Geochemistry, Geophysics, Geosystems (Sep 2021)

Multidisciplinary Constraints on Magma Compressibility, the Pre‐Eruptive Exsolved Volatile Fraction, and the H2O/CO2 Molar Ratio for the 2006 Augustine Eruption, Alaska

  • Valerie K. Wasser,
  • Taryn M. Lopez,
  • Kyle R. Anderson,
  • Pavel E. Izbekov,
  • Jeffrey T. Freymueller

DOI
https://doi.org/10.1029/2021GC009911
Journal volume & issue
Vol. 22, no. 9
pp. n/a – n/a

Abstract

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Abstract Geodetically modeled reservoir volume changes during volcanic eruptions are commonly much smaller than the observed eruptive volumes. This discrepancy is thought to be partially due to the compressibility of magma, which is largely controlled by the presence of exsolved volatiles. The 2006 eruption of Augustine Volcano, Alaska, produced an eruptive volume that was ∼3 times larger than the geodetically estimated syn‐eruptive subsurface volume change. In this study, we use a multistep methodology that combines constraints from geodetic, volcanic gas, geologic, and petrologic data together with equations relating physical processes to observable parameters. We apply a Monte Carlo approach to quantify uncertainties. Ultimately, we solve for the exsolved volatile volume fraction and the magma compressibility. We estimate Augustine's 2006 pre‐eruptive exsolved volatile phase to be ∼5.5 vol% of the magma at storage depths, yielding a bulk magma compressibility of ∼3.8 × 10−10 Pa−1. We develop a novel approach to estimate the H2O/CO2 ratio of the syn‐eruptive gas emissions in the absence of direct H2O emission measurements which are hard to obtain due to the high background levels in ambient air. We find a best‐fit H2O/CO2 molar ratio of 29. We also investigate the effects of applying different equations of state to our model. We find that the Ideal Gas Law might be used as a first approximation due to its simplicity; however, it overestimates volatile density and compressibility significantly at storage depths. This project capitalizes on the insights that can be gained by integrating multidisciplinary data with models of physical processes.

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