Where will the next flank eruption at Etna occur? An updated spatial probabilistic assessment

Abstract

In this paper, we propose an update of the spatial probability map for flank eruptions from Etna (Italy), based on the distribution of the flank eruptive fissures that opened in the last 4000 years. The general procedure followed is to split the fissure dataset into training and testing subsets; then we build models on the training subset under different assumptions and test them on their likelihood of the testing subset. This allows selecting objectively the best models and assumptions. Furthermore, it allows testing whether (i) unavoidable incompleteness in the mapped fissures, and (ii) possible migration through time in the location of the flank activity, have an effect on the training models that can or cannot be neglected. We used different spatial models by exploiting different Kernel functions (Exponential, Cauchy, Uniform, and Gaussian), and calculated the degree of clustering of flank fissures in the training data. The results show that neither under-recording nor possible migration in time affect significantly the informativeness of the previous flank fissures in forecasting the location of the successive ones. Our study provides a canonical map of the spatial probability for future flank eruptions at Etna based on the location of flank fissures that opened in the last 4000 years. The map confirms a preferred location along a Northeast-to-South area, corresponding to the location of the most active rifts. It also shows that the Southern flank of the volcano, which is the most urbanized one, sits downhill of the largest cumulated-probability area for flank eruption. We also run sensitivity analyses to test the effect of (i) restricting the data to the most recent 400 years, and (ii) including the information on the unclamping stress induced on the mapped fissures by sources of deformation proposed in literature for recent eruptions of Etna. The results of the sensitivity analyses confirm the main features of the canonical map, and add information on the epistemic uncertainty attached to it.