No abstract

In this work we assess how volcano geodetic observations can be used to gain insights into hydrothermal system dynamics. We designed a range of numerical models of hydrothermal unrest and associated ground deformation caused by the thermo–poro–elastic response of the substratum. Throughout an episode of unrest, ground deformation is consistently first controlled by the poroelastic response of the substratum to pore pressure increase near the injection area. Later, thermal expansion may become the dominant process if the injection is sustained. We inverted these synthetic geodetic data using simple conventional pressure source models and compared the retrieved source characteristics with that of the synthetic hydrothermal systems. Simple pressure source models can reproduce well ground deformation caused by pore–pressure increase at depth. Most importantly, the pressure source's depth retrieved from the inversions corresponds to those of the area of injection of the hot magmatic fluids into the hydrothermal system. When the thermoelastic contribution to ground deformation becomes significant through time, simple point or spherical finite sources cannot reproduce the ground deformation signal. This allows one to determine whether observed ground deformation events due to hydrothermal unrest are distinct episodes of unrest and injection at depth, or whether one may correspond to the late, thermally‐controlled phase of a previous event. Finally we applied this strategy to White Island volcano, New Zealand, to gain insights into the processes driving the last two episodes of ground uplift.

[1] Vulcanian explosions with plumes to 12 km occurred at Soufrière Hills volcano (SHV) between July 2008 and January 2009. We report strainmeter and barometric data, featuring quasi-linear strain changes that correlate with explosive evacuation of the conduit at rates of ∼0.9−2 × 10 7 kg s −1 . July and January explosion-generated strains were similar, ∼20 nanostrain at ∼5 km, and interpreted as contractions of a quasi-cylindrical conduit, with release of magmastatic pressure, and exsolution-generated overpressure of order 10 MPa. The 3 December 2008 event was distinctive with larger signals (∼140-200 nanostrain at 5-6 km) indicating that a rapid pressurization preceded and triggered the explosion. Modeling suggests a dike with ENE trend, implying that feeder dikes at SHV had diverse attitudes at different times during the eruption. All explosions were associated with acoustic pulses and remarkable atmospheric gravity waves. Citation: Chardot, L., et al. (2010), Explosion dynamics from strainmeter and microbarometer observations,

International audienceDuring the Soufrière Hills eruption, vulcanian explosions have generally occurred 1) in episodic cycles; 2) isolated during pauses in extrusion, and 3) after major collapses of the dome. In a different eruptive context, significant vulcanian explosions occurred on 29 July 2008, 3 December 2008, and 3 January 2009. Deposits are pumiceous except for the 3 December event. We reconstructed the dispersal pattern of the deposits and their textural characteristics to evaluate erupted volume and vesicularity of the magma at fragmentation. We discuss the implications of these explosions in terms of eruptive processes and chronology, and the hazards posed by their sudden and often unheralded occurrence. We suggest that overpressurization of the conduit can develop over time-scales of months to weeks by a process of self-sealing of conduit walls and/or the cooling dome by silica polymorphs. This work provides new insights for understanding the generation of hazardous vulcanian explosions at andesitic volcanoes

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[1] Five Vulcanian explosions were triggered by collapse of the Soufrière Hills Volcano lava dome in 2003. We report strainmeter data for three explosions, characterized by four stages: a short transition between the onset of disturbance and a pronounced change in strain; a quasi-linear ramp accounting for the majority of strain change; a more gradual continued decline of strain to a minimum value; and a strain recovery phase lasting hours. Remarkable ∼800 s barometric gravity waves propagated at ∼30 m s −1 . Eruption volumes estimated from plume height and strain data are 0.32-0.42 × 10 6 , 0.26-0.49 × 10 6 , and 0.81-0.84 × 10 6 m 3 , for Explosions 3-5 respectively, consistent with quasi-cylindrical conduit drawdown <2 km. The duration of vigorous explosion is given by the strain signature, indicating mass fluxes of order 10 7 kg s −1 . Conduit pressures released reflect static weight of porous gas-charged magma, and exsolution-generated overpressures of order 10 MPa.

Bayesian Networks (BNs) are probabilistic graphical models that provide a robust and flexible framework for understanding complex systems. Limited case studies have demonstrated the potential of BNs in modeling multiple data streams for eruption forecasting and volcanic hazard assessment. Nevertheless, BNs are not widely employed in volcano observatories. Motivated by their need to determine eruption-related fieldwork risks, we have worked closely with the New Zealand volcano monitoring team to appraise BNs for eruption forecasting with the purpose, at this stage, of assessing the utility of the concept rather than develop a full operational framework. We adapted a previously published BN for a pilot study to forecast volcanic eruption on Whakaari/White Island. Developing the model structure provided a useful framework for the members of the volcano monitoring team to share their knowledge and interpretation of the volcanic system. We aimed to capture the conceptual understanding of the volcanic processes and represent all observables that are regularly monitored. The pilot model has a total of 30 variables, four of them describing the volcanic processes that can lead to three different types of eruptions: phreatic, magmatic explosive and magmatic effusive. The remaining 23 variables are grouped into observations related to seismicity, fluid geochemistry and surface manifestations. To estimate the model parameters, we held a workshop with 11 experts, including two from outside the monitoring team. To reduce the number of conditional probabilities that the experts needed to estimate, each variable is described by only two states. However, experts were concerned about this limitation, in particular for continuous data. Therefore, they were reluctant to define thresholds to distinguish between states. We conclude that volcano monitoring requires BN modeling techniques that can accommodate continuous variables. More work is required to link unobservable (latent) processes with observables and with eruptive patterns, and to model dynamic processes. A provisional application of the pilot model revealed several Christophersen et al. Bayesian Networks in Volcano Monitoring key insights. Refining the BN modeling techniques will help advance understanding of volcanoes and improve capabilities for forecasting volcanic eruptions. We consider that BNs will become essential for handling ever-burgeoning observations, and for assessing data's evidential meaning for operational eruption forecasting.

[1] We obtain estimates of the seismic velocity and attenuation for White Island volcano by use of high-impact sandbag drops from helicopter. Three drops were attempted, two at either end of a 6-station linear array within the crater floor, and the third in the volcano's crater lake. The bags were dropped from $310-380 m height and contained $700 kg of sand. The impact velocity was estimated at $60-70 m/s yielding a kinetic energy of about 10 6 Nm, giving P-wave onsets to a distance of $1 km. We obtained a seismic velocity estimate of Vp = 1.2 km/s for the unconsolidated crater floor and Vp = 2.2 km/s for rays traversing through consolidated rock outside the crater. Attenuation was very strong (Q < 10) for both consolidated and unconsolidated parts of the volcano. This trial shows that low cost helicopter mass drops can be successfully applied to safely determine sub-surface properties at hazardous volcanoes.