Subduction processes play a pivotal role in facilitating material exchange between the crust and mantle, contributing to the growth of continents. However, the onset and evolution of subduction remain hotly debated. Here, we developed a high-dimensional machine learning (ML) model to use multiple compositional data (e.g., Nb/La, Nb, Ti, Nb/U, Pb/Nd, and Nb/Th) to distinguish arc-type from non-arc basalts worldwide, then applied this well-trained ML model to identify and delineate the secular occurrence of Archean arc-type basalts. Our findings indicate that basaltic arc magmatism can be traced back to only a few early Archean cratons. A noticeable increase in the prevalence of arc-type basalts during the Mesoarchean and Neoarchean suggests the synchronous formation of Archean subduction across different cratons. This observation hints at transitioning from the predominantly stagnant geodynamic regime to the early, more mobile tectonic regime.

中文翻译：

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太古代俯冲发生的长期变化

俯冲过程在促进地壳和地幔之间的物质交换方面发挥着关键作用，有助于大陆的生长。然而，俯冲的开始和演化仍然存在激烈争论。在这里，我们开发了一个高维机器学习（ML）模型，使用多种成分数据（例如，Nb/La、Nb、Ti、Nb/U、Pb/Nd 和 Nb/Th）来区分弧型和非弧型。世界各地的弧型玄武岩，然后应用这个训练有素的机器学习模型来识别和描述太古代弧型玄武岩的长期产状。我们的研究结果表明，玄武岩弧岩浆作用只能追溯到少数早期太古代克拉通。中太古代和新太古代期间弧型玄武岩的显着增加表明太古代俯冲跨越不同克拉通的同步形成。这一观察暗示着从主要停滞的地球动力学状态向早期、更移动的构造状态的转变。