A machine learning geobarometer for diamond-hosted majoritic garnet inclusions

Abstract:

Diamond-hosted majoritic garnet inclusions provide unique insights into the Earth’s deep, and otherwise inaccessible, mantle. Compared with other types of mineral inclusions found in sub-lithospheric diamonds, majoritic garnets can provide the most accurate estimates of diamond formation pressures because garnet chemistry is known to be a function of pressure from laboratory experiments [e.g. 1,2]. However, our evaluation of the available empirical barometers using a large compilation of experimental data demonstrates that none can be applied with confidence to diamond-hosted garnet inclusions. To address this, we have developed a novel type of majorite barometer using machine learning algorithms. Application of multiple cross validation strategies demonstrates that machine learning outperforms all available barometers, and allows accurate prediction of the formation pressure across the full range of experimental majoritic garnet compositions found in the literature.

Application this barometer to the global database of diamond-hosted majoritic garnet inclusions reveals that their formation occurs in specific pressure modes throughout the upper mantle. However, exsolved clinopyroxene that are often observed within garnet inclusions are not reported for the majority of these analyses. Reconstruction of inclusions, in the cases where this is currently possible, reveals that ignoring small exsolved components can lead to underestimating inclusion pressures by up to 8 GPa (~ 240 km). The predicted formation pressures of majoritic garnet inclusions from South America are consistent with crystallisation of carbon-rich slab-derived melts in Earth’s deep upper mantle and transition zone.

[1] Akaogi, M., and S. Akimoto (1977), PEPI, 15, 90–106. [2] Beyer, C., and D. J. Frost (2017), EPSL, 461, 30–39.