Understanding causality and uncertainty in volcanic observations: an example of forecasting eruptive activity on Soufrière Hills Volcano, Montserrat

Sheldrake, T. E., Aspinall, W. P., Odbert, H. M., Wadge, G. and Sparks, R. S. J. (2017) Understanding causality and uncertainty in volcanic observations: an example of forecasting eruptive activity on Soufrière Hills Volcano, Montserrat. Journal of Volcanology and Geothermal Research, 341. pp. 287-300. ISSN 0377-0273 doi: 10.1016/j.jvolgeores.2017.06.007

Abstract/Summary

Following a cessation in eruptive activity it is important to understand how a volcano will behave in the future and when it may next erupt. Such an assessment can be based on the volcano's long-term pattern of behaviour and insights into its current state via monitoring observations. We present a Bayesian network that integrates these two strands of evidence to forecast future eruptive scenarios using expert elicitation. The Bayesian approach provides a framework to quantify the magmatic causes in terms of volcanic effects (i.e., eruption and unrest). In October 2013, an expert elicitation was performed to populate a Bayesian network designed to help forecast future eruptive (in-)activity at Soufrière Hills Volcano. The Bayesian network was devised to assess the state of the shallow magmatic system, as a means to forecast the future eruptive activity in the context of the long-term behaviour at similar dome-building volcanoes. The findings highlight coherence amongst experts when interpreting the current behaviour of the volcano, but reveal considerable ambiguity when relating this to longer patterns of volcanism at dome-building volcanoes, as a class. By asking questions in terms of magmatic causes, the Bayesian approach highlights the importance of using short-term unrest indicators from monitoring data as evidence in long-term forecasts at volcanoes. Furthermore, it highlights potential biases in the judgements of volcanologists and identifies sources of uncertainty in terms of magmatic causes rather than scenario-based outcomes.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/72257
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Elsevier
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