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Figure 3 | Journal of Applied Volcanology

Figure 3

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

Figure 3

Bayesian belief network diagram for La Soufrière. BBN showing the relationship between volcanic processes, states and observations available in 1976, used to infer future activity. Nodes represent both hidden (grey) and observable (blue) states. The arcs between nodes represent conditional dependencies (e.g. direct causal relationships or influence) and are characterized by conditional probability tables (CPTs). The arrows indicate the direction of influence e.g. venting tremor is believed to be a sign of perturbation of the hydrothermal system resulting from magma ascent. In this case, all conditional probability distributions (and associated uncertainties) were obtained by expert elicitation (for example, the probability of observing SO2 given that magma is ascending), the network structure being agreed by the group prior to elicitation. For data rich applications parameters can be estimated purely from data, or a combination of observations and prior knowledge (e.g. using Dirichlet priors or other parameter constraints: Niculescu et al. (2006). The basic difference between this and a logic or event tree representation is explained in the Methods section and Figure 4.

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