Bayesian construction of a long-term vent opening probability map in the Long Valley volcanic region (CA, USA)

Abstract

(2017) Bayesian construction of a long-term vent opening map in the Long Valley volcanic region (CA, USA), Statistics in Volcanology 3.1 : 1 − 36. DOI: http://dx.doi.org/10.5038/2163-338X.3.1 Authors retain copyright of their material under a Creative Commons Non-Commercial Attribution 3.0 License. Bevilacqua et al. Long Valley long-term vent opening maps 2 Abstract The Long Valley volcanic region is an active volcanic area situated at the eastern base of the Sierra Nevada escarpment and dominated by a 32 km wide resurgent caldera created ~760 ka. Eruptions after 180 ka have been localized at Mammoth Mountain on the western rim of the caldera and along the Mono-Inyo Craters volcanic chain stretching about 45 km northward. Three different probability models have been developed and then combined in a logic tree to estimate the long-term spatial probability of vent opening. These models include (i) an anisotropic kernel density estimator based on past vent locations, (ii) a new Bayesian model for coupling new vents with pre-existing faults, and (iii) a uniformly distributed probability map. The model combination procedure relies on Bayesian model averaging. Using a doubly stochastic framework enables us to incorporate some of the main sources of epistemic uncertainty about the interpretation of the volcanic system, thereby exploring their effect. Our vent-opening probability maps show two higher likelihood regions for new vent opening, one around Mammoth Mountain to the south, and the other along the Mono-Inyo Craters to the north. The spatial vent opening probability, conditional on an eruption, is estimated as ~64% in the northern region and ~36% in the southern region, with an uncertainty of about ±20%. These findings provide a rational basis for the hazard mapping of a potential eruption in the Long Valley volcanic region, suggesting that the hazard associated with Mammoth Mountain volcanism should be fully evaluated.