The Probability of Mantle Plumes in Global Tomographic Models

Abstract

While the downward mass flux in the Earth's deep interior is well constrained by seismic tomography, the upward flux is still poorly understood and debated. Recent tomography studies suggest that we are now starting to resolve deep mantle plume structures. However, a lack of uncertainty quantification impedes a full assessment of their significance and whether they are statistically distinct from noise. This work uses a spherical wavelet transform and random noise realizations to quantify the probability of deep plume-like features in six recent global tomographic models. We find that out of 50 possible mantle deep plumes, 12 are highly likely, with probabilities larger than 80%, and 12 are likely, with probability between 70% and 80%. Objective, quantitative approaches as proposed in this study should be used for model interpretation. The five most likely deep mantle plumes are Tahiti, Macdonald, East Africa, Pitcairn, and Marquesas, which have some of the largest buoyancy fluxes estimated in a previous study that used hotspot swell volumes. This could resolve past discrepancies between deep mantle plumes inferred by visual analysis of tomography models and flux estimations from hotspot swell data. In addition, a notable unlikely deep mantle plume is Yellowstone, with probability lower than 50%. We also identify a likely deep mantle plume associated with the Amsterdam-St Paul hotspot, a region scarcely discussed in previous studies and that deserves future investigation. Hence, our automated, objective approach is a valuable alternative approach for the quantitative interpretation of tomographic models.