loading page

Volcanic eruption time forecasting using a stochastic enhancement of the Failure Forecast Method
  • +5
  • Andrea Bevilacqua,
  • Abani Patra,
  • E Pitman,
  • Marcus Bursik,
  • Flora Giudicepietro,
  • Giovanni Macedonio,
  • Augusto Neri,
  • Greg Valentine
Andrea Bevilacqua
University at Buffalo

Corresponding Author:bev87@hotmail.it

Author Profile
Abani Patra
University at Buffalo
Author Profile
E Pitman
SUNY Buffalo
Author Profile
Marcus Bursik
University at Buffalo
Author Profile
Flora Giudicepietro
Istituto Nazionale di Geofisica e Vulcanologia
Author Profile
Giovanni Macedonio
INGV
Author Profile
Augusto Neri
INGV National Institute of Geophysics and Volcanology
Author Profile
Greg Valentine
University at Buffalo
Author Profile

Abstract

In this study, we use a doubly stochastic model to develop a short-term eruption forecasting method based on precursory signals. The method enhances the Failure Forecast Method (FFM) equation, which represents the potential cascading of signals leading to failure. The reliability of such forecasts is affected by uncertainty in data and volcanic system behavior and, sometimes, a classical approach poorly predicts the time of failure. To address this, we introduce stochastic noise into the original ordinary differential equation, converting it into a stochastic differential equation, and systematically characterize the uncertainty. Embedding noise in the model can enable us to have greater forecasting skill by focusing on averages and moments. In our model, the prediction is thus perturbed inside a range that can be tuned, producing probabilistic forecasts. Furthermore, our doubly stochastic formulation is particularly powerful in that it provides a complete posterior probability distribution, allowing users to determine a worst-case scenario with a specified level of confidence. We verify the new method on simple historical datasets of precursory signals already studied with the classical FFM. The results show the increased forecasting skill of our doubly stochastic formulation. We then present a preliminary application of the method to more recent and complex monitoring signals.