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

0P1_4E-O7

Room A3

Date/Time: July 20 16:00-16:15

Real-time forecasting of catastrophic rock failure and implications for predictability ofvolcanic eruptions
Ian G Main1 , Andrew F Bell1 , Rosa Filguera1 , Malcolm Atkinson1 , Philip G Meredith2 , Christopher
Kilburn2 , Stephen A Boon2
1

University of Edinburgh, UK, 2 University College London (UCL), UK
E-mail: ian.main@ed.ac.uk

There are a variety of methods for forecasting the probability of a volcanic eruption in a given time period, often
relying as much on expert judgement as formal analysis of geophysical, geodetic and geochemical data. Physicaltheories are commonly based on laboratory constitutive rules for catastrophic rock failure due to sub-critical crack
growth under a constant load or loading rate. In both cases an accelerating rate of deformation or acoustic
emissions is predicted with a well-defined inverse power law form that is consistent with independent laboratory
observation. Similar precursors have also been seen to first order in seismicity data before volcanic eruptions inindividual and stacked sequences, as well as prior to some intrusion events. To date most of these analyses are
retrospective, so the failure time and outcome is already known. Success in such hind-casts is a necessary but
not sufficient criterion for assessing the true forecasting power or quality in real time, and is a prime motivation forthe development of the global Collaboratory for the Study of Earthquake Predictability (CSEP), where forecasts arelodged verifiably in advance. Here we assess the potential significance and utility of real-time forecasting using
such constitutive rules by applying a variety of techniques. First we describe the results of Monte-Carlo simulationof realistic ideal model sequences with an appropriate degree of noise added. The best results are obtained whenmaximum likelihood statistical models that take appropriate account of the error structure are used. This method
has yet to be commonly applied in volcanic seismology. Even so there is large uncertainty quite close to thecatastrophic failure time, so reliable forecasting may not be possible even in this ideal case. Communication ofthis uncertainty, and deciding in advance on appropriate actions to be taken with low-probability forecasts, thenbecome as indispensable as making the forecast itself. The next step is to quantify forecast quality in a controlledlaboratory setting by modern real-time data assimilation techniques. We describe a prototype infrastructure forthis exercise that streams live data from a participating laboratory via a user-friendly portal, analyses the data andmakes continuously-updated forecasts in real time including uncertainty, and will present some preliminary results
if available. Finally we apply the method to data from recent volcanic eruptions, analysing the results as if they had
come in real time. In this case the uncertainties are not purely statistical, they are also epistemic, and continuous
multiple-hypothesis testing along the CSEP model is likely to be needed in cases which do not follow the simple
laboratory pattern. In this case model discrimination using standard methods such as Information Criteria or Bayes
ratio may also not be possible in real time until very near the eruption.

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