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

Sheldrake, Thomas Edward; Aspinall, W.P.; Odbert, H.M.; Wadge, G.; Sparks, R.S.J.

doi:10.1016/j.jvolgeores.2017.06.007

Scientific article

English

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

Contributors

Sheldrake, Thomas Edward

; Aspinall, W.P.; Odbert, H.M.; Wadge, G.; Sparks, R.S.J.

Published in

Journal of Volcanology and Geothermal Research, vol. 341, p. 287-300

Publication date

2017

Abstract

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.

Keywords

Bayesian network
Expert elicitation
Eruption forecasting
Uncertainty
Evidence synthesis
Causal inference

UNIGE affiliation entities

Faculté des sciences / Section des sciences de la Terre et de l'Environnement / Département des sciences de la Terre

UNIGE research groups

Petrology and Volcanology

Citation (ISO format)

SHELDRAKE, Thomas Edward et al. Understanding causality and uncertainty in volcanic observations: An example of forecasting eruptive activity on Soufrière Hills Volcano, Montserrat. In: Journal of Volcanology and Geothermal Research, 2017, vol. 341, p. 287–300. doi: 10.1016/j.jvolgeores.2017.06.007

Article (Published version)

CC BY

Identifiers

PID : unige:96296
DOI : 10.1016/j.jvolgeores.2017.06.007

Additional URL for this publication

http://linkinghub.elsevier.com/retrieve/pii/S0377027316303687

Journal ISSN

0377-0273

922views

266downloads

Creation

24/08/2017 11:08:00

First validation

24/08/2017 11:08:00

Update

15/03/2023 01:57:08

Status update

15/03/2023 01:57:08

Last indexation

18/02/2025 12:47:42

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

Contributors

Published in

Publication date

Abstract

Keywords

UNIGE affiliation entities

UNIGE research groups

Citation (ISO format)

Main files (1)

Identifiers

Additional URL for this publication

Journal ISSN

Technical informations

Creation

First validation

Update

Status update

Last indexation