中国地质大学（武汉）陈国雄课题组在利用机器学习探究冥古代构造地质学方面取得新进展。相关论文发表在2023年5月30日出版的《地质学》杂志上。

课题组人员使用锆石化学数据 (包括超过 4.0 b.y. 的19种元素)，开发了高维机器学习(ML)的方法，以表征在一些典型的构造环境 (例如，弧、与地幔柱相关的热点和裂缝) 结晶的锆石，以及来自火成岩 (I-型) 或沉积岩 (S-型) 岩浆的锆石。研究提出的高维机器学习方法，从非统一性的角度，与传统的判别图(例如，U/Pb vs. Y 和稀土元素(REE)+Y vs.P)相比，该方法可以在大于 89% 的较高预测精度下，识别给定锆石的构造背景和花岗岩类岩石类型  (从太古代到显生宙)。

基于高维机器学习的方法，其识别标志取决于锆石化学的系统差异，特别是构造环境的U，Th和重稀土元素的显著差异，以及I-型和S-型岩浆的 P 和 Hf 的显著差异。将调试好的高维机器学习模型应用于澳大利亚杰克山的冥古代锆石表明，这些锆石主要结晶于大陆弧形成的岩浆中(90%)，其中45%属于S-型熔化物。研究结果为冥古代俯冲活动相关的沉积物再循环提供了明确的证据。

据介绍，形成地球最早地壳的构造联系和岩浆成分仍备受争议。以前研究人员为实现这一目标所做的努力，在很大程度上依赖于使用显生宙样品开发的低维判别图来确定冥古代锆石的来源，如果不考虑锆石成分的长期变化，这些判别图则不足以捕捉系统的差异。

附：英文原文

Title: Hadean tectonics: Insights from machine learning

Author: Guoxiong Chen, Timothy Kusky, Lei Luo, Quanke Li, Qiuming Cheng

Issue&Volume: 2023-05-30

Abstract: 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.

DOI: 10.1130/G51095.1

Source: https://pubs.geoscienceworld.org/gsa/geology/article/doi/10.1130/G51095.1/623830/Hadean-tectonics-Insights-from-machine-learning