CUG’s research team of big data in earth science and early earth evolution led by CHENG Qiuming, Director of State Key Laboratory of Geological Processes and Mineral Resources, and Prof. Timothy Kusky, Director of Global Geotectonics Center, has published their latest results in Geology with the title “Hadean Tectonics: Insights from Machine Learning. The first author is CHEN Guoxiong, a research associate of CUG, and the corresponding author is CHENG.
The tectonic affiliations and magma compositions that formed Earth’s earliest crusts remain hotly debated. Previous efforts toward this goal have relied heavily on determining the provenance of Hadean zircons using low-dimensional discriminant diagrams developed from Phanerozoic samples, which are inadequate for capturing systematic differences without considering secular changes in zircon composition. Here, we developed high-dimensional machine learning (ML) approaches using zircon chemistry data (spanning 19 elements over 4.0 b.y.) to characterize zircons that crystallized in some typical tectonic settings (e.g., arcs, plume-related hotspots, and rifts) and from either igneous (I-type) or sedimentary (S-type) magmas. The proposed ML method, from a nonuniformitarian perspective, identifies the tectonic settings and granitoid types of given zircons (from Archean to Phanerozoic) at a higher prediction accuracy of >89% compared to ~66%–82% for traditional discriminant diagrams (e.g., U/Yb vs. Y and rare earth elements (REE) + Y vs. P). The ML-based discriminators depend on the systematic differences in zircon chemistry, notably, significant differences in U, Th, and heavy REE for tectonic settings, and P and Hf for I- and S-type magmas. Application of the trained ML models to Hadean zircons from Jack Hills, Australia, suggests that these zircons were mainly crystallized in continental arc–forming magmas (90%) with 45% belonging to S-type melts. This result provides clear evidence of sediment recycling associated with subduction activity in the Hadean.
Figure 1.Controversy over theHadean tectonicpatternand compositions.
Figure2. Machine learning–based discrimination of tonalite-trondhjemite-granodiorite (TTG) and Jack Hills zircon (JHZ) provenances. (A, B) Tectonic setting and granite type in which TTG zircons formed, respectively. (C, D) Tectonic setting and granite type in which JHZ formed, respectively. MOR—mid-ocean ridge.
Link: https://doi.org/10.1130/G51095.1
