Stochastic forecasting: precursor identification and scenario simulation using multivariate time series from Soufrière Hills volcano, Montserrat

Time series recorded at active volcanoes are often incomplete and can consist of small data sets. Due to the complexity of volcanic processes and inherent uncertainty, a probabilistic framework is needed for forecasting. A stochastic approach, named DEVIN, was developed to perform forecasts of volcanic activity. DEVIN is a multivariate approach based on geostatistical concepts which enables: (1) detection and quantification of time correlation using variograms, (2) identification of precursors by parameter monitoring and (3) forecasting of specific volcanic events by Monte Carlo methods. The DEVIN approach was applied using seismic data monitored from the Soufrière Hills Volcano (Montserrat). Forecasts were produced for the onset of dome growth with the help of potential precursors identified by monitoring of variogram parameters. Using stochastic simulations of plausible eruptive scenarios, these forecasts were expressed in terms of probability of occurrence. They constitute valuable input data as required by probabilistic risk assessments.