Blasting experiments were performed that investigate multiple explosions that occur in quick succession in the ground and their effects on host material and atmosphere. Such processes are known to occur during volcanic eruptions at various depths, lateral locations, and energies. The experiments follow a multi-instrument approach in order to observe phenomena in the atmosphere and in the ground, and measure the respective energy partitioning. The experiments show significant coupling of atmospheric (acoustic)- and ground (seismic) signal over a large range of (scaled) distances (30–330 m, 1–10 mJ^-1/3). The distribution of ejected material strongly depends on the sequence of how the explosions occur. The overall crater sizes are in the expected range of a maximum size for many explosions and a minimum for one explosion at a given lateral location. The experiments also show that peak atmospheric over-pressure decays exponentially with scaled depth at a rate of d0 = 6.47×10-4 mJ-1/3; at a scaled explosion depth of 4×10-3 mJ-1/3 ca. 1% of the blast energy is responsible for the formation of the atmospheric pressure pulse; at a more shallow scaled depth of 2.75×10-3 mJ-1/3 this ratio lies at ca. 5.5–7.5%. A first order consideration of seismic energy estimates the sum of radiated airborne and seismic energy to be up to 20% of blast energy.

Interaction of magma with ground or surface water can lead to explosive phreatomagmatic eruptions. Poorly understood questions of this process center on effects of system geometry, length- and time scales, and these necessitate experiments at larger scale than previously conducted in order to investigate the thermohydraulic escalation behavior of rapid heat transfer. Previous experimental work either realized melt-water interaction at similar (meter-) scales, using a thermite-based magma analog in a confining vessel, or on smaller scale using about 0.4kg remelted volcanic rock in an open crucible, with controlled premix and a trigger event created by a low energy air gun pellet (about 5J kinetic energy). The new setup uses 55kg melt for interaction, and the timing and locaiton of the magma-water premix can be controlled on a scale up to 1m. A trigger mechanism is a falling hammer that drives a plunger into the melt (about 28K kinetic energy). Results show intense interaction at relatively low water to magma mass ratio. A video analysis quantifies the rate and amount of melt ejection and compares the results with those using the same melt in the smaller scale setup. Experiments show that on the meter scale intense interaction can start spontaneously without an external trigger if the melt column above the initial mixing location is larger than 0.3m. A dependency of system response on water mass flow rate was not observed.

In this study, we use a doubly stochastic model to develop a short-term eruption forecasting method based on precursory signals. The method enhances the Failure Forecast Method (FFM) equation, which represents the potential cascading of signals leading to failure. The reliability of such forecasts is affected by uncertainty in data and volcanic system behavior and, sometimes, a classical approach poorly predicts the time of failure. To address this, we introduce stochastic noise into the original ordinary differential equation, converting it into a stochastic differential equation, and systematically characterize the uncertainty. Embedding noise in the model can enable us to have greater forecasting skill by focusing on averages and moments. In our model, the prediction is thus perturbed inside a range that can be tuned, producing probabilistic forecasts. Furthermore, our doubly stochastic formulation is particularly powerful in that it provides a complete posterior probability distribution, allowing users to determine a worst-case scenario with a specified level of confidence. We verify the new method on simple historical datasets of precursory signals already studied with the classical FFM. The results show the increased forecasting skill of our doubly stochastic formulation. We then present a preliminary application of the method to more recent and complex monitoring signals.