IAVCEI 2013 Scientific Assembly - July 20 - 24, Kagoshima, Japan
Forecasting Volcanic Activity - Reading and translating the messages of nature for society

0P1_4E-O1

Room A3

Date/Time: July 20 14:30-14:45

Short–term eruption forecasting with BET_EF: applications and results
Laura Sandri1 , Warner Marzocchi2 , Jacopo Selva1 , Paolo Papale3 , Dmitri Rouwet1
1Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Bologna, Italy, 2 Istituto Nazionale
di Geofisica e Vulcanologia, Sezione di Roma1, Roma, Italy, 3 Istituto Nazionale di Geofisica e
Vulcanologia, Sezione di Pisa, Pisa, Italy
E-mail: laura.sandri@bo.ingv.it

Bayesian Event Tree for Eruption Forecasting (BET_EF) code was proposed several years ago as a tool toquantify probabilities, and associated uncertainty, of the most relevant outcomes of volcanic unrest, such as thedetection of the state of unrest, and the probability of (i) magma actively causing unrest, (ii) eruption, (iii) ventposition and(iv) eruptive type or size or class. The proposed code computes the probabilities based on heterogeneous
sources of information, such as volcanological conceptual models, observed frequencies, monitoring data and expert
opinion. The importance of the different pieces of information depends on the time scale of the expected changes in thevolcano state, that is, ultimately, on the time term of the forecast: the most relevant evidence is respectively
represented by volcanological models and observed past frequency for a long–term forecast, and monitoring
information and expert opinion in the short-term.
Here, we summarize the results obtained and the lessons learned by applying the code in simulation exercises(MESIMEX in 2006 at Vesuvius, Italy; RUAUMOKO in 2008 at the Auckland Volcanic Field, New Zealand; COLIMA
in 2012 at Colima, Mexico) and in an ongoing, several years long, application at Campi Flegrei, Italy, heavily based onreal monitoring data and sessions of expert elicitation.
Although it is still an ongoing evaluation, one of the most important lessons learned from the application of BET_EF is thatits output probabilities have so far reproduced the opinion of a pool of experts. In this view, such probabilities
are a clear example of "subjective" probability, representing a "degree of belief" on the likelihood of the occurence of anuncertain–outcome event, rather than the "expected frequency" of occurrence of a generic type of events. This is
enforced also by the widespread belief, within the volcanological community, that every volcano (if not every eruption)
has some peculiarities, and so a "unique" model for the forecast of eruptions, based on catalogs from many volcanoes andvalid for all of them, is not possible. This approach has similarities with the one taken by many hazard assessment madeby the seismological communities (Marzocchi and Zechar, 2011); a recent example is Uniform California Earthquake
Rupture Forecast that aims at providing, by means of expert opinion, a comprehensive framework for computing a rupture
forecast for California. The testability of the "degree of belief" is still a subject of discussion, and it is not yet available
a self–consistent probabilistic framework to test such a kind of probabilistic approach. In this presentation we summarize
why a subjective probability is fundamental for taking decisions in managing volcanic unrest and the current initiatives that
aim at evaluating scientifically such a kind of forecasts.

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