Decoding earth’s plate tectonic history using sparse geochemical data
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Abstract
Accurately mapping plate boundary types and locations through time is essential for understanding the
evolution of the plate-mantle system and the exchange of material between the solid Earth and surface
environments. However, the complexity of the Earth system and the cryptic nature of the geological
record make it difficult to discriminate tectonic environments through deep time. Here we present a new
method for identifying tectonic paleo-environments on Earth through a data mining approach using
global geochemical data. We first fingerprint a variety of present-day tectonic environments utilising up
to 136 geochemical data attributes in any available combination. A total of 38301 geochemical analyses
from basalts aged from 5e0 Ma together with a well-established plate reconstruction model are used to
construct a suite of discriminatory models for the first order tectonic environments of subduction and
mid-ocean ridge as distinct from intraplate hotspot oceanic environments, identifying 41, 35, and 39 key
discriminatory geochemical attributes, respectively. After training and validation, our model is applied to
a global geochemical database of 1547 basalt samples of unknown tectonic origin aged between 1000
e410 Ma, a relatively ill-constrained period of Earth’s evolution following the breakup of the Rodinia
supercontinent, producing 56 unique global tectonic environment predictions throughout the Neoproterozoic
and Early Paleozoic. Predictions are used to discriminate between three alternative published
Rodinia configuration models, identifying the model demonstrating the closest spatio-temporal consistency
with the basalt record, and emphasizing the importance of integrating geochemical data into
plate reconstructions. Our approach offers an extensible framework for constructing full-plate, deeptime
reconstructions capable of assimilating a broad range of geochemical and geological observations,
enabling next generation Earth system models.
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