Supplemental Information for Mantle Thermochemical Variations
beneath the Continental United States Through Petrologic Interpretation
of Seismic Tomography
William J. Shinevar1*, Eva M.
Golos2, Oliver Jagoutz3, Mark D.
Behn4, Robert Van der Hilst3
1 MIT/WHOI Joint Program in Oceanography/Applied Ocean
Engineering
2 Brown University
3 Massachusetts Institute of Technology
4 Boston College
*now at University of Colorado Boulder
This supplement contains two supplemental tables containing compiled
xenolith and primary magma thermobarometry, two supplemental figures,
and a data file containing the results plotted in Figures 3‒6.
Supplementary Information:
Comparison with primary magma
thermobarometry
Another temperature comparison is with primary magma thermobarometry,
which uses compositions of primary magmas and primary melt inclusions to
calculate the pressure and temperature at which a melt was last in
equilibrium with the mantle (c.f. Till, 2017). Here we use temperatures
estimated from different thermometers that incorporate both tholeiitic
and alkaline samples (Leeman et al. , 2005; Ruscitto et
al. , 2010; Till et al. , 2013; Plank and Forsyth, 2016; Till,
2017). We also include recalculated temperature estimates from high-Mg
andesites from Mt. Shasta (Baker et al., 1994; Grove et al., 2002) using
the thermometer of Mitchell and Grove (2015) at 1.5 GPa (Mt. Shasta,
coldest blue square, Figure S7) (Supplementary Table 2). Due to the
importance of measuring H2O and CO2 on
the liquidus temperature and pressure, we only use estimates made with
measured H2O based on melt inclusions. As volatiles
rapidly diffuse from melt inclusions (Bucholz et al., 2013; Gaetani et
al., 2012), the temperature estimates for hydrous melting using these
water contents are an upper bound (Till, 2017), but generally agree with
estimates from regional tholeiitic primary magmas. To compare regions
with different numbers of estimates, we bin magmatic temperature
estimates into 0.5°x0.5° regions for pressure estimates within 0.3 GPa
of the regional pressures at 60 and 80 km. We take the uncertainty in
the magmatic temperature estimates to be the maximum of the reported
uncertainty or the standard deviation of averaged estimates. This binned
temperature estimate is compared with the mean temperature of our
results at the relative depths slices within 0.5° arc-distance.
Uncertainty in our temperature results is defined as the maximum between
the average temperature uncertainty and the standard deviation of
averaged temperatures.
Our results underpredict magmatic temperatures estimates (square, Figure
S1). On average, we underpredict magmatic temperature estimates beyond
estimated uncertainty (RMSE=260°C at 60 km, 110°C at 80 km). There are
multiple hypotheses for this disagreement: (1) scale of the temperature
estimates, (2) error in the anelastic correction of seismic wave speeds,
(3) error in the magmatic thermobarometers, or (4) error in the forward
calculation of mantle seismic wave speeds, potentially due to the
exclusion of melt or hydrous phases. The following paragraphs discuss
these possibilities.
The first potential cause for the systematic difference in our results
and magmatic temperature estimates is the scale over which each is
measuring. Magmatic temperature estimates sample the temperature at
which the melt was in equilibrium with the mantle and may only represent
small regions (~1 m) of the mantle, especially if melt
is focused (e.g., Kelemen and Dick, 1995) and/or escapes the mantle on
short (~10 kyr) timescales (Feineman and DePaolo, 2003).
Conversely, seismic tomographic inversions such as MITPS_20 are limited
to resolving seismic anomalies greater than ~1.5°x1.5°
with vertical resolution on the order of 10 km (Golos et al., 2020).
Thus, non-pervasive, small-scale seismic anomalies due to thermal
upwellings or melt may be smeared or not sensed. Furthermore, seismic
inversions smooth their wave speeds in order to stabilize the inversion,
though MITPS_20 corrects for some of this effect (see Methodology).
Subduction zones are especially difficult to image due to any smearing
of the cold, subducting lithosphere, which increases seismic wave speed,
decreasing the temperature estimate. The fact that the 80-km
temperatures are in better agreement with magmatic estimates may suggest
that at 60 km, vertical smearing may increase the inverted seismic wave
speeds as the tomography samples from starkly colder lithosphere along a
steep geotherm. Regional, high-resolution seismic studies are necessary
to understand these effects.
A second reason for the temperature discrepancy could be error in
anelastic corrections of seismic wave speed. Anelasticity experiments on
olivine and peridotite are difficult, with various experimental groups
giving different results and sensitivities (Faul and Jackson, 2015;
Karato and Park, 2018). The Behn et al. (2009) power-law
formulation of anelasticity does not fit experimental data well at low
quality factors (Q-1>0.1, high
temperatures or melt present. Jackson and Faul, 2010). Certain
parameters in the both anelasticities are relatively unconstrained, like
the activation volume that controls the pressure sensitivity (Faul and
Jackson, 2015; Jackson and Faul, 2010). Other comparisons of
high-quality seismic experiments and forward calculations of peridotite
seismic wave speeds required altering the relaxation peak of the
frequencies in order for the observations to be interpreted by the
Jackson and Faul (2010) anelasticity (Ma et al., 2020). Furthermore, the
effect of water content on anelasticity is currently debated (Aizawa et
al., 2008; Cline et al., 2018; Karato and Park, 2018). Increasing the
water content decreases Vs at high temperatures,
thus shifting all forward calculations above ~900°C to
the left in Figure 2. As we assumed relatively dry water contents
(COH=50 H/106 Si), assuming an
increased water content would decrease the interpreted temperatures.
While grain size is an important parameter for anelasticities (Behn et
al., 2009; Faul and Jackson, 2005), we have assumed a reasonable grain
size near the upper bound observed in xenoliths. Any grain size
reduction increases anelastic effects, thus reducing the modeled
temperature. Therefore, variable grain size and its effect on
anelasticity cannot reconcile the temperature discrepancy discussed
here. Oxidation has been found to increase dissipation (Cline et al.,
2018), not incorporated in our methods. This would also reduce our
temperature results in arc settings as arc mantle is more oxidized
(Kelley and Cottrell, 2012). Conversely, as long as melt and fluids are
focused, oxidation would not decrease large-scale seismic wave speed as
only a small portion of the mantle may be highly oxidized.
While increasing water content can drastically reduce the peridotite
solidus, tholeiitic (dry) magmas are observed in the western United
States (Till, 2017). Melting experiments on dry peridotite compositions
require temperatures >1300°C at 60 km depth, greater than
nearly all our temperature results (Hirschmann, 2000). Given the
existence of Holocene age tholeiitic magmas, the reported uncertainty in
magmatic thermobarometry (11–43°C, 0.1–0.4 GPa) cannot explain the
temperature discrepancy present at 60 km.
Miscalculation in the forward calculation of seismic wave speeds in
WISTFUL is also unlikely to explain the temperature estimate
discrepancy. As we incorporate expected non-systematic uncertainty from
our forward calculations into the error allowed for fitting and utilize
current experimental moduli for most mineral endmembers, the only
systematic error from this could be due to a difference in mixing
assumptions, e.g. anisotropy. WISTFUL calculates the isotropic wave
speeds, so comparing with the fast direction wave speeds would produce
colder than realistic temperature estimates. As MITPS_20 inverts
isotropic wave speed from combination of teleseismic body waves and
surface wave arrival times, a systematic increase in recovered wave
speed due to anisotropy beneath all regions with magmatic temperature
estimates is unlikely.
Lastly, WISTFUL does not incorporate any effect of melt and hydrous
phases, both of which would decrease the predicted temperature. Melt
strongly reduces Vs (e.g., Hammond and Humphreys,
2000), but the exact wave speed reduction is heavily dependent on the
melt content and the melt connectivity (Zhu et al., 2011). Thus,
incorporating the effect of melt would make supersolidus temperatures
require even lower wave speeds than observed. Similarly, pargasitic
amphibole,
(NaCa2(Mg4Al)(Si6Al2)O22(OH)2),
the most common hydrous phase predicted for the shallow mantle (Dawson
and Smith, 1982), has Vs =3.85 km
s-1 andVp/Vs =1.83 at 60 km pressure and
1200°C assuming the same anelasticity described in Section 3 and moduli
from Abers and Hacker (2016). At the same conditions, diopside
(MgCaSi2O6) hasVs =4.35 km s-1 andVp/Vs =1.78. Replacing
clinopyroxene at the same temperature with pargasite would decreaseVs and increaseVp/Vs (shifting all forward
calculations to the upper left in Figure 2). Therefore, the addition of
melt and/or hydrous phases would decrease the predicted temperature for
the same seismic wave speed. Despite amphibole dikes and peridotites
being hypothesized as a significant source of volatiles for alkaline and
ocean-island basalts (Harry and Leeman, 1995; Pilet et al., 2011), the
volume required to have a geochemical impact is unlikely to have a
noticeable impact on seismic wave speed of the shallow mantle on the
scale we are can interpret with MITPS_20 (~1.5°).
In summary, our results agree within error of recent (<10 Ma)
xenolith compositions, predict temperature greater or equal temperature
to spinel-bearing and garnet-bearing xenoliths, but underpredict
magmatic temperature estimates, especially at 60 km. The systematic
difference between our best-fit temperature estimates with magmatic
temperature estimates are best explained by a difference in the scale of
the estimates, smearing in the tomographic models at shallow depths
along a steep geotherm, and/or errors in anelastic corrections. Further
experimental work on anelasticity is required to better interpret high
temperature mantle regions like the western United States.
Supplementary Table 1: Compiled xenolith thermobarometry and
compositional data. Empty cells represent data that was not reported or
measured. All compositional data is in wt. %. References listed at the
bottom of the table.
Supplementary Table 2: Locations, sources, and primary magma
thermobarometry data utilized for Figure S1. Full references listed at
bottom of table.
Supplemental Data File 1: This data file contains the MITPS_20
model used to produce the results presented in the paper of temperature,
Mg #, and density with uncertainties at 60, 80, and 100 km depth.
Variables are named following the table below.