Open AccessBayesian Hierarchical Time Predictable Model for eruption occurrence: an application to Kilauea Volcano
Author(s) -
Passarelli Luigi,
Sandri Laura,
Bonazzi Alessandro,
Marzocchi Warner
Publication year - 2010
Publication title -
geophysical journal international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.302
H-Index - 168
eISSN - 1365-246X
pISSN - 0956-540X
DOI - 10.1111/j.1365-246x.2010.04582.x
Subject(s) - volcano , probabilistic logic , geology , volcanic hazards , seismology , bayesian probability , hazard , statistical model , probabilistic forecasting , volcanology , computer science , machine learning , artificial intelligence , chemistry , organic chemistry
SUMMARY The physical processes responsible for volcanic eruptions are characterized by a large number of degrees of freedom, often non‐linearly coupled. This extreme complexity leads to an intrinsic deterministic unpredictability of such events that can be satisfactorily described by a stochastic process. Here, we address the long‐term eruption forecasting of open conduit volcanoes through a Bayesian Hierarchical Modelling information in the catalogue of past eruptions, such as the time of occurrence, the duration, and the erupted volumes. The aim of the model is twofold: (1) to get new insight about the physics of the process, using the model to test some basic physical hypotheses of the eruptive process and (2) to build a stochastic model for long‐term eruption forecasting; this is the basic component of Probabilistic Volcanic Hazard Assessment that is used for rational land use planning and to design Emergency plan. We apply the model to Kilauea eruption occurrences and check its feasibility to be included in Probabilistic Volcanic Hazard Assessment.