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A plume origin for hydrous melt at the lithosphere–asthenosphere boundary

Abstract

Plate tectonics requires a low-viscosity layer beneath the lithosphere–asthenosphere boundary (LAB), yet the origin of this ductile transition remains debated1,2. Explanations include the weakening effects of increasing temperature3,4, mineral hydration5 or partial melt6. Electrical resistivity is sensitive to all three effects7, including melt volatile content8, but previous LAB constraints from magnetotelluric soundings did not simultaneously consider the thermodynamic stability of the inferred amount of melt and the effect of uncertainty in the estimated resistivity8,9,10,11,12,13,14. Here we couple an experimentally constrained parameterization of mantle melting in the presence of volatiles15,16 with Bayesian resistivity inversion17 and apply this to magnetotelluric data sensitive to a LAB channel beneath the Cocos Plate9. Paradoxically, we find that the conductive channel requires either anomalously large melt fractions with moderate volatile contents or moderate melt fractions with anomalously large volatile contents, depending on the assumed mantle temperature. Large melt fractions are unlikely to be mechanically stable and conflict with melt-migration models18. As large volatile contents require a highly enriched mantle source inconsistent with mid-ocean-ridge estimates19, our results indicate that a mantle plume emplaced volatile-rich melts in the LAB channel. This requires the presence of a previously undetected nearby plume or the influence of the distant Galápagos hotspot. Plumes that feed thin, hydrous melt channels9,14,20 may be an unrecognized source of LAB anomalies globally.

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Fig. 1: Diagrams illustrating trade-offs inherent in estimating mantle melt fraction and volatile content.
Fig. 2: Probability distributions for melt fraction and melt H2O content or bulk mantle hydration obtained from a Monte Carlo method.
Fig. 3: Marginal distributions for melt H2O, melt fraction, melt CO2, and bulk hydration from a Monte Carlo process.

Data Availability

The magnetotelluric data that were inverted and analysed in this study can be found at: https://doi.org/10.5281/zenodo.5510673Source data are provided with this paper.

Code Availability

The transdimensional Bayesian inversion code is available at https://bayesian1dem.bitbucket.io/. The suite of codes used to model the partial melting processes as well as the bulk resistivity of two-phase mantle will be released on request. Source data are provided with this paper.

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Acknowledgements

We gratefully acknowledge A. Malinverno and K. Kelley for productive conversations and helpful suggestions, and D. Hasterok for sharing his MATLAB script of the melting parameterization. The magnetotelluric data were collected with funding from NSF awards OCE-0841114 and OCE-0840894. This work was supported by the Electromagnetic Methods Research Consortium at Columbia University. A.R. publishes with the permission of the CEO of Geoscience Australia.

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Contributions

S.N. and K.K. collected and processed the magnetotelluric data. D.B. and S.N. wrote the partial melting and bulk resistivity modelling codes. A.R. and D.B. wrote the Bayesian inversion codes. D.B., S.N. and K.K. modelled the magnetotelluric data and produced the figures. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Daniel Blatter.

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Extended data figures and tables

Extended Data Fig. 1 Survey map, resistivity model estimate and field data from the Cocos Plate MT experiment offshore Nicaragua9.

Top, survey map and 2D deterministic resistivity model estimate, modified from ref. 9. The subducting Cocos Plate boundary is shown in dark red. Warm colours are more conductive, cool colours resistive. The conductive anomaly at the LAB is outlined by the black dashed line. MT stations are shown as triangles, with those stations included in this study indicated in red. Bottom, field MT data from all MT sites used in this study shown as apparent resistivity (upper row) and phase (lower row). A representative subset of Bayesian inversion model responses are shown in grey lines

Source data

Extended Data Fig. 2 Posterior probability density distributions for resistivity.

a, b, Marginal probability as a function of depth for MT sites s08 and s10, respectively. Warm colours indicate regions of higher probability. Red lines denote the 5th and 95th percentiles of the distribution at each depth, respectively. The solid white line is a vertical profile through the 2D deterministic inversion result (Extended Data Fig. 1) taken at each MT site. One-dimensional marginal distributions for resistivity are obtained for each MT sounding used in this study by computing the conductance of each model in the posterior ensemble over the interval between 40 km and 75 km (white dashed lines), then normalizing by the thickness of the interval (equivalent resistivity). ce, Equivalent resistivity distributions for MT sites s08, s10 and the combined distribution for all MT sites. The distribution in e is used throughout this analysis

Source data

Extended Data Fig. 3

Probability distributions for melt fraction and melt CO2 content (a) or bulk CO2 (b), for the combined MT soundings, estimated from a Monte Carlo method. Dashed lines indicate isotherms. Bulk water was held constant at 240 ppm. Nearly all the (T, ρ) draws require large melt fraction and/or high bulk CO2 concentration

Source data

Extended Data Fig. 4

Marginal distributions for melt H2O (a), melt fraction (b), melt CO2 (c) and bulk hydration (d), similar to Fig. 3 but with bulk hydration held constant at 240 ppm. Only at the coldest temperatures are low-degree, high-CO2-concentration melts stable. At warmer temperatures, bulk carbon concentrations must be elevated to match bulk resistivity. At the warmest temperatures, melt fractions are high enough that bulk resistivity is nearly insensitive to bulk carbon concentration

Source data

Extended Data Fig. 5 Melt and dissolved water produced as a function of depth beneath a mid-ocean ridge for various mantle potential temperatures.

a, Melt fraction as a function of depth. Cumulative kilometres of melt (starting from a depth of 150 km) (b) and the corresponding cumulative water in the melt (c). Dashed lines are the MT estimated melt and water contents of the hydrous channel; mantle potential temperatures and colours match Fig. 3. Only at the warmest temperatures is the water extracted from the solid mantle and incorporated into the partial melt under the ridge greater than the amount of water inferred in the hydrous channel

Source data

Extended Data Fig. 6 Comparison of asthenosphere resistivity from marine MT observations of oceanic plates.

Conductive LAB channels were observed in the MELT34,35, SERPENT9 and PI-LAB14 experiments. The magenta region indicates other MT studies2,11,36 that observed more resistive asthenosphere with no indications of conductive channels. For anisotropic inversion models9,34,35, only the inline resistivity (TM mode) is shown. Values are approximate

Source data

Extended Data Fig. 7 Suitability of the observed MT data for 1D modelling.

Top, TE mode (blue circles) and TM mode (red circles) data for all stations are shown with model responses (red lines) for 1D ρy resistivity profiles extracted from the 2D inversion model of ref. 9 beneath each station. Small differences at the shortest periods are owing to lateral variations in sediment thickness and shallow crustal structure. The RMS fit between the TM data and 1D profile responses are given for each station. Low RMS values indicate data that may be suitable for 1D modelling. RMS values shown in red text indicate stations deemed incompatible with 1D modelling owing to either large RMS values or large mean mode splits. Bottom, selection of 1D compatible data is further refined by examining the mean mode split between the TE and the TM complex impedances at each station. Mean mode splits with a relative difference below 0.25 indicate stations with data compatible with 1D interpretation

Source data

Extended Data Fig. 8 Effect of CO2 on resistivity of hydrous melt at T = 1,400 °C (ref. 8). Shown for comparison is the relationship for purely hydrous melts51 (solid blue line).

The effect of carbon on melt resistivity is not pronounced until the carbon concentration in the melt exceeds about 6 wt%

Source data

Extended Data Fig. 9 Effect of temperature, bulk hydration, melt fraction and melt volatile content on mantle bulk electrical resistivity.

a, For a damp mantle with no melt, both temperature and bulk hydration reduce bulk resistivity. The line styles in all three plots follow the temperatures in a. The curves in a are truncated where the solidus is reached and melt would be produced. b, A dry mantle with hydrous melt is considerably less resistive, even for small melt water concentrations. c, Carbon dioxide has a strong influence on melt resistivity, but only at high melt CO2 concentrations. Bulk resistivity was computed without carbon dioxide in b and without water in c. Mantle composition was assumed to be 60% olivine, 40% pyroxene

Source data

Extended Data Fig. 10 Using bulk resistivity and temperature to constrain petrologically stable melt fraction and melt H2O concentration.

For stable melts, melt fraction and melt water concentration both increase as a function of total mantle hydration (a), exerting the dominant control on bulk resistivity (b). Constant-resistivity combinations of melt fraction and melt water content (c, coloured curves) are plotted alongside the stable, constant-temperature combinations from a (c, black curves). For known T and ρ, the stable melt fraction and melt water content are known precisely (d, blue dot). If uncertainty in T or ρ is included, the stable combinations of melt fraction and melt water content plot along a line (d, red or green lines, respectively). If both T and ρ are uncertain, the petrologically stable combinations lie in a 2D region (d, grey region). Dashed lines (whether blue, orange or black) indicate curves at constant temperature throughout the figure

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Blatter, D., Naif, S., Key, K. et al. A plume origin for hydrous melt at the lithosphere–asthenosphere boundary. Nature 604, 491–494 (2022). https://doi.org/10.1038/s41586-022-04483-w

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