PALAEO-RECONSTRUCTION (OR PLATE RECONSTRUCTION) MODELLING OF SUPERCONTINENT CYCLICITY, WITH APPLICATIONS TO UNDERSTANDING AND VISUALISING DEEP TIME EARTH HISTORY
Many global palaeo-reconstruction models are now available ranging from Phanerozoic into the Proterozoic with one (in development) extending back to the Archaean. These models provide an opportunity to develop methodology to quantify the proportion of continent crust present within each supercontinent while also allowing one to quantitatively compare the predictions of different models. To avoid confusion between geological interpretations and model-dependent associations, we term the model ‘building blocks’ geodynamic units (GDUs), and we consider only GDUs representing continental crust. Amalgamations of GDUs are named SuperGDUs - digital twins for geological supercontinents and smaller crustal amalgamations (proximal accumulations with no internal relative motion).
Using our methodology, we compare four reconstruction models, ranging in maximum age from ~1.8 to ~3.0Ga and containing from ~150 to ~2700 GDUs. Parallel processing is required to achieve variations at 5 Ma age increments. Automated delineation of concave hull shapes is so inconsistent that the only feasible approach to quantify amalgamation is based on area.
All models exhibit similar amalgamation percentages, relative to total modelled continental crust at the time. Pangaea is clearly identified and more nuanced models distinguish amalgamation of Gondwana prior to accretion of Laurussia. Similar relative proportions of continental crust are evident for Rodinia, Nuna and a Kenorland-like situation. Older reconstructed amalgamations are variable due to poor constraints on possible configurations and the limited preservation of Archaean crust. A cutoff of ~55% seems appropriate for definition of a primary (single largest) SuperGDU and hence a supercontinent.
Derivation of amalgamations of multiple GDUs into subordinate SuperGDUs further provides benefits for the computation and visualisation of associated data, the automatic process of identifying plate boundaries and other geodynamic features controlling ore deposits through deep time and the future application of machine learning.