed Sentinel-1 interferograms of volcanoes and use machine learning methods to identify eruptions and periods of unrest. We then review the ways in which InSAR data are being used in different contexts. At frequently erupting basaltic systems in Iceland, Hawaiʻi, the Galápagos , and Piton de la Fournaise, InSAR has become an effective monitoring tool and is integrated with other datasets and models to forecast magma pathways. For large explosive eruptions, deformation measurements often remain challenging, but SAR backscatter is increasingly used to map damaging flows and measure the changing shape of ocean islands. Sentinel-1's long archive provides critical baseline measurements that are vital for measuring slow deformation, capturing new periods of unrest and providing fresh insights into subsurface dynamics. Understanding the drivers of deformation remains challenging and typically relies on integration with external datasets. Future European Space Agency missions have the potential to improve both resolution and coverage providing an even richer dataset to further enhance global volcano monitoring

Erta Ale volcano↗

USGS2026

Insights from more than a year of ongoing episodic lava fountaining at Kilauea

Episodic lava fountaining is rare globally, with only three occurrences from Kīlauea (Hawaii) since 1823. Beginning in December 2024, an eruption within the summit crater, Halemaʻumaʻu, has produced 52 fountaining episodes to date, each lasting typically for hours and separated by days to weeks. Although multiple mechanisms have been proposed, fundamental questions remain about how magma reservoir pressure, conduit permeability, and volatile exsolution control fountaining onset. We present a multi-parameter record of episodic fountaining at Kīlauea using modern geochemical, geophysical, and geologic monitoring. These observations revealed systematic correlations between fountaining and summit inflation, enabling unprecedented quantitative forecasts. This eruption will provide new constraints on the timing, evolution, and processes driving episodic fountaining globally, and highlights Kīlauea’s role as a natural laboratory for studying volcanic processes.

Hawaii↗

USGS2024

Triggering the 2022 eruption of Mauna Loa

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

Hawaii↗

USGS2024

Ground deformation and gravity for volcano monitoring

Introduction When magma accumulates or migrates, it can cause pressurization and related ground deformation. Characterization of surface deformation provides important constraints on the potential for future volcanic activity, especially in combination with seismic activity, gas emissions, and other indicators. A wide variety of techniques and instrument types have been applied to the study of ground deformation at volcanoes (sidebar, p. 2; Dzurisin, 2000, 2003, 2007). Geodetic instruments include continuously recording Global Navigation Satellite System (GNSS; of which the United States’ Global Positioning System is one example) stations (fig. D1), borehole tiltmeters, and interferometric synthetic aperture radar (InSAR) measurements (from satellites, occupied and unoccupied aircraft systems, and ground-based sensors). Additional geodetic measurements like continuous- and survey-mode gravity (fig. D2) can contribute substantially to interpreting these data. Borehole strainmeters (see chapter K , this volume, by Hurwitz and Lowenstern, 2024) also have outstanding utility for monitoring deformation, although because of cost and permitting challenges, we do not include them as part of standard volcano monitoring networks for U.S. volcanoes. Still other techniques like light detection and ranging (lidar), structure from motion, and optical satellite data can be used to derive gross topographic changes, which can be used to map volcanic deposits, infer eruption rates, and gain insights into the source processes associated with eruptive activity (see chapter G , this volume, on tracking surface changes caused by volcanic activity; Orr and others, 2024). Experience has shown that no single geodetic monitoring technique is adequate to detect and track the entire range of ground-motion patterns that occur at volcanoes, primarily because of the temporal and spatial diversity of volcano deformation (fig. D3). Similarly, the magnitude of surface deformation varies widely. Geodetic monitoring strategies should therefore include multiple techniques and instrument types to cover a wide range of spatial and temporal scales. In identifying recommendations for geodetic instrumentation for volcano monitoring networks, we attempted to maximize the diversity of instrument types to measure the full range of deformation signals and minimize their expense and number; thus, we do not include several well-known deformation-monitoring techniques in our recommendations. Extensometers, for example, measure strains over distances of a few meters and have an excellent record of success in detecting changes in preeruptive localized ground motion across existing cracks, including at Mount St. Helens, Washington (Iwatsubo and others, 1992), and Piton de la Fournaise, Réunion Island (Peltier and others, 2006). Despite being relatively inexpensive, extensometers are best used primarily when localized ground displacements (for example, ground cracks) need to be tracked, and are not necessary at all volcanoes. In considering volcano deformation monitoring strategies, two complicating factors are deserving of special attention. First, not all deformation is driven by subsurface magmatic activity—for example, at many large stratovolcanoes (for example, Mount Rainier), flank collapses and landslides are significant geologic hazards (Reid and others, 2001) that may occur even in the absence of magmatic activity. Monitoring the stability of volcanoes is thus another critical application of geodetic monitoring networks to inform hazard assessment. One of the most famous examples of edifice instability is the large flank collapse that initiated the May 18, 1980, eruption of Mount St. Helens. Deformation monitoring had detected a bulge on the north flank of the mountain in April 1980 that was expanding by several meters per day (Lipman and others, 1981). Given that flank collapses can happen at any time during a period of volcanic unrest (or even outside a period of unrest), the capability to assess edifice stability is critical. Second, although volcanoes are commonly treated as idealized structures that erupt from single points, like centralvent stratovolcanoes, many are characterized by long rift zones from which eruptions may originate, and distributed volcanic fields are characterized by broadly spaced vents. For example, linear dikes are common at Kīlauea, Mauna Loa, and between Mount Shasta and Medicine Lake in California. At Kīlauea, one of these linear dikes emerged more than 40 kilometers (km) away from the summit of the volcano during the lower East Rift Zone eruption in 2018. Other volcanic fields, like Lassen volcanic center, California, or the San Francisco Volcanic Field, Arizona, have many small vents spread over a wide area. Although the instrumentation guidelines presented in this chapter remain phrased for central-vent volcanoes, they should be modified as needed in the context of the eruptive characteristics of each individual volcanic system. Spatial analysis of geodetic network coverage could help to ensure adequate instrumentation in areas where volcanism can occur over a broad area as opposed to a central vent. As an example, consider the adjacent volcanoes Mount Shasta and Medicine Lake. If station locations are chosen based only on the distance from the centers of the volcanoes, then any geodetic anomalies between the two volcanoes—an area of potential volcanism as indicated by the presence of volcanic features—may remain undetected by ground-based instrumentation. The spatial analysis is accomplished via a grid of pressure point sources (Mogi, 1958) evenly distributed across the map area, at a depth of 5 km in this example (fig. D4). Each source is inflated until predicted deformations exceed the GNSS white noise uncertainty estimates at one site (Langbein, 2017; Murray and Svarc, 2017). This volume of detectable magma provides a measure of the quality of the coverage (fig. D4). The results indicate that, as of 2022, there is a large area between Mount Shasta and Medicine Lake volcano with existing mapped dikes in which a substantial amount of magma could intrude without being detected geodetically. Applying this style of analysis to individual volcanic systems can provide a guide for designing network geometry given the expected locations of future eruptions.

Scientific Investigations Report↗

USGS2024

Physics-based forecasts of eruptive vent locations at calderas

Constraining stresses in the Earth's crust in volcanic regions is critical for understanding many mechanical processes related to eruptive activity. Dike pathways, in particular, are shaped by the orientation of principal stress axes. Therefore, accurate models of dike trajectories and future vent locations rely on accurate estimates of stresses in the subsurface. This work presents a framework for probabilistic constraint of the stress state of calderas by combining three-dimensional physics-based dike pathway models with observed past vent locations using a Monte Carlo approach. The retrieved stress state is then used to produce probability maps of future vent opening across a caldera. We test our stress inversion and vent forecast approach on synthetic scenarios, and find it successful depending on the distribution of the available vents and the complexity of the volcano's structural history. We explore the potential and limitations of the approach, show how its performance is sensitive to the assumptions in the models and available prior information, and discuss how it may be applied to real calderas.

Journal of Geophysical Research: Solid Earth↗

USGS2024

Computationally efficient emulation of spheroidal elastic deformation sources using machine learning models: a Gaussian-process-based approach

Elastic continuum mechanical models are widely used to compute deformations due to pressure changes in buried cavities, such as magma reservoirs. In general, analytical models are fast but can be inaccurate as they do not correctly satisfy boundary conditions for many geometries, while numerical models are slow and may require specialized expertise and software. To overcome these limitations, we trained supervised machine learning emulators (model surrogates) based on parallel partial Gaussian processes which predict the output of a finite element numerical model with high fidelity but >1,000× greater computational efficiency. The emulators are based on generalized nondimensional forms of governing equations for finite non‐dipping spheroidal cavities in elastic halfspaces. Either cavity volume change or uniform pressure change boundary conditions can be specified, and the models predict both surface displacements and cavity (pore) compressibility. Because of their computational efficiency, using the emulators as numerical model surrogates can greatly accelerate data inversion algorithms such as those employing Bayesian Markov chain Monte Carlo sampling. The emulators also permit a comprehensive evaluation of how displacements and cavity compressibility vary with geometry and material properties, revealing the limitations of analytical models. Our open‐source emulator code can be utilized without finite element software, is suitable for a wide range of cavity geometries and depths, includes an estimate of uncertainties associated with emulation, and can be used to train new emulators for different source geometries.

JGR Machine Learning and Computation↗

USGS2024

Explosive 2018 eruptions at Kīlauea driven by a collapse-induced stomp-rocket mechanism

Explosive volcanic eruptions produce hazardous atmospheric plumes composed of tephra particles, hot gas and entrained air. Such eruptions are generally driven by magmatic fragmentation or steam expansion. However, an eruption mechanism outside this phreatic–magmatic spectrum was suggested by a sequence of 12 explosive eruptions in May 2018 at Kīlauea, Hawaii, that occurred during the early stages of caldera collapse and produced atmospheric plumes reaching 8 km above the vent. Here we use seismic inversions for reservoir pressure as a source condition for three-dimensional simulations of transient multiphase eruptive plume ascent through a conduit and stratified atmosphere. We compare the simulations with conduit ascent times inferred from seismic and infrasound data, and with plume heights from radar data. We find that the plumes are consistent with eruptions caused by a stomp-rocket mechanism involving the abrupt subsidence of reservoir roof rock that increased pressure in the underlying magma reservoir. In our model, the reservoir was overlain by a pocket of accumulated high-temperature magmatic gas and lithic debris, which were driven through a conduit approximately 600 m long to erupt particles at rates of around 3,000 m 3 s −1 . Our results reveal a distinct collapse-driven type of eruption and provide a framework for integrating diverse geophysical and atmospheric data with simulations to gain a better understanding of unsteady explosive eruptions.

Hawaii↗

USGS2024

Earthquake cycle mechanics during caldera collapse: Simulating the 2018 Kīlauea eruption

In multiple observed caldera-forming eruptions, the rock overlying a draining magma reservoir dropped downward along ring faults in sequences of discrete collapse earthquakes. These sequences are analogous to tectonic earthquake cycles and provide opportunities to examine fault mechanics and collapse eruption dynamics over multiple events. Collapse earthquake cycles have been studied with zero-dimensional slider-block models, but these do not account for the complicated interplay between fluid and elastic dynamics or for factors such as the heterogeneous fault properties and non-vertical ring fault geometries often inferred at volcanoes. We present two-dimensional axisymmetric mafic piston-like collapse earthquake cycle models that include rate-and-state friction, fully-dynamic elasticity, and compressible viscous fluid magma flow. We demonstrate that collapse earthquake intervals and magnitudes are highly sensitive to inertial effects, evolving stress fields, fault geometry, and depth-varying fault friction. Given the consistent earthquake cycles observed in most eruptions, this suggests that ring faults can quickly stabilize and often become nearly vertical at depth. We use the well-monitored 2018 collapse sequence at Kı̄lauea as a case study. Our model can produce many features of Kı̄lauea seismic and geodetic observations, except for a significant amount of interseismic slip, which cannot be readily explained with simple rate-and-state friction parameterizations.

Hawaii↗

USGS2024

Versatile modeling of deformation (VMOD) inversion framework: Application to 20 years of observations at Westdahl Volcano and Fisher Caldera, Alaska, US

We developed an open source, extensible Python-based framework, that we call the Versatile Modeling of Deformation (VMOD), for forward and inverse modeling of crustal deformation sources. VMOD abstracts from specific source model implementations, data types and inversion methods. We implement the most common geodetic source models which can be combined to model and analyze multi-source deformation. VMOD supports Global Navigation Satellite System (GNSS), InSAR, electronic distance measurement, Leveling and tilt data. To infer source characteristics from observations, VMOD implements non-linear least squares and Markov Chain Monte-Carlo Bayesian inversions, including joint inversions using different sources of data. VMOD's structure allows for easy integration of new geodetic models, data types, and inversion strategies. We benchmark the forward models against other published results and the inversion approaches against other implementations. We apply VMOD to analyze deformation at Unimak Island, Alaska, observed with continuous and campaign GNSS, and ascending and descending InSAR time series generated from Sentinel-1 satellite radar acquisitions. These data show an inflation pattern at Westdahl volcano and subsidence at Fisher Caldera. We use VMOD to test a range of source models by jointly inverting the GNSS and InSAR data sets. Our final model simultaneously constrains the parameters of two sources. Our results reveal a depressurizing spheroid under Fisher Caldera ∼4–6 km deep, contracting at a rate of ∼2–3 Mm 3 /yr, and a pressurizing spherical source underneath Westdahl volcano ∼6–8 km deep, inflating at ∼5 Mm 3 /yr. This and past applications of VMOD to volcanic unrest benefit from an extensible framework which supports jointly inversions of data sets for parameters of easily composable multi-source models.

Alaska↗

USGS2024

Pre-existing ground cracks as lava flow pathways at Kīlauea in 2014

In 2014, the Pāhoa lava flow at Kīlauea, on the Island of Hawaiʻi (USA), entered a string of pre-existing meter-width ground cracks in the volcano’s East Rift Zone. The ground cracks transported lava below the surface in a direction discordant to the slope of the landscape. The cracks, which were 100s of meters long and 10s to 100s of meters deep, also widened by up to several meters as they filled, probably in part at the expense of adjacent cracks, which likely closed. Widening of the cracks caused shallow crustal blocks on the volcano’s flank to shift—this deformation was captured by a nearby GPS station and a borehole tiltmeter. The GPS station moved away from the cracks in response, while the tiltmeter showed tilting toward the cracks, consistent with opening. Noting that the lava-filled cracks act as top-fed dikes, we adapt existing theory for the thermo-rheological evolution of dikes to analyze transport of lava captured by ground cracks and propose mechanisms for the exit of the lava back to the surface. This study shows that ground cracks as narrow as 50 cm wide can facilitate the transport of advancing lava flows and can carry lava in directions that differ from those expected based on surface topography, invalidating flow path projections based on the assumption of subaerial flow.

Hawaii↗

USGS2024

Stress-driven recurrence and precursory moment-rate surge in caldera collapse earthquakes

Predicting the recurrence times of earthquakes and understanding the physical processes that immediately precede them are two outstanding problems in seismology. Although geodetic measurements record elastic strain accumulation, most faults have recurrence intervals longer than available measurements. Foreshocks provide the principal observations of processes before mainshocks, but variability between sequences limits generalizations of pre-failure behaviour. Here we analyse seismicity and deformation data for highly characteristic caldera collapse earthquakes from 2018 Kīlauea Volcano (Hawaii, USA), with a mean recurrence interval of 1.4 days. These events provide a unique test of stress-induced earthquake recurrence and document processes preceding mainshocks with magnitude greater than five. We show that recurrence intervals are well predicted by stress histories inferred from near-field deformation measurements and that cycle-averaged seismicity reveals a critical phase, minutes before mainshocks, where earthquakes grew larger and seismic moment rate surged dramatically. The average moment rate in the final 15 minutes (0.7% of the mean cycle duration) was 4.75 times the background, a highly significant change. We infer that as the average stress increased, ruptures were more likely to overcome geometric barriers and grow larger, leading to characteristic, whole-fault ruptures. These findings imply that stress heterogeneity influences both earthquake nucleation and growth, including on potentially hazardous tectonic faults.

Nature Geoscience↗

USGS2023

Understanding the drivers of volcano deformation through geodetic model verification and validation

Volcano geodesy often involves the use of models to explain observed surface deformation. A variety of forward models are used, from analytical point sources to numerical simulations that consider complex magma system geometries, topography, and material properties. Various inversion methods can then be used to relate observed volcano data to models. Ideally, forward models should be verified through intercomparison, to check for implementation errors and quantify the error induced by any approximations used. Additionally, forward models and inversion methods should be validated through tests with synthetic and/or real data, to determine their ability to match data and estimate parameter values within uncertainty. However, to date, there have not been comprehensive verification and validation efforts in volcano geodesy. Here, we report on the first phase of the Drivers of Volcano Deformation (DVD) exercises, which were designed to build community involvement through web-based exercises involving calculations of static elastic displacement around pressurized magma reservoirs. The forward model exercises begin with a spherical reservoir in a homogeneous half space, then introduce topography, heterogeneous elastic properties, and spheroidal geometries. The inversion exercises provide synthetic noisy surface displacement data for a spherical reservoir in a homogeneous half space and assess consistency in estimates of reservoir location and volume/pressure change. There is variability in the results from both forward modeling and inversions, which highlights the strengths and limitations of different forward models, as well as the importance of inversion method choice and uncertainty quantification. This first phase of the DVD exercises serves as a community resource and will facilitate further efforts to develop standards of reproducibility.

Bulletin of Volcanology↗

USGS2023

Coordinating science during an eruption: Lessons from the 2020–2021 Kīlauea volcanic eruption

Data collected during well-observed eruptions can lead to dramatic increases in our understanding of volcanic processes. However, the necessary prioritization of public safety and hazard mitigation during a crisis means that scientific opportunities may be sacrificed. Thus, maximizing the scientific gains from eruptions requires improved planning and coordinating science activities among governmental organizations and academia before and during volcanic eruptions. One tool to facilitate this coordination is a Scientific Advisory Committee (SAC). In the USA, the Community Network for Volcanic Eruption Response (CONVERSE) has been developing and testing this concept during workshops and scenario-based activities. The December 2020 eruption of Kīlauea volcano, Hawaii, provided an opportunity to test and refine this model in real-time and in a real-world setting. We present here the working model of a SAC developed during this eruption. Successes of the Kīlauea SAC (K-SAC) included broadening the pool of scientists involved in eruption response and developing and codifying procedures that may form the basis of operation for future SACs. Challenges encountered by the K-SAC included a process of review and facilitation of research proposals that was too slow to include outside participation in the early parts of the eruption and a decision process that fell on a small number of individuals at the responding volcano observatory. Possible ways to address these challenges include (1) supporting community-building activities between eruptions that make connections among scientists within and outside formal observatories, (2) identifying key science questions and pre-planning science activities, which would facilitate more rapid implementation across a broader scientific group, and (3) continued dialog among observatory scientists, emergency responders, and non-observatory scientists about the role of SACs. The SAC model holds promise to become an integral part of future efforts, leading in the short and longer term to more effective hazard response and greater scientific discovery and understanding.

Hawaii↗

USGS2023

Calibration of imperfect geophysical models by multiple satellite interferograms with measurement bias

Model calibration consists of using experimental or field data to estimate the unknown parameters of a mathematical model. The presence of model discrepancy and measurement bias in the data complicates this task. Satellite interferograms, for instance, are widely used for calibrating geophysical models in geological hazard quantification. In this work, we used satellite interferograms to relate ground deformation observations to the properties of the magma chamber at K i¯lauea Volcano in Hawai‘i. We derived closed-form marginal likelihoods and implemented posterior sampling procedures that simultaneously estimate the model discrepancy of physical models, and the measurement bias from the atmospheric error in satellite interferograms. We found that model calibration by aggregating multiple interferograms and downsampling the pixels in the interferograms can reduce the computation complexity compared to calibration approaches based on multiple data sets. The conditions that lead to no loss of information from data aggregation and downsampling are studied. Simulation illustrates that both discrepancy and measurement bias can be estimated, and real applications demonstrate that modeling both effects helps obtain a reliable estimation of a physical model’s unobserved parameters and enhance its predictive accuracy. We implement the computational tools in the RobustCalibration package available on CRAN.

Technometrics↗

USGS2023

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

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

Hawaii↗

USGS2023

The 2018 eruption of Kīlauea: Insights, puzzles, and opportunities for volcano science

The science of volcanology advances disproportionately during exceptionally large or well-observed eruptions. The 2018 eruption of Kīlauea Volcano (Hawai‘i) was its most impactful in centuries, involving an outpouring of more than one cubic kilometer of basalt, a magnitude 7 flank earthquake, and the volcano’s largest summit collapse since at least the nineteenth century. Eruptive activity was documented in detail, yielding new insights into large caldera-rift eruptions; the geometry of a shallow magma storage-transport system and its interaction with rift zone tectonics; mechanisms of basaltic tephra-producing explosions; caldera collapse mechanics; and the dynamics of fissure eruptions and high-volume lava flows. Insights are broadly applicable to a range of volcanic systems and should reduce risk from future eruptions. Multidisciplinary collaboration will be required to fully leverage the diversity of monitoring data to address many of the most important outstanding questions.

Hawai‘i↗

USGS2022

Incremental caldera collapse at Kīlauea Volcano recorded in ground tilt and high-rate GNSS data, with implications for collapse dynamics and the magma system

Ground deformation during caldera collapse at Kīlauea Volcano in 2018 was recorded in unprecedented detail on a network of real-time GNSS (Global Navigation Satellite System) and tilt instruments. Observations informed hazard assessments during the eruption and now yield insight into collapse dynamics and the magma system. The caldera grew in size over 78 days in a series of repeating, quasi-periodic ∼ "> ∼ ∼ day-long cycles. During abrupt seconds-long collapse events, fault-bounded caldera blocks subsided by meters, while the surrounding edifice moved upwards and outwards by as much as tens of centimeters. Between collapses, stations outside of the caldera moved inwards and downwards at decreasing rates, largely reversing co-collapse deformations. In total, the caldera subsided >500 m at its deepest point while the surrounding edifice subsided mostly less than 2 m chiefly in a region south of the new caldera. Ground deformation reflects magma withdrawal from the broader summit magma system and faulting processes related to collapse. Deformation cycles were caused by step-like pressurization of Kīlauea’s subcaldera magma system due to episodic, stick-slip roof rock subsidence, followed by gradual pressure reduction as magma continued to drain from the summit, stressing faults and leading to subsequent collapses. A model of piston-like subsidence implies that larger collapses increased pressure in a compressible subcaldera magma reservoir by several MPa, driving flow to the rift through a relatively wide conduit. Collapses did not fully recover precollapse pressure loss in the reservoir, and excess pressure driving the eruption was very low; the eruption was thus tenuously sustained by collapses. Important open questions remain about the relation between caldera floor subsidence and ground deformation, the role of other magma storage zones, and the interplay of summit and rift processes in controlling the evolution of the eruption.

Hawaii↗

USGS2022

Could Kı̄lauea's 2020 post caldera-forming eruption have been anticipated?

In 2018 Kīlauea volcano erupted a decade’s worth of basalt, given estimated magma supply rates, triggering caldera collapse. Yet, less than 2.5 years later Kīlauea re-erupted. At the 2018 eruption onset, pressure within the summit reservoir was ~20 MPa above magmastatic. By the onset of collapse this decreased by ~17 MPa. Analysis of magma surges at the 2018 fissures, following collapse events, implies excess pressure at the eruption end of only ~1 MPa. Given the new vent elevation, ∼11 − 12 MPa pressure increase was required to bring magma to the surface in December 2020. Analysis of GPS data between 8/2018 and 12/2020 shows there was a 73% probability that this condition was met at the onset of the 2020 eruption. Given a plausible range of possible vent elevations, there was a 40 to 88% probability of sufficient pressure to bring magma to the surface 100 days before the eruption.

Hawai'i↗