Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/13881
Authors: Bevilacqua, Andrea* 
Bursik, Marcus* 
Patra, Abani* 
Pitman, Eric Bruce* 
Title: Multi-model probability assessments in the Long-Valley volcanic region (CA)
Issue Date: 2017
Keywords: Long Valley volcanic region
volcanic hazard assessment
Abstract: The Long Valley volcanic region is an active volcanic area situated at the east base of the Sierra Nevada escarpment, and dominated by a 32-km wide resurgent caldera of ~760 ka. Eruptions during the last 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. The past eruption record is characterized by significant acceleration during the last 6 ka. In 1325 - 1350 AD there was a ~1 km3 eruption along a 25 km section of the Mono-Inyo Craters chain. The most recent eruption in ~1700 AD created Paoha Island in Mono Lake. The last eruption in the southern part of the system was ~10 ka (Red Cones), but continuous CO2 degassing, potential precursory signals and recent geophysical studies suggest that the Mammoth Mountain area could be active again. Multiple spatial probability models were developed, based on past vents locations. One of the models couples this information with pre-existing faults, sampling a fault outcrop site as a parameter of proximity to the vent location forecast. Similarly, different Poisson-type models have been developed for modeling the temporal sequence of eruptions and making estimates for the current volcanic intensity of the system (i.e. the expected rate of eruptions per year). The models implement various self-excitement features, assuming that the expected volcanic intensity is increased by past events and is instead decreased by prolonged periods of quiescence. All the available models can be considered as different “experts”, and this has significant analogies with “Structured Expert Judgment” problems. “Bayesian Model Averaging” is presented as a flexible technique for combining the results of multiple models, relying on their performance in hindcasting the past record. The analysis is setup in a doubly stochastic framework, enabling us to incorporate some of the main sources of epistemic uncertainty - these include the effects of the unknown relevance of Mammoth Mountain area, the incompleteness of the past record and mapped faults, and the uncertain age (and location) of past events. Our findings provide a rational basis for hazard mapping of the next eruption in the Long Valley volcanic region, suggesting that the hazard associated with Mammoth Mountain volcanism should be carefully reevaluated.
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