Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/10820
Authors: Constantinescu, Robert* 
Robertson, Richard* 
Lindsay, Jan M* 
Tonini, Roberto* 
Sandri, Laura* 
Rouwet, Dmitri* 
Smith, Patrick* 
Stewart, Roderick* 
Title: Application of the probabilistic model BET_UNREST during a volcanic unrest simulation exercise in Dominica, Lesser Antilles
Journal: Geochemistry, Geophysics, Geosystems 
Series/Report no.: /17 (2016)
Issue Date: 2016
DOI: 10.1002/2016GC006485
Abstract: We report on the first ‘‘real-time’’ application of the BET_UNREST (Bayesian Event Tree for Volcanic Unrest) probabilistic model, during a VUELCO Simulation Exercise carried out on the island of Dominica, Lesser Antilles, in May 2015. Dominica has a concentration of nine potentially active volcanic centers and frequent volcanic earthquake swarms at shallow depths, intense geothermal activity, and recent phreatic explosions (1997) indicate the region is still active. The exercise scenario was developed in secret by a team of scientists from The University of the West Indies (Trinidad and Tobago) and University of Auckland (New Zealand). The simulated unrest activity was provided to the exercise’s Scientific Team in three ‘‘phases’’ through exercise injects comprising processed monitoring data. We applied the newly created BET_UNREST model through its software implementation PyBetUnrest, to estimate the probabilities of having (i) unrest of (ii) magmatic, hydrothermal or tectonic origin, which may or may not lead to (iii) an eruption. The probabilities obtained for each simulated phase raised controversy and intense deliberations among the members of the Scientific Team. The results were often considered to be ‘‘too high’’ and were not included in any of the reports presented to ODM (Office for Disaster Management) revealing interesting crisis communication challenges. We concluded that the PyBetUnrest application itself was successful and brought the tool one step closer to a full implementation. However, as with any newly proposed method, it needs more testing, and in order to be able to use it in the future, we make a series of recommendations for future applications.
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