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Volcanic–plutonic parity and the differentiation of the continental crust

Nature volume 523, pages 301–307 (2015)Cite this article

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Abstract

The continental crust is central to the biological and geological history of Earth. However, crustal heterogeneity has prevented a thorough geochemical comparison of its primary igneous building blocks—volcanic and plutonic rocks—and the processes by which they differentiate to felsic compositions. Our analysis of a comprehensive global data set of volcanic and plutonic whole-rock geochemistry shows that differentiation trends from primitive basaltic to felsic compositions for volcanic versus plutonic samples are generally indistinguishable in subduction-zone settings, but are divergent in continental rifts. Offsets in major- and trace-element differentiation patterns in rift settings suggest higher water content in plutonic magmas and reduced eruptibility of hydrous silicate magmas relative to dry rift volcanics. In both tectonic settings, our results indicate that fractional crystallization, rather than crustal melting, is predominantly responsible for the production of intermediate and felsic magmas, emphasizing the role of mafic cumulates as a residue of crustal differentiation.

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Figure 1: Major-element Harker diagrams.
Figure 2: Trace-element Harker diagrams.
Figure 3: Fe–Mg systematics of arc and rift volcanic and plutonic rocks.
Figure 4: Water-saturated and water-undersaturated solidi in granitic systems.
Figure 5: Histograms of rock abundance as a function of silica content.
Figure 6: Comparison of global volcanic and plutonic differentiation to calculated and experimental fractional crystallization trends.

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Acknowledgements

C.B.K. was supported by a Computational Science Graduate Fellowship through US Department of Energy Office of Science grant DE-FG02-97ER25308. Computational resources were provided by the National Energy Research Scientific Computing Center under DOE Office of Science Contract No. DE-AC02-05CH11231, and by the Princeton Institute for Computational Science and Engineering. The authors are grateful to P. M. Antoshechkina for providing recompiled alphaMELTS executables and J. D. Gronewold for initial contributions.

Author information

Authors and Affiliations

  1. Department of Geosciences, Guyot Hall, Princeton University, Princeton, 08544, New Jersey, USA

    C. Brenhin Keller, Blair Schoene, Melanie Barboni, Kyle M. Samperton & Jon M. Husson

  2. Department of Earth, Planetary, and Space Sciences, University of California, Los Angeles, 90095-1567, California, USA

    Melanie Barboni

  3. Department of Geoscience, University of Wisconsin-Madison, Madison, 53706, Wisconsin, USA

    Jon M. Husson

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Contributions

C.B.K conducted the Monte Carlo calculations and geochemical simulations. All authors participated in interpretation of the results and preparation of the manuscript.

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Correspondence to C. Brenhin Keller.

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The authors declare no competing financial interests.

Additional information

Computational source code under a GNU GPL v2.0 open-source license, along with all source data, is freely available at https://github.com/PrincetonUniversity/VolcanicPlutonic and as Supplementary Information.

Extended data figures and tables

Extended Data Figure 1 Prior and posterior sample distributions for both volcanic and plutonic samples.

Left column, prior distributions (original data, a–d); right column, posterior distributions (that is, after Monte-Carlo analysis, e–h); note log scale on vertical axis. Red, volcanic samples; blue, plutonic samples; ideal distributions given uniform sampling of the continents (black line) are shown for comparison. Since bootstrap resampling probabilities are weighted to reject no more than 90% of samples, improvement in posterior distribution is limited to roughly one log unit.

Extended Data Figure 2 Sample locations and tectonic setting assignments.

a, All volcanic and plutonic sample locations (n = 294,347). b, Tectonic setting map modified from the USGS Global Crustal Database (adapted from ref. 55, USGS). c, Assigned arc and rift volcanic and plutonic sample locations (n = 141,561). d, Oceanic (MOR) sample locations (n = 48,605). Plutonic samples are plotted above and may obscure full range of volcanic samples. Samples not originating in explicit arc or rift provinces (grey area in b) were excluded from setting-specific differentiation trends, but included in the general volcanic and plutonic differentiation trends in subsequent figures.

Extended Data Figure 3 Age distributions for plutonic and volcanic samples with reported age data.

a, Plutonic (blue); b, volcanic (red). Both distributions are dominated by Phanerozoic (<540 Ma) samples, with a slightly greater proportion of pre-200 Ma samples in the plutonic distribution.

Extended Data Figure 4 Schematic illustration of instantaneous cumulate compositions required by fractionation trends.

a–c, The figure illustrates the compositional offset between melt and cumulate trajectories for the curved trends produced for compatible (a) and incompatible elements (c) during fractional crystallization as well as for a hypothetical linear fractionation trend (b). Instantaneous cumulate compositions will fall along blue lines tangent to the melt curve (red), with the extent of offset along this line correlating with the efficiency of cumulate extraction. Relatively efficient fractionation, corresponding to a large compositional offset and a high-curvature differentiation trend, is suggested by the observed curvature of major- and trace-element differentiation trends (Figs 1, 2); efficient differentiation is additionally consistent with thermal constraints (for example, ref. 12) which indicate limited heat budgets for crustal differentiation.

Extended Data Figure 5 Mean major- and trace-element differentiation trends as a function of SiO2.

a–g, Major elements; h–n, trace elements. Data are binned in 2 wt% intervals; mean values are shown with 2 s.e. uncertainties (error bars) for volcanic (red) and plutonic (blue) samples with known ages less than 100 Ma, along with tholeiitic mid ocean ridge differentiates (grey outline).

Extended Data Figure 6 Median major- and trace-element differentiation trends as a function of SiO2.

a–g, Major elements; h–n, trace elements. Data (with error bars) are shown with elemental distributions (lines) for volcanic (red) and plutonic (blue) samples, along with tholeiitic mid ocean ridge differentiates (grey outline). Error bars and MOR outline show median and 2 s.e. uncertainties in 2 wt% SiO2 bins, while distributions are shown for 5 wt% SiO2 bins.

Extended Data Figure 7 Mean major- and trace-element differentiation as a function of MgO.

a–g, Major elements; h–n, trace elements. Data (with error bars) are shown with elemental distributions (lines) for volcanic (red) and plutonic (blue) samples, along with tholeiitic mid ocean ridge differentiates (grey outline). Error bars and MOR outline show mean and 2 s.e. uncertainties in 1 wt% MgO bins, while distributions are shown for 1.25 wt% MgO bins.

Extended Data Figure 8 MELTS simulations of magma differentiation by fractional crystallization.

The figure shows a comparison of inverted MELTS parameters and compositional paths from ∼1.3 × 106 MELTS simulations with initial oxygen fugacity set by the FMQ buffer (left) versus another ∼1.3 × 106 simulations with initial oxygen fugacity one log unit above the FMQ buffer (FMQ + 1, right). a–f, Initial and average volatile distributions of 350 best-fitting simulations (a, b); P–T and P–SiO2 paths of 350 best-fitting MELTS simulations (c, d); and major-element melt and cumulate compositions for 200 best-fitting MELTS simulations (e, f).

Extended Data Figure 9 Trace-element modelling based on MELTS simulation results.

The figure shows simulated Sr (a), Ba (b) and Eu (c) differentiation paths (blue lines) and corresponding cumulate compositions (green points) calculated by applying GERM partition coefficients26 to the mineral percentages derived from 200 MELTS simulations with best-fitting major-element trends. As in Extended Data Fig. 8, best-fitting MELTS simulations were selected out of a total of 1.36 × 106 MELTS simulations with varying initial H2O, CO2, and P–T paths (Methods) and initial oxygen fugacity set by the FMQ buffer. The diameter of each cumulate point is proportional to the volume of cumulates produced at each simulation step.

Extended Data Table 1 Partition coefficients
Full size table

Supplementary information

Supplementary Data 1

This file contains the main dataset of 171,690 continental volcanic, 122,751 continental plutonic, and 44,000 oceanic crustal whole-rock analyses, and a key to reference abbreviations. (XLSX 121612 kb)

Supplementary Data 2

This file contains the best-fitting results of alphaMELTS primitive basalt differentiation simulations at oxygen fugacities of FMQ and FMQ+1. Provided as a compressed directory of ASCII text files. (ZIP 174614 kb)

Supplementary Data 3

The partition coefficient dataset used for all trace element calculations. (XLSX 296 kb)

Supplementary Data 4

This file contains the computational source code used to conduct Weighted Bootstrap-Resampling Monte Carlo analysis, run parallel alphaMELTS simulations, and to study trace element behavior during differentiation. Provided as a compressed directory of ASCII text files. All original code is released under a GNU GPL v2.0 open-source license. (ZIP 48246 kb)

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Keller, C., Schoene, B., Barboni, M. et al. Volcanic–plutonic parity and the differentiation of the continental crust. Nature 523, 301–307 (2015). https://doi.org/10.1038/nature14584

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