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\begin{document}

\title{Constraining Upper Mantle Viscosity Using Temperature and Water Content
Inferred from Seismic and Magnetotelluric Data}



\author[1]{Florence Ramirez}%
\author[2]{K Selway}%
\author[1]{C P Conrad}%
\author[3]{C Lithgow-Bertelloni}%
\author[1]{Florence Ramirez}%
\affil[1]{University of Oslo}%
\affil[2]{Macquarie University}%
\affil[3]{University of California}%


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  \date{May 27, 2024}


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\begin{abstract}
Mantle viscosity controls a variety of geodynamic processes such as
glacial isostatic adjustment (GIA), but it is poorly constrained because
it cannot be measured directly from geophysical measurements. Here we
develop a method that calculates viscosity using empirical viscosity
flow laws coupled with mantle parameters (temperature and water content)
inferred from seismic and magnetotelluric (MT) observations. We find
that combining geophysical constraints allows us to place significantly
tighter bounds on viscosity estimates compared to using seismic or MT
observations alone. In particular, electrical conductivity inferred from
MT data can determine whether upper mantle minerals are hydrated, which
is important for viscosity reduction. Additionally, we show that rock
composition should be considered when estimating viscosity from
geophysical data because composition directly affects seismic velocity
and electrical conductivity. Therefore, unknown composition increases
uncertainty in temperature and water content, and makes viscosity more
uncertain. Furthermore, calculations that assume pure thermal control of
seismic velocity may misinterpret compositional variations as
temperature, producing erroneous interpretations of mantle temperature
and viscosity. Stress and grain size also affect the viscosity and its
associated uncertainty, particularly via their controls on deformation
regime. Dislocation creep is associated with larger viscosity
uncertainties than diffusion creep. Overall, mantle viscosity can be
estimated best when both seismic and MT data are available and the
mantle composition, grain size and stress can be estimated. Collecting
additional MT data probably offers the greatest opportunity to improve
geodynamic or GIA models that rely on viscosity estimates.%
\end{abstract}%



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