Journal of Volcanology and Geothermal Research最新文献

A Mw = 6.4 earthquake, possibly related to submarine volcanism, occurred on October 23, 2006, in the vicinity of Monday Seamount, located in the Izu-Bonin-Mariana arc. Despite the size of the earthquake, the precise location of aftershocks is intractable with the routine data processing because of poor station coverage and complex waveforms traversing the oceanic lithosphere. This study overcomes these problems by using the Broadband Ocean Bottom Seismometer (BBOBS) array data with better azimuthal coverage and by developing new methods to detect the P-wave arrival time to locate the aftershocks precisely. Most of the relocated epicenters are located in the rift zone next to the edifice of the seamount. We interpret this pattern as the seismic activity induced by stress perturbation due to the dike intrusion.

The forecast of pulsatory explosions during volcanic unrest periods is an essential issue for the assessment and mitigation of volcanic hazards. Although various precursors are detectable through geophysical and geochemical monitoring, difficulties remain in precisely constraining possible scenarios. A probabilistic approach is effective in assessing risk while considering various uncertainties. Sakurajima volcano characterized by frequent Vulcanian activity is one of the suitable fields for the probabilistic forecast of pulsatory explosions. Their inflation-deflation patterns of ground deformation related to Vulcanian explosions are useful for evaluating the imminence and size of the next event. The large database obtained from its vigorous activity can contribute to statistical analysis. In this study, aiming the probabilistic forecast of the timing and size of explosions, we investigated the duration of inflation and volume changes at the pressure source using strain records of over 5000 events of Sakurajima volcano. Then, a stochastic model was estimated to explain the distribution of these events. The log-logistic distribution was found to be an appropriate model for data distribution, indicating the presence of competing processes, such as pressurization and depressurization, in the conduit. The model parameters of the log-logistic distribution temporally fluctuated reflecting the volcanic activity, especially increasing the magma supply from a deep region. We also suggested a methodology to constrain the probabilities of the likely timing and size of an imminent explosion using real-time strain monitoring and an estimated model distribution. Although some improvements would be needed for practical forecasting, our approach could be useful in predicting possible ash hazards.

The frequency content of volcanogenic seismicity is often used to classify events and their spatial and temporal progression is then used to map subsurface volcanic processes. The progression of volcano-seismic events and associated source processes also plays a critical role in eruption forecasting. Here we develop and evaluate a computerized methodology for characterizing volcano-seismic event types using Frequency Index and Average Peak Frequency. We apply and test this technique at Great Sitkin Volcano, Alaska, classifying over 9000 hypocenters between 1999 and 2023. This 24-year time span covers periods of seismic quiescence, earthquake activity on nearby tectonic (bookshelf) faults, precursory unrest from 2016 to 2021, and the explosive onset in May 2021 of the ongoing effusive eruption. We use the spatial and temporal evolution of classified event types to map the active volcanic and tectonic processes, develop a conceptual model of the subsurface magmatic system, and perform a retrospective analysis of eruption forecasts at Great Sitkin Volcano between 2016 and the present. The classification and progression of hypocenters suggests the subsurface Great Sitkin Volcano magmatic system consists of a mid- to lower- crustal source zone between 10 and 40 km depth and an upper crustal magma storage area between −1 and 10 km depth (hypocenter depth is referenced to sea level and negative depths reflect height above sea level). The earliest precursors occurred in July 2016 and consisted of deep long-period and volcano-tectonic earthquakes at mid-crustal depths suggesting the subsequent unrest and eruption were triggered by a deeper intrusion of magma. This mid-crustal seismic activity was immediately followed by the onset upper-crustal long-period events and volcano-tectonic earthquakes VTs suggesting a strong linkage between the shallow and deeper portions of the magmatic system. The upper crustal area was likely capped by the 1974 lava dome until the magmatic explosion on May 26, 2021.

We analyse the vertical gravity gradient (VGG) properties at calderas using the Campi Flegrei (CF) site in Italy. In situ observed VGG values can depart significantly from the theoretical (normal) value of −308.6 μGal/m, particularly in areas of rugged relief. It is assumed that in sufficiently flat areas, the effect of geology, i.e., of the subsurface density heterogeneities, on VGG could prevail over the effect of terrain (topography), which can subsequently be neglected. With respect to the CF caldera, which is often considered as ‘reasonably flat area’, according to our findings the effect of topography on VGG is usually underestimated, while the effect of deeper geology is overestimated. We model the effect of the near topography on VGG at CF and subsequently verify the results of modelling by in situ observations to support our predictions. The results show that, in terms of VGG, the topographic relief plays a more significant role than the assumed geological sources even at ‘flat’ calderas such as CF. For a better understanding, in addition to CF, we analyse the effect of deeper geological sources on VGG also in the territory of Slovakia using a detailed gravimetric database of Slovakia. As a result, we question the use of in situ observed VGG values when processing and interpreting observed time-lapse gravity changes in volcanic areas accompanied by surface deformation.

Distinguishing volcanic debris avalanche deposits from other epiclastic breccia could be complex. For more than 60 years, the Tinguiririca deposit (sourced from the homonymous volcano) in the Andes of Central Chile has been described by different authors as glacial moraines, a lahar, a volcanic debris avalanche, and even a debris flow deposit. To decipher its obscured origin and emplacement dynamics, we have carried out a detailed investigation of its distribution, contact relationships, sedimentology, and facies. Our findings unravel that the 57 km-long deposit is 5 to 300 m thick, totalling a reconstructed volume of 3.64 ± 0.05 km³. It is composed of unsorted heterometric breccias formed by clasts and blocks arranged in mixed and matrix facies characterised by distinctive lithological domains. In general, three clasts lithologies are dominant, consisting of black and grey andesites and hydrothermally altered clasts with jigsaw cracks and fractures. The deposit overlies terraced colluvium along the valleys and forms hummocks and ridges. Emplacement velocities estimates range from 39.6 m/s to 108.4 m/s. Therefore, the Tinguiririca deposit should represent a massive volcanic debris avalanche that formed after a lateral collapse that affected the ancient Tinguiririca Volcanic Complex, during the Late Pleistocene (between 45 ± 18 and c. 19.2 ± 1.2 ka). The abundance of hydrothermal minerals within the deposit's matrix and clasts (i.e., illite, phengite, epidote, tridymite, chlorite, hematite, jarosite, and alunite) all represent the volcano's hydrothermal system that likely favoured rock weakness and edifice collapse. Finally, the new interpretation is valuable for evaluating volcanic hazards and requires further mapping and research efforts.

The mass eruption rate (MER) of an explosive volcanic eruption is a commonly used quantifier of the magnitude of the eruption, and estimating it is important in managing volcanic hazards. The physical connection between the MER and the rise height of the eruption column results in a scaling relationship between these quantities, allowing one to be inferred from the other. Eruption source parameter datasets have been used to calibrate the relationship, but the uncertainties in the measurements used in the calibration are typically not accounted for in applications. This can lead to substantial over- or under-estimation. Here we apply a simple Bayesian approach to incorporate uncertainty into the calibration of the scaling relationship using Bayesian linear regression to determine probability density functions for model parameters. This allows probabilistic prediction of mass eruption rate given a plume height observation in a way that is consistent with the data used for calibration. By using non-informative priors, the posterior predictive distribution can be determined analytically. The methods and datasets are collected in a python package, called merph. We illustrate their use in sampling plausible MER—plume height pairs, and in identifying usual eruptions. We discuss applications to ensemble-based hazard assessments and potential developments of the approach.

Typical eruptions at Kīlauea volcano involve the evacuation of magma from the summit and/or south caldera reservoirs towards the East or Southwest rift zones. The reservoir drainage provokes the summit deflation, and on extreme occasions, such as in 2018, the summit caldera collapse. Systematically, seismic tremor, often with a particular multichromatic spectral signature characterized by frequency gliding, accompanies summit deflation episodes. In 2018, this type of continuous tremor accompanied the steady subsidence stage, whereas discrete earthquakes dominated the collapse stage. In this work, we aim to understand the source mechanism of the syn-deflation tremor of 2018. To locate the seismic source, we develop a novel machine-learning-based algorithm as an alternative to the amplitude source location technique. We use a large high-resolution catalog to resolve a composite amplitude decay function. Under these conditions, our method outperforms the traditional technique. We locate the tremor source 1 km below the eastern perimeter of the Halema‘uma‘u crater, which coincides with the position of the summit magma reservoir, as determined in many other studies. Furthermore, we model the seismic source as pressure oscillations driven by gas porous flow at the roof of the reservoir. In this model, gas accumulates temporarily in many gas pockets between the magma and the roof. Our modeling shows that the gas flux is responsible for the tremor amplitude modulations, whereas the gas pocket thickness controls the frequency variations. Beyond a critical point of depressurization, the magma cannot contribute further to the tremor oscillations via decompression-driven degassing, nor support the roof above it, resulting in rock failure. This work advances our understanding of magma-degassing dynamics and tremor generation at Kilauea volcano, and provides novel seismological techniques for volcano seismology monitoring and research.