Volcano Seismicity Research Articles

Deep‐Learning‐Based Phase Picking for Volcano‐Tectonic and Long‐Period Earthquakes

AbstractThe application of deep‐learning‐based seismic phase pickers has surged in recent years. However, the efficacy of these models when applied to monitoring volcano seismicity has yet to be fully evaluated. Here, we first compile a data set of seismic waveforms from various volcanoes globally. We then show that the performances of two widely used deep‐learning pickers deteriorate systematically as the earthquakes' frequency content decreases. Therefore, the performances are especially poor for long‐period earthquakes often associated with fluid/magma movement. Subsequently, we train new models which perform significantly better, including when tested on two data sets where no training data were used: volcanic earthquakes along the Cascadia subduction zone and tectonic low‐frequency earthquakes along the Nankai Trough. Our model/workflow can be applied to improve monitoring of volcano seismicity globally while our compiled data set can be used to benchmark future methods for characterizing volcano seismicity, especially long‐period earthquakes which are difficult to monitor.

Volcanic unrest as seen from the magmatic source: Reyðarártindur pluton, Iceland

How the Earth’s crust accommodates magma emplacement influences the signals that can be detected by monitoring volcano seismicity and surface deformation, which are routinely used to forecast volcanic eruptions. However, we lack direct observational links between deformation caused by magma emplacement and monitoring signals. Here we use field mapping and photogrammetry to quantify deformation caused by the emplacement of at least 2.5 km3 of silicic magma in the Reyðarártindur pluton, Southeast Iceland. Our results show that magma emplacement triggered minor and local roof uplift, and that magma reservoir growth was largely aseismic by piecemeal floor subsidence. The occurrence and arrangement of fractures and faults in the reservoir roof can be explained by magmatic overpressure, suggesting that magma influx was not fully accommodated by floor subsidence. The tensile and shear fracturing would have caused detectable seismicity. Overpressure eventually culminated in eruption, as evidenced by exposed conduits that are associated with pronounced local subsidence of the roof rocks, corresponding to the formation of an asymmetric graben at the volcano surface. Hence, the field observations highlight processes that may take place within silicic volcanoes, not accounted for in widely used models to interpret volcanic unrest.

Bridging Supervised and Unsupervised Learning to Build Volcano Seismicity Classifiers at Kilauea Volcano, Hawaii

Abstract Real-time classification of volcano seismicity could become a useful component in volcanic monitoring. Supervised learning provides a powerful means to achieve this but often requires a large amount of manually labeled data. Here, we build supervised learning models to discriminate volcano tectonic events (VTs), long-period events (LPs), and hybrid events in Kilauea by training with pseudolabels from unsupervised clustering. We test three different supervised models, and all of them achieve >93% accuracy. We apply the model ensemble to the six-day seismicity during the eruption in 2018 and show that they were mainly VTs (62%), in comparison with the dominance of LPs prior to the eruption (68%). The success of our method is aided by the accuracy of the majority of pseudolabels and the consistency of the three models’ performance. Using Shapley additive explanations, we show that the frequency contents at 1–4 Hz are the most important to differentiate volcano seismicity types. This work, together with our previous clustering analysis, provides an example of bridging unsupervised and supervised learning to construct potential real-time seismic classifiers from scratch.

A long time of rest at Llaima volcano following the 2010 MW 8.8 Maule earthquake, Chile

Llaima Volcano (38.692°S, 71.729°W) is one of the most active centers in South America, with ∼56 eruptions since 1852 CE. During the last eruptive cycle (2007–2009), the volcano displayed typical features of open conduit systems, with frequent long-period volcanic seismicity (LP). After the MW 8.8 Maule megathrust earthquake (27 February 2010), Llaima volcano located 305 km to SE of the epicenter, has experienced one of the longest periods of quiescence since 1852, with reduced activity observed since the beginning of its continuous instrumental monitoring in the year 2007. To better understand this behavior, we track the repose time between eruptions in the 20th century to depict the current period of quiescence and provide details of the pattern of volcano seismicity after the earthquake. The number of LP events decreased ∼90% shortly after the earthquake. In turn, crustal faults nearby (∼20 km south of the main crater) showed an increased level of seismicity lasting for approximately six months after the mainshock before returning to background levels. In order to propose driving mechanisms for such behavior, we computed the static changes of the stress tensor on chosen receiver structures. The acting receiver structure differs from those related to the long-term stress regime controlled by regional tectonics and was inferred from the pre-earthquake shallow seismicity (2007–2009). LP and volcanotectonic seismicity (VT) before the earthquake were located in a NW-SE pattern, in contrast to the NE-trending alignment of flank vents (and hence dike swarms) usually interpreted as an indicator of the maximum horizontal stress axis. From the static stress transfer imparted by the earthquake, we find that an opening event would have occurred at a NW-trending receiver structure coupled with closing of other potential structures, which promoted magma storage along this blind structure but precluding further propagation to the surface. Our results thus show a rare example of earthquake-induced suppression of volcanic activity, where the geometry of the fault-fracture network that shape the plumbing system likely plays a major role.

A Specific Earthquake Processing Workflow for Studying Long‐Lived, Explosive Volcanic Eruptions With Application to the 2008 Okmok Volcano, Alaska, Eruption

AbstractBy 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.

Editorial: Volcanism in the Central Volcanic Zone of the Andes

This Research Topic was born out of the 1 st Symposium on the Central Volcanic Zone of the Andes 21 (CVZA; carried out in virtual mode during 2021) which counted for the presence of geoscientists 22 working in the volcanological, petrological and geochemical fields, among the others. Our objective 23 was to bring together contributions focused on the evolution and impact of volcanism in the CVZA 24 from its formation (Late Oligocene) up to present times, including specific and multidisciplinary 25 studies, with both detailed and regional approaches. The nine articles published in this Research Topic 26 cover a spectrum of topics applied in different areas of the CVZA, including poly-and monogenetic 27 volcanism, the influence of the crust in magmatic processes, fluid geochemistry, volcano seismicity, 28 petrology, geochronology, hazards assessment, relationships between volcanoes and 29 archeology/anthropology/sociology. 30 Barry et al. (2022) compare the degassing processes between the Central and Southern volcanic zones 31 of the Andes using He and carbon isotopes, and CO2/ 3 He ratios obtained from gas and water samples 32 related to fumaroles and thermal springs. They conclude that crustal thickness exerts a primary control 33 on the extent of fluid-crust interaction, as helium and other volatiles rise through the upper plate in the 34 Andean convergent margin, with more significant crustal inputs in the CVZA due its thicker crust. 35

Performance of a Rotational Sensor to Decipher Volcano Seismic Signals on Etna, Italy

AbstractVolcano‐seismic signals such as long‐period events and tremor are important indicators for volcanic activity and unrest. However, their wavefield is complex and characterization and location using traditional seismological instrumentation is often difficult. In 2019 we recorded the full seismic wavefield using a newly developed 3C rotational sensor co‐located with a 3C traditional seismometer on Etna, Italy. We compare the performance of the rotational sensor, the seismometer and the Istituto Nazionale di Geofisica e Vulcanologia‐Osservatorio Etneo (INGV‐OE) seismic network with respect to the analysis of complex volcano‐seismic signals. We create event catalogs for volcano‐tectonic (VT) and long‐period (LP) events combining a STA/LTA algorithm and cross‐correlations. The event detection based on the rotational sensor is as reliable as the seismometer‐based detection. The LP events are dominated by SH‐type waves. Derived SH phase velocities range from 500 to 1,000 m/s for LP events and 300–400 m/s for volcanic tremor. SH‐waves compose the tremor during weak volcanic activity and SH‐ and SV‐waves during sustained strombolian activity. We derive back azimuths using (a) horizontal rotational components and (b) vertical rotation rate and transverse acceleration. The estimated back azimuths are consistent with the INGV‐OE event location for (a) VT events with an epicentral distance larger than 3 km and some closer events, (b) LP events and tremor in the main crater area. Measuring the full wavefield we can reliably analyze the back azimuths, phase velocities and wavefield composition for VT, LP events and tremor in regions that are difficult to access such as volcanoes.

Development of Low-Cost IoT based data acquisition system for real-time monitoring of volcanoes seismicity

This paper describes how to develop a low-cost data acquisition (DAQ) system for real-time remote monitoring of volcano seismicity. The DAQ system is developed based on IoT platform, consists of several module devices both hardware and software, and installed in two places called seismic-stations (SS) and observation-post (OP). In the implementation, the SS is placed on the body of the volcano meanwhile the OP is usually placed at a certain distance from the SS with more adequate electrical and communication facilities. In this design, the SS consists of seismic sensors and a digitizer, whereas the OP consists of a computer server and ADSL modem. Communication between SS and OP conducted by using a 5 GHz Wi-Fi telemetry system. At the OP, the data is processed to produce the form of digital seismograms, hypocentres, arrival times, and signal magnitudes in SAC or mini seed file formats by Earthworm software. Database server for cloud access is created using Winston Wave Server (WWS) and MySQL Data Base Management Systems (DBMS). The seismic data can be accessed in real time remotely by users using the SWARM application via website access.

Deformation-controlled long-period seismicity in low-cohesion volcanic sediments

Volcano seismicity is an important tool in monitoring and forecasting activity at volcanoes globally. Volcanic earthquakes show diverse spectral characteristics, of which shallow long-period (low-frequency) seismicity and long-duration tremor are generally interpreted as indicators of fluid migration, and as potential eruption precursors. Here we show that a common low-cohesion volcanic sediment from the Campi Flegrei caldera (Italy) produces low-frequency and long-duration seismicity as it undergoes deformation in dry conditions. We employed acoustic-emission rock-deformation experiments at a range of strain rates to produce events that, when normalized for scale, were spectrally indistinguishable from the long-period and tremor seismicity observed in natural volcanic settings. The generation of these signals was enhanced at lower laboratory strain rates. Correlated X-ray tomography of the samples before and after deformation constrained the source as distributed damage. Long-period seismicity at volcanoes may be generated by deformation of weak material in the subsurface, not solely by fluid movement, according to rock deformation experiments.

Magma pressure discharge induces very long period seismicity

Volcano seismicity is one of the key parameters to understand magma dynamics of erupting volcanoes. However, the physical process at the origin of the resulting complex and broadband seismic signals remains unclear. Syn-eruptive very long period (VLP) seismic signals have been explained in terms of the sudden expansion of gas pockets rising in the liquid melt. Their origin is linked to a magma dynamics which triggers the explosive process occurring before the explosive onset. We provide evidence based on acoustic, thermal, and ground deformation data to demonstrate that VLP signals at Stromboli are generated at the top of the magma column mainly after the explosion onset. We show that VLP amplitude and duration scale with the eruptive flux which induces a decompression of 103–104 Pa involving the uppermost ~ 250 m of the feeding conduit. The seismic VLP source represents the final stage of a ~ 200 s long charge and discharge mechanism the magma column has to release excess gas accumulated at the base of a denser and degassed magma mush. The position of the VLP seismic source coincides with the centroid of the shallow mush plug and tracks elevation changes of the magma free surface.

Pressure-Driven Opening and Filling of a Volcanic Hydrofracture Recorded by Tuffisite at Húsafell, Iceland: A Potential Seismic Source

The opening of magmatic hydraulic fractures is an integral part of magma ascent, the triggering of volcano seismicity, and defusing the explosivity of ongoing eruptions via outgassing magmatic volatiles. If filled with pyroclastic particles, these fractures can be recorded as tuffisites. Tuffisites are therefore thought to play a key role in both initiating eruptions and controlling their dynamics, and yet their genesis remains poorly understood. Here we characterise the processes, pressures and timescales involved in tuffisite evolution within the country rock through analysis of the sedimentary facies and structures of a large sub-horizontal tuffisite vein, 0.9 m thick and minimum 40 m in length, at the dissected Húsafell volcano, western Iceland. The vein occurs where a propagating rhyolitic sheet intrusion stalled at a depth of ∼500 m beneath a relatively strong layer of welded ignimbrite. Laminations, cross-stratification, channels, and internal injections indicate erosion and deposition in multiple fluid pulses, controlled by fluctuations in local fluid pressure and changes in fluid-particle concentration. The field evidence suggests that this tuffisite was emplaced by as many as twenty pulses, depositing sedimentary units with varying characteristics. Assuming that each sedimentary unit (∼0.1 m thick and minimum 40 m in length) is emplaced by a single fluid pulse, we estimate fluid overpressures of ∼1.9–3.3 MPa would be required to emplace each unit. The Húsafell tuffisite records the repeated injection of an ash-laden fluid within an extensive subhorizontal fracture, and may therefore represent the fossil record of a low-frequency seismic swarm associated with fracture propagation and reactivation. The particles within the tuffisite cool and compact through time, causing the rheology of the tuffisite fill to evolve and influencing the nature of the structures being formed as new material is injected during subsequent fluid pulses. As this new material is emplaced, the deformation style of the surrounding tuffisite is strongly dependent on its evolving rheology, which will also control the evolution of pressure and the system permeability. Interpreting tuffisites as the fossil record of fluid-driven hydrofracture opening and evolution can place new constraints on the cycles of pressurisation and outgassing that accompany the opening of magmatic pathways, key to improving interpretations of volcanic unrest and hazard forecasting.

Wavelet‐Based Characterization of Very‐Long‐Period Seismicity Reveals Temporal Evolution of Shallow Magma System Over the 2008–2018 Eruption of Kīlauea Volcano

AbstractVery‐long‐period (VLP) volcano seismicity often encodes subsurface magma movement, and thus provides insight into subsurface magma transport processes. We develop a fully automated signal processing workflow using wavelet transforms to detect and assess period, decay rate, and ground motions of resonant VLP signals. We then generate a VLP catalog over the 2008–2018 open‐vent summit eruption of Kīlauea Volcano containing thousands of events. Two types of magma resonance dominate our catalog: vertical sloshing of the open magma column in and out of the shallow magma reservoir, and lateral sloshing of magma in the lava lake. These events were triggered mainly from the surface and less commonly from depth. The VLP catalog is then combined with other geophysical datasets to characterize evolution of the shallow magma system. VLP ground motion patterns show both abrupt and gradual changes in shallow magma reservoir geometry. Variation in resonant periods and decay rates of both resonance types occurred on timescales from hours to years, indicating variation in magma density and viscosity that likely reflect unsteady shallow outgassing and convection. A lack of correlation between decay rates of the two dominant resonant modes suggests a decoupling between magma in the conduit and lava lake. Known intrusions and rift zone eruptions often represented change points for resonance characteristics and their relations with other datasets. This data synthesis over a 10‐year eruptive episode at Klauea Volcano demonstrates how VLP seismicity can sharpen insights into magma system evolution for use in monitoring and understanding eruptive processes.

Hydrothermal pressure-temperature control on CO2 emissions and seismicity at Campi Flegrei (Italy)

Fluids supplied by stored magma at depth are causal factors of volcanic unrest, as they can cause pressurization/heating of hydrothermal systems. However, evidence for links between hydrothermal pressurization, CO2 emission and volcano seismicity have remained elusive. Here, we use recent (2010−2020) observations at Campi Flegrei caldera (CFc) to show hydrothermal pressure, gas emission and seismicity at CFc share common source areas and well-matching temporal evolutions. We interpret the recent escalation in seismicity and surface gas emissions as caused by pressure-temperature increase at the top of a vertically elongated (0.3–2 km deep) gas front. Using mass (steam) balance considerations, we show hydrothermal pressurization is causing energy transfer from the fluids to the host rocks, ultimately triggering low magnitude earthquakes within a seismogenetic volume containing the hydrothermal system. This mechanism is probably common to other worldwide calderas in similar hydrothermal activity state.

Thermo‐Hydro‐Mechanical Model and Caprock Deformation Explain the Onset of an Ongoing Seismo‐Volcanic Unrest

AbstractModeling seismicity at volcanoes remains challenging as the processes that control seismic energy release due to fluid transport, heat flow, and rock deformation are firmly coupled in complex geological media. Here, we couple fluid‐flow and mechanical (deformation) simulators (TOUGHREACT–FLAC3D) to reproduce fluid‐induced seismicity at Campi Flegrei caldera (southern Italy) in isothermal (HM) and nonisothermal (THM) conditions. The unique ability of the Campi Flegrei caprock to withstand stress induced by hot‐water injections is included in the model parametrization. After pore pressure accumulation is guided by a combination of thermal and hydromechanical interactions, contrasting compressive and extensional forces act on the basal and top parts of the caprock, respectively. Then, pressure perturbation and caprock deformation induce fractures that allow hot fluids uprising to pressurize the overlying fault, driving it toward failure and triggering seismicity. Under similar mechanical boundary conditions, the induced thermal effects prompt seismic slip earlier but with higher seismic magnitudes when (1) thermal equilibrium is preserved and (2) the thermal contrast is enhanced due to increased fluid injection temperatures. The results indicate that numerical models of volcano seismicity must consider the influence of rock‐sealing formations to obtain more robust, accurate, and realistic seismic predictions at volcanoes. The proposed models satisfactorily reproduce the magnitude–depth distribution of the swarm (October 5, 2019), preceding the two strongest earthquakes recorded in 35 years at the caldera (3.1 and 3.3—on December 6, 2019, and April 26, 2020, respectively) using hot‐water injection from depth.

Machine-learning-based detection of volcano seismicity using the spatial pattern of amplitudes

SUMMARYWe propose a new algorithm, focusing on spatial amplitude patterns, to automatically detect volcano seismic events from continuous waveforms. Candidate seismic events are detected based on signal-to-noise ratios. The algorithm then utilizes supervised machine learning to classify the existing candidate events into true and false categories. The input learning data are the ratios of the number of time samples with amplitudes greater than the background noise level at 1 s intervals (large amplitude ratios) given at every station site, and a manual classification table in which ‘true’ or ‘false’ flags are assigned to candidate events. A two-step approach is implemented in our procedure. First, using the large amplitude ratios at all stations, a neural network model representing a continuous spatial distribution of large amplitude probabilities is investigated at 1 s intervals. Second, several features are extracted from these spatial distributions, and a relation between the features and classification to true and false events is learned by a support vector machine. This two-step approach is essential to account for temporal loss of data, or station installation, movement, or removal. We evaluated the algorithm using data from Mt. Ontake, Japan, during the first ten days of a dense observation trial in the summit region (2017 November 1–10). Results showed a classification accuracy of more than 97 per cent.

ESeismic: Towards an Ecuadorian volcano seismic repository

In this work, we present the development, description, and performance evaluation of two volcano seismological datasets: one containing raw seismic signals (MicSigV1) and another containing a set of 84 distinctive attributes or features for bench-marking (SeisBenchV1), which was extracted from the MicSigV1 dataset, recorded at Cotopaxi volcano in Ecuador. These datasets represent the first public Ecuadorian volcano seismicity repository that will update, publish, and support the release of data to the scientific community on a continual basis. Each dataset contains samples of mostly long-period and volcano-tectonic event classes. Other types of events, such as regional, hybrid, and icequakes, were also included despite their minor presence in the datasets. These datasets will serve to provide an important resource for research in signal processing, feature calculation, feature selection, feature space reduction, seismic events detection, and classification areas. The datasets can be downloaded through a copyright agreement that supports the correct use of the released datasets.

Improved 1D velocity model and deep long-period earthquakes in Kanlaon Volcano, Philippines: Implications for its magmatic system

We determined an improved 1D P-wave velocity model for Kanlaon Volcano, one of the most active volcanoes in the Philippines, for use in precise earthquake relocation. The inversion for velocity model was conducted using the program VELEST, which solved the coupled hypocenter–velocity problem from dataset of 251 local earthquakes recorded by the Kanlaon Volcano Seismic Network from 2014 to 2017. Using the improved velocity model, we relocated the volcano-tectonic (VT) earthquakes in Kanlaon with hypoDD, resulting in better-constrained hypocenter locations based on the decrease in RMS and corresponding error assessment tests. Subsequently, we relocated swarms of long-period volcanic earthquakes that occurred from September 1, 2017 to August 31, 2018 and prompted the Philippine Institute of Volcanology and Seismology to raise the volcano's alert level. These long-period earthquakes had emergent arrivals, hence we applied a cross-correlation procedure using REDPy to obtain an improved arrival time picks. These improved hypocenters revealed the existence of deep long-period (DLP) earthquakes that occurred at depths of 23 to 36 km beneath the volcano. These are the first DLP events documented in Kanlaon since the establishment of the Kanlaon Volcano Observatory in 1961. They were found to belong into families based on similarity of waveforms suggesting they were produced by repetitive, non-destructive sources. The occurrences of DLPs in Kanlaon reflected the pressurization of its deep-seated magmatic system. Their identification is critical in assessing the current state of Kanlaon Volcano especially since its last magmatic eruption was in 1902.

The relation between viscosity and acoustic emissions as a laboratory analogue for volcano seismicity

The relation between viscosity and acoustic emissions as a laboratory analogue for volcano seismicity

Using a process-based model of pre-eruptive seismic patterns to forecast evolving eruptive styles at Sinabung Volcano, Indonesia

Most volcanoes worldwide are not monitored in real-time; for those that are, patterns of pre-eruptive earthquakes coupled with conceptual models of magma ascent enable short-term forecasting of eruption onset. Basic event locations, characterization of background seismicity, and recognition of changes in earthquake types and energy release are most important to successful eruption forecasting. During renewed activity at Sinabung volcano, Indonesia, this approach was used by the Center for Volcanology and Geological Hazards Mitigation (CVGHM) and the USGS Volcano Disaster Assistance Program to forecast eruption onset, identify changes in eruptive styles and raise or lower alert levels and extend or contract evacuation zones. After >400years of quiescence, Sinabung began erupting in August 2010. The volcano was unmonitored at the onset of these eruptions, which were phreatic, but soon after a monitoring network was installed by CVGHM. Increasing swarms of high-frequency volcano tectonic (VT) earthquakes were used to forecast continuing phreatic eruptions. Volcanic activity decreased in mid-September 2010, while additional intrusions at depth (inferred from continued distal VT swarms) continued through September 2013, when explosive phreatic eruptions recurred. Explosive eruptions were forecast based on increases in real-time seismic amplitude measurement (RSAM) and VT seismicity. Seismicity changed markedly in late November and early December 2013 with the occurrence of deep earthquakes and an overall transition from low-frequency (LF) dominated and irregular (in time and magnitude) earthquakes to more regular LF and hybrid seismicity – a transition that accompanied the continued rise, eventual emergence and growth of a lava dome in the summit crater. This lava dome was first observed on 18 December. In late December 2013 to early January 2014, the eruptive style changed again as additional ascending magma deformed the summit and the dome grew beyond the capacity of the summit crater, resulting in the en masse collapse of the lava dome (2Mm3) on 11 January and the largest pyroclastic flow to date. The collapse was forecast on the basis of a several order of magnitude increase in RSAM, continued strong distal VT seismicity, an increase in proximal seismicity, and large-scale observed deformation of the summit area. Similarly, a later collapse of a second summit lava dome on 1 February 2014 was forecast on the basis of increased distal seismicity. Here, we demonstrate how a process-based volcano seismicity model was used in combination with real-time data to forecast the time and magnitude of eruptions, as well as changes in eruption style.

Analysis of Seismic‐Driven Instability of Mt. Nuovo in the Ischia Island, Italy

Abstract Ischia is a volcanic island located at the margin of the Gulf of Naples in the southeastern Tyrrhenian Sea, Italy. In this article, we investigate the stability of the deep‐seated block known as Mt. Nuovo in the northwestern flank of the Mt. Epomeo volcano under a combined gravitational and seismic load. Mt. Nuovo is bounded by a number of active seismic faults and is placed close to the epicentral area of the 1883 Casamicciola earthquake (I MCS =X, M w 5.8), that is, the largest shock occurring in the island in historical times. We explore the hypothesis that a Casamicciola‐like earthquake might occur with an epicenter in the Mt. Nuovo block area. Using relationships between maximum seismic intensity and peak ground acceleration found in the literature as well as a new relationship derived specifically for this study, we apply the minimum lithostatic deviation method, which is a variant of the limit‐equilibrium method, to assess the stability of the block. The main finding is that Mt. Nuovo would be mobilized if such an earthquake took place on a fault very close to the block, which would give rise to a catastrophic mass failure and to a disastrous tsunami. This calls for the need of continuous monitoring of the volcano seismicity and deformation pattern.