Abstract
Unrest at the Greek volcanic island of Santorini in 2011–2012 was a cause for unease for some governments, concerned about risks to their nationals on this popular holiday island if an eruption took place. In support of urgent response planning undertaken by the UK government, we developed a rapid evaluation of different eruption scenario probabilities, using the Bayesian Belief Network (BBN) formulation for combining multiple strands of scientific and observational evidence. Here we present three alternative BBN models that were devised in early 2012 for assessing the situation: (1) a basic static net for evaluating probabilities at any one moment in time, utilising just four key unrest indicators; (2) a compound time-stepping net, extending the basic net to update probabilities through time as the indicators changed; and (3) a more comprehensive net, with multiple lines of other data and observations incorporated, reflecting diversity of modern multi-parameter monitoring techniques. A key conclusion is that, even with just three or four basic indicators, it is not feasible, or defensible, to attempt to judge mentally the implications of signs of unrest – a structured probabilistic procedure using Bayes’ Rule is a rational approach for enumerating evidential strengths reliably. In the Santorini case, the unrest, and official anxiety, diminished quite quickly and our approach was not progressed to the point where detailed consideration was given to BBN parameters, analysis of data uncertainty or the elicitation of expert judgements for quantifying uncertainties to be used in the BBN. Had this been done, the resulting scenario probabilities could have been adopted to determine likelihoods of volcanic hazards and risks caused by possible eruptive activity, as identified in a concurrent assessment of the scale and intensities of potential volcanic impacts (Jenkins et. al., Assessment of ash and gas hazard for future eruptions at Santorini Volcano, Greece. forthcoming). Ideally, such hazard and risk assessments should be elaborated in detail and critiqued well before crisis-level unrest develops – not initiated and implemented within a few hours just when a situation looks ominous. In particular, careful analysis of all information is required to determine and represent parameter uncertainties comprehensively and dependably.
Highlights
In January 2011, there was a sharp increase in seismic activity beneath the Kameni islands in the Santorini caldera (Thera), and surface deformation was detected that was interpreted as the inflation of a magmatic source (Newman et al 2012; Papoutsis et al 2013)
While the most common presumption was that the unrest was due to magmatic intrusion at shallow depth, it is plausible that it was a result of wider tectonic stresses, and not or necessarily solely volcanic in origin. If this possibility is ignored or discounted – something that can happen, say, with volcanologists in crisis mode focusing on their specialism – the corollary is that volcanic hazard levels might be over-stated
The structured graphical procedure afforded by the Bayesian Belief Network (BBN) technique offers an efficient and tractable way to manage the problem
Summary
Note that this model exemplifies an extension of the conversation to secondary indicators, such as Sea_temp and Sea_state, in relation to which observations of above ambient temperature or of bubbling are presumed to be evidence of elevated (submarine) gas output In this example a node Felt_quakes is included, with discrete enumerated states quantified in terms of number of events in a given time in specified ranges. This latter scenario might be regarded as a “worst considered case” Outputs from such dispersion models take the form of probabilistic maps and exceedance probability curves for key locations Those findings can be conditioned on the initiating eruption probability, as determined by the eruption BBN analysis approach described here, and on eruption style, intensity, duration and other factors
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