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Volcanic Eruption Forecasts From Accelerating Rates of Drumbeat Long‐Period Earthquakes

Accelerating rates of quasiperiodic “drumbeat” long‐period earthquakes (LPs) are commonly reported before eruptions at andesite and dacite volcanoes, and promise insights into the nature of fundamental preeruptive processes and improved eruption forecasts. Here we apply a new Bayesian Markov chain M...

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Bibliographic Details
Published in:Geophysical research letters 2018-02, Vol.45 (3), p.1339-1348
Main Authors: Bell, Andrew F., Naylor, Mark, Hernandez, Stephen, Main, Ian G., Gaunt, H. Elizabeth, Mothes, Patricia, Ruiz, Mario
Format: Article
Language:English
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Summary:Accelerating rates of quasiperiodic “drumbeat” long‐period earthquakes (LPs) are commonly reported before eruptions at andesite and dacite volcanoes, and promise insights into the nature of fundamental preeruptive processes and improved eruption forecasts. Here we apply a new Bayesian Markov chain Monte Carlo gamma point process methodology to investigate an exceptionally well‐developed sequence of drumbeat LPs preceding a recent large vulcanian explosion at Tungurahua volcano, Ecuador. For more than 24 hr, LP rates increased according to the inverse power law trend predicted by material failure theory, and with a retrospectively forecast failure time that agrees with the eruption onset within error. LPs resulted from repeated activation of a single characteristic source driven by accelerating loading, rather than a distributed failure process, showing that similar precursory trends can emerge from quite different underlying physics. Nevertheless, such sequences have clear potential for improving forecasts of eruptions at Tungurahua and analogous volcanoes. Key Points Power law acceleration in rates of long‐period earthquakes observed before large explosive eruption at Tungurahua volcano, Ecuador Earthquake source characteristics indicate repeated quasiperiodic activation of single source driven by accelerated loading New Bayesian gamma point process methodology applied to analyze data set and provide probabilistic eruption forecasts
ISSN:0094-8276
1944-8007
DOI:10.1002/2017GL076429