Abstract

The existence of possible deep connections between nearby volcanoes has so far only been formulated on the basis of correlation in their eruptive activities or geochemical arguments. The use of geodetic data to monitor the deep dynamics of magmatic systems and the possible interference between them has remained limited due to the lack of techniques to follow transient processes. Here, for the first time, we use sequential data assimilation technique (Ensemble Kalman Filter) on ground displacement data to evaluate a possible interplay between the activities of Grímsvötn and Bárðarbunga volcanoes in Iceland. Using a two-reservoir dynamical model for the Grímsvötn plumbing system and assuming a fixed geometry and constant magma properties, we retrieve the temporal evolution of the basal magma inflow beneath Grímsvötn that drops by up to 85% during the 10 months preceding the initiation of the Bárðarbunga rifting event. We interpret the loss of at least 0.016 km3 in the magma supply of Grímsvötn as a consequence of magma accumulation beneath Bárðarbunga and subsequent feeding of the Holuhraun eruption 41 km away. We demonstrate that, in addition to its interest for predicting volcanic eruptions, sequential assimilation of geodetic data has a unique potential to give insights into volcanic system roots.

Highlights

  • The rate of magma supply to volcanic systems which fundamentally controls the eruptive activity is a determinant piece of information mostly retrieved by geodesy and/or gas measurements

  • Problems in volcanology in the past[9,10,11], this study is the first one to apply sequential data assimilation based on a dynamical model as proposed by ref.[12] using a real dataset recorded on a volcano

  • Another interesting result is that if we follow the similar approach to track Qin by first fixing non-evolving uncertain parameters, but use an inversion approach (i.e. Markov Chain Monte Carlo (MCMC)) as a second step instead of data assimilation, we find that MCMC slightly detected the change in Qin, did not yield a strong satisfactory fit with the data (Fig. 5)

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Summary

Results

Despite the non-uniqueness, these values are consistent with the data, the physics of the model and the results of previous studies[15,17,25], such that we are able to fix the non-evolving parameters and proceed to step-2 to follow the variation of Qin. If no observation is used to correct the dynamical model, the result is called the “Free-run” (Fig. 5) where the model is only propagated forward in time. Note that we only use an early subset of the radial displacement data for the inversion (i.e. dotted green box in Fig. 5A), the inferred best-fit. Parameters Geometry ad (km), radius of the deep reservoir.

Initial condition
Implications of the change in magma supply rate at Grímsvötn
Lessons learned
Methods
ΔPsti ti
Author Contributions
Additional Information
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