Decoding earth's plate tectonic history using sparse geochemical data

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 5-0 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 -410 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, deep-time reconstructions capable of assimilating a broad range of geochemical and geological observations, enabling next generation Earth system models. (C) 2019, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V.