Retrospective analysis of uncertain eruption precursors at La Soufrière volcano, Guadeloupe, 1975–77: volcanic hazard assessment using a Bayesian Belief Network approach

• Published in: Journal of applied volcanology Vol. 3; no. 1; pp. 3 - 26
• Main Authors: Hincks, Thea K, Komorowski, Jean-Christophe, Sparks, Stephen R, Aspinall, Willy P
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2014; Springer Nature B.V; BioMed Central Ltd; Springer Verlag
• Subjects: Analysis; Bayesian theory; Crises; Decision making; Earth and Environmental Science; Earth Sciences; Eruptions; Geological hazards; Geology; Geophysics/Geodesy; Hazard assessment; Natural Hazards; Probability theory; Risk assessment; Sciences of the Universe; Uncertainty; Volcanoes; Volcanology; Guadeloupe; ASW, Caribbean Sea, Lesser Antilles, St. Lucia, Soufriere; ASW, Caribbean Sea, Lesser Antilles, Guadeloupe; Uncertainty; Volcanic hazards; Bayesian inference; Expert judgement; Decision making; Multi-parameter monitoring
• ISSN: 2191-5040; 2191-5040
• DOI: 10.1186/2191-5040-3-3

Background Scientists monitoring active volcanoes are increasingly required to provide decision support to civil authorities during periods of unrest. As the extent and resolution of monitoring improves, the process of jointly interpreting multiple strands of indirect evidence becomes increasingly complex. Similarities with uncertainties in medical diagnosis suggest a formal evidence-based approach, whereby monitoring data are analysed synoptically to provide probabilistic hazard forecasts. A statistical tool to formalize such inferences is the Bayesian Belief Network (BBN). By explicitly representing conditional dependencies between the volcanological model and observations, BBNs use probability theory to treat uncertainties in a rational and auditable manner, as warranted by the strength of the scientific evidence. A retrospective analysis is given for the 1976 Guadeloupe crisis, using a BBN to provide inferential assessment of the state of the evolving magmatic system and probability of incipient eruption. Conditional dependencies are characterized quantitatively by structured expert elicitation. Results Analysis of the available monitoring data suggests that at the height of the crisis the probability of magmatic intrusion was high, in accordance with scientific thinking at the time. The corresponding probability of magmatic eruption was elevated in July and August 1976 and signs of precursory activity were justifiably cause for concern. However, collective uncertainty about the future course of the crisis was also substantial. Of all the possible scenarios, the most likely outcome evinced by interpretation of observations on 31 August 1976 was 'no eruption’ (mean probability 0.5); the chance of a magmatic eruption/blast had an estimated mean probability of ~0.4. There was therefore no evidential basis for asserting one scenario to be significantly more likely than another. Conclusions Our analysis adds objective probabilistic expression to the volcanological narrative at the time of the 1976 crisis, and demonstrates that a formal evidential case could have supported the authorities' concerns about public safety and decision to evacuate. Revisiting the episode highlights many challenges for modern, contemporary decision making under conditions of considerable uncertainty, and suggests the BBN is a suitable framework for marshalling multiple, uncertain observations, model results and interpretations. The formulation presented here can be developed as a tool for ongoing use in the volcano observatory.